Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks. - Masashi Sugiyama; Hidemitsu Ogawa (NIPS, 1999)

A new information criterion for the selection of subspace models. - Masashi Sugiyama; Hidemitsu Ogawa (ESANN, 2000)

Incremental Active Learning with Bias Reduction. - Masashi Sugiyama; Hidemitsu Ogawa (IJCNN (1), 2000)

Subspace Information Criterion for Model Selection. - Masashi Sugiyama; Hidemitsu Ogawa (Neural Comput., 2001)

Incremental Active Learning for Optimal Generalization. - Masashi Sugiyama; Hidemitsu Ogawa (Neural Comput., 2001)

Incremental projection learning for optimal generalization. - Masashi Sugiyama; Hidemitsu Ogawa (Neural Networks, 2001)

Properties of incremental projection learning. - Masashi Sugiyama; Hidemitsu Ogawa (Neural Networks, 2001)

Selecting Ridge Parameters in Infinite Dimensional Hypothesis Spaces. - Masashi Sugiyama; Klaus-Robert Müller (ICANN, 2002)

The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces. - Masashi Sugiyama; Klaus-Robert Müller (J. Mach. Learn. Res., 2002)

Theoretical and Experimental Evaluation of the Subspace Information Criterion. - Masashi Sugiyama; Hidemitsu Ogawa (Mach. Learn., 2002)

Optimal design of regularization term and regularization parameter by subspace information criterion. - Masashi Sugiyama; Hidemitsu Ogawa (Neural Networks, 2002)

A unified method for optimizing linear image restoration filters. - Masashi Sugiyama; Hidemitsu Ogawa (Signal Process., 2002)

Subspace information criterion for nonquadratic regularizers-Model selection for sparse regressors. - Koji Tsuda; Masashi Sugiyama; Klaus-Robert Müller (IEEE Trans. Neural Networks, 2002)

Improving Precision of the Subspace Information Criterion. - Masashi Sugiyama (IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2003)

Regularizing generalization error estimators: a novel approach to robust model selection. - Masashi Sugiyama; Motoaki Kawanabe; Klaus-Robert Müller (ESANN, 2004)

Estimating the error at given test input points for linear regression. - Masashi Sugiyama (Neural Networks and Computational Intelligence, 2004)

Trading Variance Reduction with Unbiasedness: The Regularized Subspace Information Criterion for Robust Model Selection in Kernel Regression. - Masashi Sugiyama; Motoaki Kawanabe; Klaus-Robert Müller (Neural Comput., 2004)

Model Selection Under Covariate Shift. - Masashi Sugiyama; Klaus-Robert Müller (ICANN (2), 2005)

Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension Reduction. - Gilles Blanchard; Masashi Sugiyama; Motoaki Kawanabe; Vladimir G. Spokoiny; Klaus-Robert Müller (NIPS, 2005)

Active Learning for Misspecified Models. - Masashi Sugiyama (NIPS, 2005)

Importance-Weighted Cross-Validation for Covariate Shift. - Masashi Sugiyama; Benjamin Blankertz; Matthias Krauledat; Guido Dornhege; Klaus-Robert Müller (DAGM-Symposium, 2006)

A Novel Dimension Reduction Procedure for Searching Non-Gaussian Subspaces. - Motoaki Kawanabe; Gilles Blanchard; Masashi Sugiyama; Vladimir G. Spokoiny; Klaus-Robert Müller (ICA, 2006)

Obtaining the Best Linear Unbiased Estimator of Noisy Signals by Non-Gaussian Component Analysis. - Masashi Sugiyama; Motoaki Kawanabe; Gilles Blanchard; Vladimir G. Spokoiny; Klaus-Robert Müller (ICASSP (3), 2006)

Local Fisher discriminant analysis for supervised dimensionality reduction. - Masashi Sugiyama (ICML, 2006)

Mixture Regression for Covariate Shift. - Amos J. Storkey; Masashi Sugiyama (NIPS, 2006)

Model Selection Using a Class of Kernels with an Invariant Metric. - Akira Tanaka; Masashi Sugiyama; Hideyuki Imai; Mineichi Kudo; Masaaki Miyakoshi (SSPR/SPR, 2006)

Constructing Kernel Functions for Binary Regression. - Masashi Sugiyama; Hidemitsu Ogawa (IEICE Trans. Inf. Syst., 2006)

Analytic Optimization of Shrinkage Parameters Based on Regularized Subspace Information Criterion. - Masashi Sugiyama; Keisuke Sakurai (IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2006)

In Search of Non-Gaussian Components of a High-Dimensional Distribution. - Gilles Blanchard; Motoaki Kawanabe; Masashi Sugiyama; Vladimir G. Spokoiny; Klaus-Robert Müller (J. Mach. Learn. Res., 2006)

Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error. - Masashi Sugiyama (J. Mach. Learn. Res., 2006)

Asymptotic Bayesian generalization error when training and test distributions are different. - Keisuke Yamazaki; Motoaki Kawanabe; Sumio Watanabe; Masashi Sugiyama; Klaus-Robert Müller (ICML, 2007)

Value Function Approximation on Non-Linear Manifolds for Robot Motor Control. - Masashi Sugiyama; Hirotaka Hachiya; Christopher Towell; Sethu Vijayakumar (ICRA, 2007)

Multi-Task Learning via Conic Programming. - Tsuyoshi Kato; Hisashi Kashima; Masashi Sugiyama; Kiyoshi Asai (NIPS, 2007)

Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation. - Masashi Sugiyama; Shinichi Nakajima; Hisashi Kashima; Paul von Bünau; Motoaki Kawanabe (NIPS, 2007)

Influence-based collaborative active learning. - Neil Rubens; Masashi Sugiyama (RecSys, 2007)

Analytic Optimization of Adaptive Ridge Parameters Based on Regularized Subspace Information Criterion. - Shun Gokita; Masashi Sugiyama; Keisuke Sakurai (IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2007)

A New Meta-Criterion for Regularized Subspace Information Criterion. - Yasushi Hidaka; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2007)

Generalization Error Estimation for Non-linear Learning Methods. - Masashi Sugiyama (IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2007)

Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis. - Masashi Sugiyama (J. Mach. Learn. Res., 2007)

Covariate Shift Adaptation by Importance Weighted Cross Validation. - Masashi Sugiyama; Matthias Krauledat; Klaus-Robert Müller (J. Mach. Learn. Res., 2007)

Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation. - Hirotaka Hachiya; Takayuki Akiyama; Masashi Sugiyama; Jan Peters 001 (AAAI, 2008)

On the Margin Explanation of Boosting Algorithms. - Liwei Wang 001; Masashi Sugiyama; Cheng Yang; Zhi-Hua Zhou; Jufu Feng (COLT, 2008)

Inlier-Based Outlier Detection via Direct Density Ratio Estimation. - Shohei Hido; Yuta Tsuboi; Hisashi Kashima; Masashi Sugiyama; Takafumi Kanamori (ICDM, 2008)

nu-support vector machine as conditional value-at-risk minimization. - Akiko Takeda; Masashi Sugiyama (ICML, 2008)

Order Retrieval. - Neil Rubens; Vera Sheinman; Takenobu Tokunaga; Masashi Sugiyama (LKR, 2008)

Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection. - Takafumi Kanamori; Shohei Hido; Masashi Sugiyama (NIPS, 2008)

Pool-Based Agnostic Experiment Design in Linear Regression. - Masashi Sugiyama; Shinichi Nakajima (ECML/PKDD (2), 2008)

Integration of Multiple Networks for Robust Label Propagation. - Tsuyoshi Kato; Hisashi Kashima; Masashi Sugiyama (SDM, 2008)

Active Learning with Model Selection in Linear Regression. - Masashi Sugiyama; Neil Rubens (SDM, 2008)

Geodesic Gaussian kernels for value function approximation. - Masashi Sugiyama; Hirotaka Hachiya; Christopher Towell; Sethu Vijayakumar (Auton. Robots, 2008)

Approximating the Best Linear Unbiased Estimator of Non-Gaussian Signals with Gaussian Noise. - Masashi Sugiyama; Motoaki Kawanabe; Gilles Blanchard; Klaus-Robert Müller (IEICE Trans. Inf. Syst., 2008)

Approximating Mutual Information by Maximum Likelihood Density Ratio Estimation. - Taiji Suzuki; Masashi Sugiyama; Jun Sese; Takafumi Kanamori (FSDM, 2008)

A batch ensemble approach to active learning with model selection. - Masashi Sugiyama; Neil Rubens (Neural Networks, 2008)

A Multipurpose Linear Component Analysis Method Based on Modulated Hebb-Oja Learning Rule. - Marko V. Jankovic; Masashi Sugiyama (IEEE Signal Process. Lett., 2008)

Density Ratio Estimation: A New Versatile Tool for Machine Learning. - Masashi Sugiyama (ACML, 2009)

Efficient data reuse in value function approximation. - Hirotaka Hachiya; Takayuki Akiyama; Masashi Sugiyama; Jan Peters 001 (ADPRL, 2009)

A Framework of Adaptive Brain Computer Interfaces. - Yan Li; Yasuharu Koike; Masashi Sugiyama (BMEI, 2009)

Covariate shift adaptation for semi-supervised speaker identification. - Makoto Yamada; Masashi Sugiyama; Tomoko Matsui (ICASSP, 2009)

Least absolute policy iteration for robust value function approximation. - Masashi Sugiyama; Hirotaka Hachiya; Hisashi Kashima; Tetsuro Morimura (ICRA, 2009)

Estimating Squared-Loss Mutual Information for Independent Component Analysis. - Taiji Suzuki; Masashi Sugiyama (ICA, 2009)

Active Policy Iteration: Efficient Exploration through Active Learning for Value Function Approximation in Reinforcement Learning. - Takayuki Akiyama; Hirotaka Hachiya; Masashi Sugiyama (IJCAI, 2009)

Probabilistic principal component analysis based on JoyStick Probability Selector. - Marko V. Jankovic; Masashi Sugiyama (IJCNN, 2009)

Mutual information approximation via maximum likelihood estimation of density ratio. - Taiji Suzuki; Masashi Sugiyama; Toshiyuki Tanaka (ISIT, 2009)

Analysis of Variational Bayesian Matrix Factorization. - Shinichi Nakajima; Masashi Sugiyama (PAKDD, 2009)

Efficient Sample Reuse in EM-Based Policy Search. - Hirotaka Hachiya; Jan Peters 001; Masashi Sugiyama (ECML/PKDD (1), 2009)

Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction. - Hisashi Kashima; Tsuyoshi Kato; Yoshihiro Yamanishi; Masashi Sugiyama; Koji Tsuda (SDM, 2009)

Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation. - Yoshinobu Kawahara; Masashi Sugiyama (SDM, 2009)

Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information: a semi-supervised approach. - Hisashi Kashima; Yoshihiro Yamanishi; Tsuyoshi Kato; Masashi Sugiyama; Koji Tsuda (Bioinform., 2009)

Mutual information estimation reveals global associations between stimuli and biological processes. - Taiji Suzuki; Masashi Sugiyama; Takafumi Kanamori; Jun Sese (BMC Bioinform., 2009)

Efficient Construction of Neighborhood Graphs by the Multiple Sorting Method - Takeaki Uno; Masashi Sugiyama; Koji Tsuda (CoRR, 2009)

Recent Advances and Trends in Large-Scale Kernel Methods. - Hisashi Kashima; Tsuyoshi Idé; Tsuyoshi Kato; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2009)

On Computational Issues of Semi-Supervised Local Fisher Discriminant Analysis. - Masashi Sugiyama (IEICE Trans. Inf. Syst., 2009)

Direct Importance Estimation with Gaussian Mixture Models. - Makoto Yamada; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2009)

A Density-ratio Framework for Statistical Data Processing. - Masashi Sugiyama; Takafumi Kanamori; Taiji Suzuki; Shohei Hido; Jun Sese; Ichiro Takeuchi; Liwei Wang 001 (IPSJ Trans. Comput. Vis. Appl., 2009)

Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation. - Yuta Tsuboi; Hisashi Kashima; Shohei Hido; Steffen Bickel; Masashi Sugiyama (J. Inf. Process., 2009)

A Least-squares Approach to Direct Importance Estimation. - Takafumi Kanamori; Shohei Hido; Masashi Sugiyama (J. Mach. Learn. Res., 2009)

Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression. - Nicole Krämer 002; Masashi Sugiyama; Mikio L. Braun (AISTATS, 2009)

Pool-based active learning in approximate linear regression. - Masashi Sugiyama; Shinichi Nakajima (Mach. Learn., 2009)

Theory and Algorithm for Learning with Dissimilarity Functions. - Liwei Wang 001; Masashi Sugiyama; Cheng Yang; Kohei Hatano; Jufu Feng (Neural Comput., 2009)

On Generalization Performance and Non-Convex Optimization of Extended nu-Support Vector Machine. - Akiko Takeda; Masashi Sugiyama (New Gener. Comput., 2009)

Adaptive importance sampling for value function approximation in off-policy reinforcement learning. - Hirotaka Hachiya; Takayuki Akiyama; Masashi Sugiyama; Jan Peters 001 (Neural Networks, 2009)

Dual-Augmented Lagrangian Method for Efficient Sparse Reconstruction. - Ryota Tomioka; Masashi Sugiyama (IEEE Signal Process. Lett., 2009)

Robust Label Propagation on Multiple Networks. - Tsuyoshi Kato; Hisashi Kashima; Masashi Sugiyama (IEEE Trans. Neural Networks, 2009)

Dependence Minimizing Regression with Model Selection for Non-Linear Causal Inference under Non-Gaussian Noise. - Makoto Yamada; Masashi Sugiyama (AAAI, 2010)

Automatic audio tagging using covariate shift adaptation. - Gordon Wichern; Makoto Yamada; Harvey D. Thornburg; Masashi Sugiyama; Andreas Spanias (ICASSP, 2010)

Direct importance estimation with probabilistic principal component analyzers. - Makoto Yamada; Masashi Sugiyama; Gordon Wichern (ICASSP, 2010)

Acceleration of sequence kernel computation for real-time speaker identification. - Makoto Yamada; Masashi Sugiyama; Gordon Wichern; Tomoko Matsui (ICASSP, 2010)

Nonparametric Return Distribution Approximation for Reinforcement Learning. - Tetsuro Morimura; Masashi Sugiyama; Hisashi Kashima; Hirotaka Hachiya; Toshiyuki Tanaka (ICML, 2010)

Implicit Regularization in Variational Bayesian Matrix Factorization. - Shinichi Nakajima; Masashi Sugiyama (ICML, 2010)

A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices. - Ryota Tomioka; Taiji Suzuki; Masashi Sugiyama; Hisashi Kashima (ICML, 2010)

Perceived Age Estimation under Lighting Condition Change by Covariate Shift Adaptation. - Kazuya Ueki; Masashi Sugiyama; Yasuyuki Ihara (ICPR, 2010)

Global Analytic Solution for Variational Bayesian Matrix Factorization. - Shinichi Nakajima; Masashi Sugiyama; Ryota Tomioka (NIPS, 2010)

Feature Selection for Reinforcement Learning: Evaluating Implicit State-Reward Dependency via Conditional Mutual Information. - Hirotaka Hachiya; Masashi Sugiyama (ECML/PKDD (1), 2010)

Direct Density Ratio Estimation with Dimensionality Reduction. - Masashi Sugiyama; Satoshi Hara 001; Paul von Bünau; Taiji Suzuki; Takafumi Kanamori; Motoaki Kawanabe (SDM, 2010)

Parametric Return Density Estimation for Reinforcement Learning. - Tetsuro Morimura; Masashi Sugiyama; Hisashi Kashima; Hirotaka Hachiya; Toshiyuki Tanaka (UAI, 2010)

Semi-supervised Estimation of Perceived Age from Face Images. - Kazuya Ueki; Masashi Sugiyama; Yasuyuki Ihara (VISAPP (2), 2010)

Theoretical Analysis of Density Ratio Estimation. - Takafumi Kanamori; Taiji Suzuki; Masashi Sugiyama (IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2010)

Spectral Methods for Thesaurus Construction. - Nobuyuki Shimizu; Masashi Sugiyama; Hiroshi Nakagawa (IEICE Trans. Inf. Syst., 2010)

Superfast-Trainable Multi-Class Probabilistic Classifier by Least-Squares Posterior Fitting. - Masashi Sugiyama (IEICE Trans. Inf. Syst., 2010)

Least Absolute Policy Iteration-A Robust Approach to Value Function Approximation. - Masashi Sugiyama; Hirotaka Hachiya; Hisashi Kashima; Tetsuro Morimura (IEICE Trans. Inf. Syst., 2010)

Least-Squares Conditional Density Estimation. - Masashi Sugiyama; Ichiro Takeuchi; Taiji Suzuki; Takafumi Kanamori; Hirotaka Hachiya; Daisuke Okanohara (IEICE Trans. Inf. Syst., 2010)

A Semi-Supervised Approach to Perceived Age Prediction from Face Images. - Kazuya Ueki; Masashi Sugiyama; Yasuyuki Ihara (IEICE Trans. Inf. Syst., 2010)

Direct Importance Estimation with a Mixture of Probabilistic Principal Component Analyzers. - Makoto Yamada; Masashi Sugiyama; Gordon Wichern; Jaak Simm (IEICE Trans. Inf. Syst., 2010)

A Transfer Learning Approach and Selective Integration of Multiple Types of Assays for Biological Network Inference. - Tsuyoshi Kato; Kinya Okada; Hisashi Kashima; Masashi Sugiyama (Int. J. Knowl. Discov. Bioinform., 2010)

Output Divergence Criterion for Active Learning in Collaborative Settings. - Neil Rubens; Ryota Tomioka; Masashi Sugiyama (Inf. Media Technol., 2010)

Conditional Density Estimation via Least-Squares Density Ratio Estimation. - Masashi Sugiyama; Ichiro Takeuchi; Taiji Suzuki; Takafumi Kanamori; Hirotaka Hachiya; Daisuke Okanohara (AISTATS, 2010)

Preface. - Masashi Sugiyama; Qiang Yang 001 (ACML, 2010)

Single versus Multiple Sorting in All Pairs Similarity Search. - Yasuo Tabei; Takeaki Uno; Masashi Sugiyama; Koji Tsuda (ACML, 2010)

Semi-supervised local Fisher discriminant analysis for dimensionality reduction. - Masashi Sugiyama; Tsuyoshi Idé; Shinichi Nakajima; Jun Sese (Mach. Learn., 2010)

Efficient exploration through active learning for value function approximation in reinforcement learning. - Takayuki Akiyama; Hirotaka Hachiya; Masashi Sugiyama (Neural Networks, 2010)

Dimensionality reduction for density ratio estimation in high-dimensional spaces. - Masashi Sugiyama; Motoaki Kawanabe; Pui Ling Chui (Neural Networks, 2010)

Semi-supervised speaker identification under covariate shift. - Makoto Yamada; Masashi Sugiyama; Tomoko Matsui (Signal Process., 2010)

Application of Covariate Shift Adaptation Techniques in Brain-Computer Interfaces. - Yan Li; Hiroyuki Kambara; Yasuharu Koike; Masashi Sugiyama (IEEE Trans. Biomed. Eng., 2010)

Conic Programming for Multitask Learning. - Tsuyoshi Kato; Hisashi Kashima; Masashi Sugiyama; Kiyoshi Asai (IEEE Trans. Knowl. Data Eng., 2010)

Trajectory Regression on Road Networks. - Tsuyoshi Idé; Masashi Sugiyama (AAAI, 2011)

Direct Density-Ratio Estimation with Dimensionality Reduction via Hetero-Distributional Subspace Analysis. - Makoto Yamada; Masashi Sugiyama (AAAI, 2011)

Multi-race age estimation based on the combination of multiple classifiers. - Kazuya Ueki; Masashi Sugiyama; Yasuyuki Ihara; Mitsuhiro Fujita (ACPR, 2011)

Automatic audio tag classification via semi-supervised canonical density estimation. - Jun Takagi; Yasunori Ohishi; Akisato Kimura; Masashi Sugiyama; Makoto Yamada; Hirokazu Kameoka (ICASSP, 2011)

On Bayesian PCA: Automatic Dimensionality Selection and Analytic Solution. - Shinichi Nakajima; Masashi Sugiyama; S. Derin Babacan (ICML, 2011)

On Information-Maximization Clustering: Tuning Parameter Selection and Analytic Solution. - Masashi Sugiyama; Makoto Yamada; Manabu Kimura; Hirotaka Hachiya (ICML, 2011)

Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent. - Shinichi Nakajima; Masashi Sugiyama; S. Derin Babacan (NIPS, 2011)

Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification. - Ichiro Takeuchi; Masashi Sugiyama (NIPS, 2011)

Foreword. - Masashi Sugiyama (IEICE Trans. Inf. Syst., 2011)

Least-Squares Independence Test. - Masashi Sugiyama; Taiji Suzuki (IEICE Trans. Inf. Syst., 2011)

Lighting Condition Adaptation for Perceived Age Estimation. - Kazuya Ueki; Masashi Sugiyama; Yasuyuki Ihara (IEICE Trans. Inf. Syst., 2011)

Improving the Accuracy of Least-Squares Probabilistic Classifiers. - Makoto Yamada; Masashi Sugiyama; Gordon Wichern; Jaak Simm (IEICE Trans. Inf. Syst., 2011)

Computationally Efficient Multi-task Learning with Least-squares Probabilistic Classifiers. - Jaak Simm; Masashi Sugiyama; Tsuyoshi Kato (IPSJ Trans. Comput. Vis. Appl., 2011)

Dependence-Maximization Clustering with Least-Squares Mutual Information. - Manabu Kimura; Masashi Sugiyama (J. Adv. Comput. Intell. Intell. Informatics, 2011)

Theoretical Analysis of Bayesian Matrix Factorization. - Shinichi Nakajima; Masashi Sugiyama (J. Mach. Learn. Res., 2011)

Maximum Volume Clustering. - Gang Niu 001; Bo Dai 001; Lin Shang 001; Masashi Sugiyama (AISTATS, 2011)

Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation. - Ryota Tomioka; Taiji Suzuki; Masashi Sugiyama (J. Mach. Learn. Res., 2011)

A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin. - Liwei Wang 001; Masashi Sugiyama; Zhaoxiang Jing; Cheng Yang; Zhi-Hua Zhou; Jufu Feng (J. Mach. Learn. Res., 2011)

Suffcient Component Analysis. - Makoto Yamada; Gang Niu 001; Jun Takagi; Masashi Sugiyama (ACML, 2011)

Cross-Domain Object Matching with Model Selection. - Makoto Yamada; Masashi Sugiyama (AISTATS, 2011)

Statistical outlier detection using direct density ratio estimation. - Shohei Hido; Yuta Tsuboi; Hisashi Kashima; Masashi Sugiyama; Takafumi Kanamori (Knowl. Inf. Syst., 2011)

Reward-Weighted Regression with Sample Reuse for Direct Policy Search in Reinforcement Learning. - Hirotaka Hachiya; Jan Peters 001; Masashi Sugiyama (Neural Comput., 2011)

Least-Squares Independent Component Analysis. - Taiji Suzuki; Masashi Sugiyama (Neural Comput., 2011)

Least-squares two-sample test. - Masashi Sugiyama; Taiji Suzuki; Yuta Itoh 001; Takafumi Kanamori; Manabu Kimura (Neural Networks, 2011)

Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search. - Masashi Sugiyama; Makoto Yamada; Paul von Bünau; Taiji Suzuki; Takafumi Kanamori; Motoaki Kawanabe (Neural Networks, 2011)

Active Learning in Recommender Systems. - Neil Rubens; Dain Kaplan; Masashi Sugiyama (Recommender Systems Handbook, 2011)

Density Ratio Estimation in Machine Learning. - Masashi Sugiyama; Taiji Suzuki; Takafumi Kanamori (Cambridge University Press, 2012)

Machine Learning in Non-Stationary Environments - Introduction to Covariate Shift Adaptation. - Masashi Sugiyama; Motoaki Kawanabe (MIT Press, 2012)

Computationally efficient multi-label classification by least-squares probabilistic classifier. - Hyun Ha Nam; Hirotaka Hachiya; Masashi Sugiyama (ICASSP, 2012)

Designing various component analysis at will. - Akisato Kimura; Hitoshi Sakano; Hirokazu Kameoka; Masashi Sugiyama (ICPR, 2012)

Perfect Dimensionality Recovery by Variational Bayesian PCA. - Shinichi Nakajima; Ryota Tomioka; Masashi Sugiyama; S. Derin Babacan (NIPS, 2012)

High-Dimensional Feature Selection by Feature-Wise Non-Linear Lasso - Makoto Yamada; Wittawat Jitkrittum; Leonid Sigal; Masashi Sugiyama (CoRR, 2012)

Dependence Maximizing Temporal Alignment via Squared-Loss Mutual Information - Makoto Yamada; Leonid Sigal; Michalis Raptis; Masashi Sugiyama (CoRR, 2012)

Feature Selection via L1-Penalized Squared-Loss Mutual Information - Wittawat Jitkrittum; Hirotaka Hachiya; Masashi Sugiyama (CoRR, 2012)

Early Stopping Heuristics in Pool-Based Incremental Active Learning for Least-Squares Probabilistic Classifier. - Tsubasa Kobayashi; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2012)

Multi-Task Approach to Reinforcement Learning for Factored-State Markov Decision Problems. - Jaak Simm; Masashi Sugiyama; Hirotaka Hachiya (IEICE Trans. Inf. Syst., 2012)

On Kernel Parameter Selection in Hilbert-Schmidt Independence Criterion. - Masashi Sugiyama; Makoto Yamada (IEICE Trans. Inf. Syst., 2012)

Importance-weighted least-squares probabilistic classifier for covariate shift adaptation with application to human activity recognition. - Hirotaka Hachiya; Masashi Sugiyama; Naonori Ueda (Neurocomputing, 2012)

Sparse Additive Matrix Factorization for Robust PCA and Its Generalization. - Shinichi Nakajima; Masashi Sugiyama; S. Derin Babacan (ACML, 2012)

Fast Learning Rate of Multiple Kernel Learning: Trade-Off between Sparsity and Smoothness. - Taiji Suzuki; Masashi Sugiyama (AISTATS, 2012)

Statistical analysis of kernel-based least-squares density-ratio estimation. - Takafumi Kanamori; Taiji Suzuki; Masashi Sugiyama (Mach. Learn., 2012)

Multi-parametric solution-path algorithm for instance-weighted support vector machines. - Masayuki Karasuyama; Naoyuki Harada; Masashi Sugiyama; Ichiro Takeuchi (Mach. Learn., 2012)

Canonical dependency analysis based on squared-loss mutual information. - Masayuki Karasuyama; Masashi Sugiyama (Neural Networks, 2012)

Improving importance estimation in pool-based batch active learning for approximate linear regression. - Nozomi Kurihara; Masashi Sugiyama (Neural Networks, 2012)

Analysis and improvement of policy gradient estimation. - Tingting Zhao 001; Hirotaka Hachiya; Gang Niu 001; Masashi Sugiyama (Neural Networks, 2012)

Sequential change-point detection based on direct density-ratio estimation. - Yoshinobu Kawahara; Masashi Sugiyama (Stat. Anal. Data Min., 2012)

Introduction to the Special Section on the 2nd Asia Conference on Machine Learning (ACML 2010). - Masashi Sugiyama; Qiang Yang 001 (ACM Trans. Intell. Syst. Technol., 2012)

f-Divergence Estimation and Two-Sample Homogeneity Test Under Semiparametric Density-Ratio Models. - Takafumi Kanamori; Taiji Suzuki; Masashi Sugiyama (IEEE Trans. Inf. Theory, 2012)

Divergence estimation for machine learning and signal processing. - Masashi Sugiyama (BCI, 2013)

Direct Approximation of Divergences Between Probability Distributions. - Masashi Sugiyama (Empirical Inference, 2013)

Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning. - Gang Niu 001; Wittawat Jitkrittum; Bo Dai 001; Hirotaka Hachiya; Masashi Sugiyama (ICML (3), 2013)

Infinitesimal Annealing for Training Semi-Supervised Support Vector Machines. - Kohei Ogawa; Motoki Imamura; Ichiro Takeuchi; Masashi Sugiyama (ICML (3), 2013)

Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering. - Shinichi Nakajima; Akiko Takeda; S. Derin Babacan; Masashi Sugiyama; Ichiro Takeuchi (NIPS, 2013)

Parametric Task Learning. - Ichiro Takeuchi; Tatsuya Hongo; Masashi Sugiyama; Shinichi Nakajima (NIPS, 2013)

Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. - Song Liu 002; John A. Quinn; Michael U. Gutmann; Masashi Sugiyama (ECML/PKDD (2), 2013)

Clustering Unclustered Data: Unsupervised Binary Labeling of Two Datasets Having Different Class Balances. - Marthinus Christoffel du Plessis; Gang Niu 001; Masashi Sugiyama (TAAI, 2013)

Density Ratio Hidden Markov Models - John A. Quinn; Masashi Sugiyama (CoRR, 2013)

Clustering Unclustered Data: Unsupervised Binary Labeling of Two Datasets Having Different Class Balances - Marthinus Christoffel du Plessis; Masashi Sugiyama (CoRR, 2013)

Noise adaptive optimization of matrix initialization for frequency-domain independent component analysis. - Makoto Yamada; Gordon Wichern; Kazunobu Kondo; Masashi Sugiyama; Hiroshi Sawada (Digit. Signal Process., 2013)

Machine Learning with Squared-Loss Mutual Information. - Masashi Sugiyama (Entropy, 2013)

Winning the Kaggle Algorithmic Trading Challenge with the Composition of Many Models and Feature Engineering. - Ildefons Magrans de Abril; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2013)

Feature Selection via l1-Penalized Squared-Loss Mutual Information. - Wittawat Jitkrittum; Hirotaka Hachiya; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2013)

Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifiers. - Hyun Ha Nam; Hirotaka Hachiya; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2013)

Direct Approximation of Quadratic Mutual Information and Its Application to Dependence-Maximization Clustering. - Janya Sainui; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2013)

Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting. - Ning Xie 003; Hirotaka Hachiya; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2013)

SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations. - Akisato Kimura; Masashi Sugiyama; Takuho Nakano; Hirokazu Kameoka; Hitoshi Sakano; Eisaku Maeda; Katsuhiko Ishiguro (Inf. Media Technol., 2013)

Designing Various Multivariate Analysis at Will via Generalized Pairwise Expression. - Akisato Kimura; Masashi Sugiyama; Hitoshi Sakano; Hirokazu Kameoka (Inf. Media Technol., 2013)

Salient Object Detection Based on Direct Density-ratio Estimation. - Masao Yamanaka; Masakazu Matsugu; Masashi Sugiyama (Inf. Media Technol., 2013)

Detection of Activities and Events without Explicit Categorization. - Masao Yamanaka; Masakazu Matsugu; Masashi Sugiyama (Inf. Media Technol., 2013)

Direct Divergence Approximation between Probability Distributions and Its Applications in Machine Learning. - Masashi Sugiyama; Song Liu 002; Marthinus Christoffel du Plessis; Masao Yamanaka; Makoto Yamada; Taiji Suzuki; Takafumi Kanamori (J. Comput. Sci. Eng., 2013)

Global analytic solution of fully-observed variational Bayesian matrix factorization. - Shinichi Nakajima; Masashi Sugiyama; S. Derin Babacan; Ryota Tomioka (J. Mach. Learn. Res., 2013)

Maximum volume clustering: a new discriminative clustering approach. - Gang Niu 001; Bo Dai 001; Lin Shang 001; Masashi Sugiyama (J. Mach. Learn. Res., 2013)

Computational complexity of kernel-based density-ratio estimation: a condition number analysis. - Takafumi Kanamori; Taiji Suzuki; Masashi Sugiyama (Mach. Learn., 2013)

Variational Bayesian sparse additive matrix factorization. - Shinichi Nakajima; Masashi Sugiyama; S. Derin Babacan (Mach. Learn., 2013)

Density-Difference Estimation. - Masashi Sugiyama; Takafumi Kanamori; Taiji Suzuki; Marthinus Christoffel du Plessis; Song Liu 002; Ichiro Takeuchi (Neural Comput., 2013)

Sufficient Dimension Reduction via Squared-Loss Mutual Information Estimation. - Taiji Suzuki; Masashi Sugiyama (Neural Comput., 2013)

Relative Density-Ratio Estimation for Robust Distribution Comparison. - Makoto Yamada; Taiji Suzuki; Takafumi Kanamori; Hirotaka Hachiya; Masashi Sugiyama (Neural Comput., 2013)

Efficient Sample Reuse in Policy Gradients with Parameter-Based Exploration. - Tingting Zhao 001; Hirotaka Hachiya; Voot Tangkaratt; Jun Morimoto; Masashi Sugiyama (Neural Comput., 2013)

Change-point detection in time-series data by relative density-ratio estimation. - Song Liu 002; Makoto Yamada; Nigel Collier; Masashi Sugiyama (Neural Networks, 2013)

Analysis of Empirical MAP and Empirical Partially Bayes: Can They be Alternatives to Variational Bayes? - Shinichi Nakajima; Masashi Sugiyama (AISTATS, 2014)

Efficient reuse of previous experiences in humanoid motor learning. - Norikazu Sugimoto; Voot Tangkaratt; Thijs Wensveen; Tingting Zhao 001; Masashi Sugiyama; Jun Morimoto (Humanoids, 2014)

Transductive Learning with Multi-class Volume Approximation. - Gang Niu 001; Bo Dai 001; Marthinus Christoffel du Plessis; Masashi Sugiyama (ICML, 2014)

Outlier Path: A Homotopy Algorithm for Robust SVM. - Shinya Suzumura; Kohei Ogawa; Masashi Sugiyama; Ichiro Takeuchi (ICML, 2014)

Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP. - Shinichi Nakajima; Issei Sato; Masashi Sugiyama; Kazuho Watanabe; Hiroko Kobayashi (NIPS, 2014)

Analysis of Learning from Positive and Unlabeled Data. - Marthinus Christoffel du Plessis; Gang Niu 001; Masashi Sugiyama (NIPS, 2014)

Multitask learning meets tensor factorization: task imputation via convex optimization. - Kishan Wimalawarne; Masashi Sugiyama; Ryota Tomioka (NIPS, 2014)

Clustering via Mode Seeking by Direct Estimation of the Gradient of a Log-Density. - Hiroaki Sasaki; Aapo Hyvärinen; Masashi Sugiyama (ECML/PKDD (3), 2014)

Support vector comparison machines. - Toby Dylan Hocking; Supaporn Spanurattana; Masashi Sugiyama (CoRR, 2014)

Efficient Reuse of Previous Experiences to Improve Policies in Real Environment. - Norikazu Sugimoto; Voot Tangkaratt; Thijs Wensveen; Tingting Zhao 001; Masashi Sugiyama; Jun Morimoto (CoRR, 2014)

Statistical Analysis of Distance Estimators with Density Differences and Density Ratios. - Takafumi Kanamori; Masashi Sugiyama (Entropy, 2014)

Constrained Least-Squares Density-Difference Estimation. - Tuan Duong Nguyen; Marthinus Christoffel du Plessis; Takafumi Kanamori; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2014)

Class Prior Estimation from Positive and Unlabeled Data. - Marthinus Christoffel du Plessis; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2014)

Unsupervised Dimension Reduction via Least-Squares Quadratic Mutual Information. - Janya Sainui; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2014)

Computationally Efficient Estimation of Squared-Loss Mutual Information with Multiplicative Kernel Models. - Tomoya Sakai 001; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2014)

Tree-Based Ensemble Multi-Task Learning Method for Classification and Regression. - Jaak Simm; Ildefons Magrans de Abril; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2014)

Least-squares independence regression for non-linear causal inference under non-Gaussian noise. - Makoto Yamada; Masashi Sugiyama; Jun Sese (Mach. Learn., 2014)

Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. - Song Liu 002; John A. Quinn; Michael U. Gutmann; Taiji Suzuki; Masashi Sugiyama (Neural Comput., 2014)

Information-Theoretic Semi-Supervised Metric Learning via Entropy Regularization. - Gang Niu 001; Bo Dai 001; Makoto Yamada; Masashi Sugiyama (Neural Comput., 2014)

Information-Maximization Clustering Based on Squared-Loss Mutual Information. - Masashi Sugiyama; Gang Niu 001; Makoto Yamada; Manabu Kimura; Hirotaka Hachiya (Neural Comput., 2014)

High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso. - Makoto Yamada; Wittawat Jitkrittum; Leonid Sigal; Eric P. Xing; Masashi Sugiyama (Neural Comput., 2014)

Semi-supervised information-maximization clustering. - Daniele Calandriello; Gang Niu 001; Masashi Sugiyama (Neural Networks, 2014)

Semi-supervised learning of class balance under class-prior change by distribution matching. - Marthinus Christoffel du Plessis; Masashi Sugiyama (Neural Networks, 2014)

Model-based policy gradients with parameter-based exploration by least-squares conditional density estimation. - Voot Tangkaratt; Syogo Mori; Tingting Zhao 001; Jun Morimoto; Masashi Sugiyama (Neural Networks, 2014)

A least-squares approach to anomaly detection in static and sequential data. - John A. Quinn; Masashi Sugiyama (Pattern Recognit. Lett., 2014)

Statistical Reinforcement Learning - Modern Machine Learning Approaches. - Masashi Sugiyama (CRC Press, 2015)

Support Consistency of Direct Sparse-Change Learning in Markov Networks. - Song Liu 002; Taiji Suzuki; Masashi Sugiyama (AAAI, 2015)

Continuous Target Shift Adaptation in Supervised Learning. - Tuan Duong Nguyen; Marthinus Christoffel du Plessis; Masashi Sugiyama (ACML, 2015)

Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities. - Hiroaki Sasaki; Voot Tangkaratt; Masashi Sugiyama (ACML, 2015)

Regularized Policy Gradients: Direct Variance Reduction in Policy Gradient Estimation. - Tingting Zhao 001; Gang Niu 001; Ning Xie 003; Jucheng Yang 001; Masashi Sugiyama (ACML, 2015)

Direct Density-Derivative Estimation and Its Application in KL-Divergence Approximation. - Hiroaki Sasaki; Yung-Kyun Noh; Masashi Sugiyama (AISTATS, 2015)

Averaging covariance matrices for EEG signal classification based on the CSP: An empirical study. - Florian Yger; Fabien Lotte; Masashi Sugiyama (EUSIPCO, 2015)

Minimum dependency key frames selection via quadratic mutual information. - Janya Sainui; Masashi Sugiyama (ICDIM, 2015)

Convex Formulation for Learning from Positive and Unlabeled Data. - Marthinus Christoffel du Plessis; Gang Niu 001; Masashi Sugiyama (ICML, 2015)

Stroke-Based Stylization Learning and Rendering with Inverse Reinforcement Learning. - Ning Xie 003; Tingting Zhao 001; Feng Tian; Xiaohua Zhang; Masashi Sugiyama (IJCAI, 2015)

A dependence maximization approach towards street map-based localization. - Kiyoshi Irie; Masashi Sugiyama; Masahiro Tomono (IROS, 2015)

Predictive Approaches for Low-Cost Preventive Medicine Program in Developing Countries. - Yukino Baba; Hisashi Kashima; Yasunobu Nohara; Eiko Kai; Partha Pratim Ghosh; Rafiqul Islam Maruf; Ashir Ahmed; Masahiro Kuroda; Sozo Inoue; Tatsuo Hiramatsu; Michio Kimura; Shuji Shimizu; et al. (KDD, 2015)

Task selection for bandit-based task assignment in heterogeneous crowdsourcing. - Hao Zhang; Masashi Sugiyama (TAAI, 2015)

Convergence of Proximal-Gradient Stochastic Variational Inference under Non-Decreasing Step-Size Sequence. - Mohammad Emtiyaz Khan; Reza Babanezhad 001; Wu Lin; Mark Schmidt 001; Masashi Sugiyama (CoRR, 2015)

Online Markov decision processes with policy iteration. - Yao Ma; Hao Zhang; Masashi Sugiyama (CoRR, 2015)

Supervised LogEuclidean Metric Learning for Symmetric Positive Definite Matrices. - Florian Yger; Masashi Sugiyama (CoRR, 2015)

Direct Density Ratio Estimation with Convolutional Neural Networks with Application in Outlier Detection. - Hyun Ha Nam; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2015)

Condition for perfect dimensionality recovery by variational Bayesian PCA. - Shinichi Nakajima; Ryota Tomioka; Masashi Sugiyama; S. Derin Babacan (J. Mach. Learn. Res., 2015)

Introduction: special issue of selected papers of ACML 2013. - Cheng Soon Ong; Wray L. Buntine; Tu Bao Ho; Masashi Sugiyama; Geoffrey I. Webb (Mach. Learn., 2015)

Direct conditional probability density estimation with sparse feature selection. - Motoki Shiga; Voot Tangkaratt; Masashi Sugiyama (Mach. Learn., 2015)

Online Direct Density-Ratio Estimation Applied to Inlier-Based Outlier Detection. - Marthinus Christoffel du Plessis; Hiroaki Shiino; Masashi Sugiyama (Neural Comput., 2015)

Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization. - Voot Tangkaratt; Ning Xie 003; Masashi Sugiyama (Neural Comput., 2015)

Bandit-Based Task Assignment for Heterogeneous Crowdsourcing. - Hao Zhang; Yao Ma; Masashi Sugiyama (Neural Comput., 2015)

Cross-Domain Matching with Squared-Loss Mutual Information. - Makoto Yamada; Leonid Sigal; Michalis Raptis; Machiko Toyoda; Yi Chang 001; Masashi Sugiyama (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Importance-weighted covariance estimation for robust common spatial pattern. - Alessandro Balzi; Florian Yger; Masashi Sugiyama (Pattern Recognit. Lett., 2015)

Active Learning in Recommender Systems. - Neil Rubens; Mehdi Elahi; Masashi Sugiyama; Dain Kaplan (Recommender Systems Handbook, 2015)

Geometry-aware stationary subspace analysis. - Inbal Horev; Florian Yger; Masashi Sugiyama (ACML, 2016)

Multitask Principal Component Analysis. - Ikko Yamane; Florian Yger; Maxime Berar; Masashi Sugiyama (ACML, 2016)

Non-Gaussian Component Analysis with Log-Density Gradient Estimation. - Hiroaki Sasaki; Gang Niu 001; Masashi Sugiyama (AISTATS, 2016)

Target-less camera-LiDAR extrinsic calibration using a bagged dependence estimator. - Kiyoshi Irie; Masashi Sugiyama; Masahiro Tomono (CASE, 2016)

Structure Learning of Partitioned Markov Networks. - Song Liu 002; Taiji Suzuki; Masashi Sugiyama; Kenji Fukumizu (ICML, 2016)

Modal Regression via Direct Log-Density Derivative Estimation. - Hiroaki Sasaki; Yurina Ono; Masashi Sugiyama (ICONIP (2), 2016)

Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning. - Gang Niu 001; Marthinus Christoffel du Plessis; Tomoya Sakai 001; Yao Ma; Masashi Sugiyama (NIPS, 2016)

Semi-supervised sufficient dimension reduction under class-prior change. - Hideko Kawakubo; Masashi Sugiyama (TAAI, 2016)

Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions. - Mohammad Emtiyaz Khan; Reza Babanezhad 001; Wu Lin; Mark Schmidt 001; Masashi Sugiyama (UAI, 2016)

Dependence maximization localization: a novel approach to 2D street-map-based robot localization. - Kiyoshi Irie; Masashi Sugiyama; Masahiro Tomono (Adv. Robotics, 2016)

Robust supervised learning under uncertainty in dataset shift. - Weihua Hu; Issei Sato; Masashi Sugiyama (CoRR, 2016)

Reinterpreting the Transformation Posterior in Probabilistic Image Registration. - Jie Luo 003; Karteek Popuri; Dana Cobzas; Hongyi Ding; Masashi Sugiyama (CoRR, 2016)

Theoretical Comparisons of Learning from Positive-Negative, Positive-Unlabeled, and Negative-Unlabeled Data. - Gang Niu 001; Marthinus Christoffel du Plessis; Tomoya Sakai 001; Masashi Sugiyama (CoRR, 2016)

Beyond the Low-density Separation Principle: A Novel Approach to Semi-supervised Learning. - Tomoya Sakai 001; Marthinus Christoffel du Plessis; Gang Niu 001; Masashi Sugiyama (CoRR, 2016)

Computationally Efficient Class-Prior Estimation under Class Balance Change Using Energy Distance. - Hideko Kawakubo; Marthinus Christoffel du Plessis; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2016)

An Online Policy Gradient Algorithm for Markov Decision Processes with Continuous States and Actions. - Yao Ma; Tingting Zhao 001; Kohei Hatano; Masashi Sugiyama (Neural Comput., 2016)

Direct Density Derivative Estimation. - Hiroaki Sasaki; Yung-Kyun Noh; Gang Niu 001; Masashi Sugiyama (Neural Comput., 2016)

Theoretical and Experimental Analyses of Tensor-Based Regression and Classification. - Kishan Wimalawarne; Ryota Tomioka; Masashi Sugiyama (Neural Comput., 2016)

Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation. - Ikko Yamane; Hiroaki Sasaki; Masashi Sugiyama (Neural Comput., 2016)

Model-based reinforcement learning with dimension reduction. - Voot Tangkaratt; Jun Morimoto; Masashi Sugiyama (Neural Networks, 2016)

Trial and Error: Using Previous Experiences as Simulation Models in Humanoid Motor Learning. - Norikazu Sugimoto; Voot Tangkaratt; Thijs Wensveen; Tingting Zhao 001; Masashi Sugiyama; Jun Morimoto (IEEE Robotics Autom. Mag., 2016)

Policy Search with High-Dimensional Context Variables. - Voot Tangkaratt; Herke van Hoof; Simone Parisi; Gerhard Neumann; Jan Peters 001; Masashi Sugiyama (AAAI, 2017)

Whitening-Free Least-Squares Non-Gaussian Component Analysis. - Hiroaki Shiino; Hiroaki Sasaki; Gang Niu 001; Masashi Sugiyama (ACML, 2017)

Least-Squares Log-Density Gradient Clustering for Riemannian Manifolds. - Mina Ashizawa; Hiroaki Sasaki; Tomoya Sakai 001; Masashi Sugiyama (AISTATS, 2017)

Estimating Density Ridges by Direct Estimation of Density-Derivative-Ratios. - Hiroaki Sasaki; Takafumi Kanamori; Masashi Sugiyama (AISTATS, 2017)

Learning Discrete Representations via Information Maximizing Self-Augmented Training. - Weihua Hu; Takeru Miyato; Seiya Tokui; Eiichi Matsumoto; Masashi Sugiyama (ICML, 2017)

Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data. - Tomoya Sakai 001; Marthinus Christoffel du Plessis; Gang Niu 001; Masashi Sugiyama (ICML, 2017)

Lung lesion detection in FDG-PET/CT with Gaussian process regression. - Ryosuke Kamesawa; Issei Sato; Shouhei Hanaoka; Yukihiro Nomura; Mitsutaka Nemoto; Naoto Hayashi; Masashi Sugiyama (Computer-Aided Diagnosis, 2017)

Expectation Propagation for t-Exponential Family Using q-Algebra. - Futoshi Futami; Issei Sato; Masashi Sugiyama (NIPS, 2017)

Learning from Complementary Labels. - Takashi Ishida 001; Gang Niu 001; Weihua Hu; Masashi Sugiyama (NIPS, 2017)

Positive-Unlabeled Learning with Non-Negative Risk Estimator. - Ryuichi Kiryo; Gang Niu 001; Marthinus Christoffel du Plessis; Masashi Sugiyama (NIPS, 2017)

Generative Local Metric Learning for Kernel Regression. - Yung-Kyun Noh; Masashi Sugiyama; Kee-Eung Kim; Frank C. Park 001; Daniel D. Lee (NIPS, 2017)

Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags. - Han Bao 002; Tomoya Sakai 001; Issei Sato; Masashi Sugiyama (CoRR, 2017)

Learning from Complementary Labels. - Takashi Ishida 001; Gang Niu 001; Masashi Sugiyama (CoRR, 2017)

Misdirected Registration Uncertainty. - Jie Luo 003; Karteek Popuri; Dana Cobzas; Hongyi Ding; William M. Wells III; Masashi Sugiyama (CoRR, 2017)

Learning Efficient Tensor Representations with Ring Structure Networks. - Qibin Zhao; Masashi Sugiyama; Andrzej Cichocki (CoRR, 2017)

Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives. - Andrzej Cichocki; Anh Huy Phan 001; Qibin Zhao; Namgil Lee; Ivan V. Oseledets; Masashi Sugiyama; Danilo P. Mandic (CoRR, 2017)

Estimation of Squared-Loss Mutual Information from Positive and Unlabeled Data. - Tomoya Sakai 001; Gang Niu 001; Masashi Sugiyama (CoRR, 2017)

Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives. - Andrzej Cichocki; Anh Huy Phan 001; Qibin Zhao; Namgil Lee; Ivan V. Oseledets; Masashi Sugiyama; Danilo P. Mandic (Found. Trends Mach. Learn., 2017)

Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios. - Hiroaki Sasaki; Takafumi Kanamori; Aapo Hyvärinen; Gang Niu 001; Masashi Sugiyama (J. Mach. Learn. Res., 2017)

Foreword: special issue for the journal track of the 8th Asian conference on machine learning (ACML 2016). - Robert J. Durrant; Kee-Eung Kim; Geoffrey Holmes 001; Stephen Marsland; Masashi Sugiyama; Zhi-Hua Zhou (Mach. Learn., 2017)

Introduction: special issue of selected papers from ACML 2015. - Geoffrey Holmes 001; Tie-Yan Liu; Hang Li 001; Irwin King; Masashi Sugiyama; Zhi-Hua Zhou (Mach. Learn., 2017)

Geometry-aware principal component analysis for symmetric positive definite matrices. - Inbal Horev; Florian Yger; Masashi Sugiyama (Mach. Learn., 2017)

Class-prior estimation for learning from positive and unlabeled data. - Marthinus Christoffel du Plessis; Gang Niu 001; Masashi Sugiyama (Mach. Learn., 2017)

Homotopy continuation approaches for robust SV classification and regression. - Shinya Suzumura; Kohei Ogawa; Masashi Sugiyama; Masayuki Karasuyama; Ichiro Takeuchi (Mach. Learn., 2017)

Direct Estimation of the Derivative of Quadratic Mutual Information with Application in Supervised Dimension Reduction. - Voot Tangkaratt; Hiroaki Sasaki; Masashi Sugiyama (Neural Comput., 2017)

Hierarchical Policy Search via Return-Weighted Density Estimation. - Takayuki Osa; Masashi Sugiyama (AAAI, 2018)

Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling. - Hongyi Ding; Mohammad Emtiyaz Khan; Issei Sato; Masashi Sugiyama (AISTATS, 2018)

Variational Inference based on Robust Divergences. - Futoshi Futami; Issei Sato; Masashi Sugiyama (AISTATS, 2018)

A fully adaptive algorithm for pure exploration in linear bandits. - Liyuan Xu; Junya Honda; Masashi Sugiyama (AISTATS, 2018)

Multi Task Learning with Positive and Unlabeled Data and its Application to Mental State Prediction. - Hirotaka Kaji; Hayato Yamaguchi; Masashi Sugiyama (ICASSP, 2018)

Guide Actor-Critic for Continuous Control. - Voot Tangkaratt; Abbas Abdolmaleki; Masashi Sugiyama (ICLR (Poster), 2018)

Learning Efficient Tensor Representations with Ring Structure Networks. - Qibin Zhao; Masashi Sugiyama; Longhao Yuan; Andrzej Cichocki (ICLR (Workshop), 2018)

Classification from Pairwise Similarity and Unlabeled Data. - Han Bao 002; Gang Niu 001; Masashi Sugiyama (ICML, 2018)

Does Distributionally Robust Supervised Learning Give Robust Classifiers? - Weihua Hu; Gang Niu 001; Issei Sato; Masashi Sugiyama (ICML, 2018)

Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model. - Hideaki Imamura; Issei Sato; Masashi Sugiyama (ICML, 2018)

Active Feature Acquisition with Supervised Matrix Completion. - Sheng-Jun Huang; Miao Xu 001; Ming-Kun Xie; Masashi Sugiyama; Gang Niu 001; Songcan Chen (KDD, 2018)

A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation. - Jie Luo 003; Matthew Toews; Inês Machado; Sarah F. Frisken; Miaomiao Zhang 002; Frank Preiswerk; Alireza Sedghi; Hongyi Ding; Steve Pieper 001; Polina Golland; Alexandra J. Golby; Masashi Sugiyama; et al. (MICCAI (4), 2018)

Masking: A New Perspective of Noisy Supervision. - Bo Han 003; Jiangchao Yao; Gang Niu 001; Mingyuan Zhou; Ivor W. Tsang; Ya Zhang 002; Masashi Sugiyama (NeurIPS, 2018)

Co-teaching: Robust training of deep neural networks with extremely noisy labels. - Bo Han 003; Quanming Yao; Xingrui Yu; Gang Niu 001; Miao Xu 001; Weihua Hu; Ivor W. Tsang; Masashi Sugiyama (NeurIPS, 2018)

Binary Classification from Positive-Confidence Data. - Takashi Ishida 001; Gang Niu 001; Masashi Sugiyama (NeurIPS, 2018)

Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces. - Motoya Ohnishi; Masahiro Yukawa; Mikael Johansson; Masashi Sugiyama (NeurIPS, 2018)

Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks. - Yusuke Tsuzuku; Issei Sato; Masashi Sugiyama (NeurIPS, 2018)

Uplift Modeling from Separate Labels. - Ikko Yamane; Florian Yger; Jamal Atif; Masashi Sugiyama (NeurIPS, 2018)

Variational Inference for Gaussian Processes with Panel Count Data. - Hongyi Ding; Young Lee; Issei Sato; Masashi Sugiyama (UAI, 2018)

Using the variogram for vector outlier screening: application to feature-based image registration. - Jie Luo 003; Sarah F. Frisken; Inês Machado; Miaomiao Zhang 002; Steve Pieper 001; Polina Golland; Matthew Toews; Prashin Unadkat; Alireza Sedghi; Haoyin Zhou; Alireza Mehrtash; Frank Preiswerk; et al. (Int. J. Comput. Assist. Radiol. Surg., 2018)

Binary Matrix Completion Using Unobserved Entries. - Masayoshi Hayashi; Tomoya Sakai 001; Masashi Sugiyama (CoRR, 2018)

On the Ambiguity of Registration Uncertainty. - Jie Luo 003; Sarah F. Frisken; Karteek Popuri; Dana Cobzas; Frank Preiswerk; Matthew Toews; Miaomiao Zhang 002; Hongyi Ding; Polina Golland; Alexandra J. Golby; Masashi Sugiyama; William M. Wells III (CoRR, 2018)

Co-sampling: Training Robust Networks for Extremely Noisy Supervision. - Bo Han 003; Quanming Yao; Xingrui Yu; Gang Niu 001; Miao Xu 001; Weihua Hu; Ivor W. Tsang; Masashi Sugiyama (CoRR, 2018)

Frank-Wolfe Stein Sampling. - Futoshi Futami; Zhenghang Cui; Issei Sato; Masashi Sugiyama (CoRR, 2018)

Matrix Co-completion for Multi-label Classification with Missing Features and Labels. - Miao Xu 001; Gang Niu 001; Bo Han 003; Ivor W. Tsang; Zhi-Hua Zhou; Masashi Sugiyama (CoRR, 2018)

Alternate Estimation of a Classifier and the Class-Prior from Positive and Unlabeled Data. - Masahiro Kato; Liyuan Xu; Gang Niu 001; Masashi Sugiyama (CoRR, 2018)

Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels. - Bo Han 003; Gang Niu 001; Jiangchao Yao; Xingrui Yu; Miao Xu 001; Ivor W. Tsang; Masashi Sugiyama (CoRR, 2018)

Stochastic Divergence Minimization for Biterm Topic Models. - Zhenghang Cui; Issei Sato; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2018)

Semi-supervised AUC optimization based on positive-unlabeled learning. - Tomoya Sakai 001; Gang Niu 001; Masashi Sugiyama (Mach. Learn., 2018)

Correction to: Semi-supervised AUC optimization based on positive-unlabeled learning. - Tomoya Sakai 001; Gang Niu 001; Masashi Sugiyama (Mach. Learn., 2018)

Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence. - Yung-Kyun Noh; Masashi Sugiyama; Song Liu 002; Marthinus Christoffel du Plessis; Frank Chongwoo Park; Daniel D. Lee (Neural Comput., 2018)

Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities. - Hiroaki Sasaki; Voot Tangkaratt; Gang Niu 001; Masashi Sugiyama (Neural Comput., 2018)

Convex formulation of multiple instance learning from positive and unlabeled bags. - Han Bao 002; Tomoya Sakai 001; Issei Sato; Masashi Sugiyama (Neural Networks, 2018)

Bayesian Posterior Approximation via Greedy Particle Optimization. - Futoshi Futami; Zhenghang Cui; Issei Sato; Masashi Sugiyama (AAAI, 2019)

Bézier Simplex Fitting: Describing Pareto Fronts of Simplicial Problems with Small Samples in Multi-Objective Optimization. - Ken Kobayashi; Naoki Hamada; Akiyoshi Sannai; Akinori Tanaka; Kenichi Bannai; Masashi Sugiyama (AAAI, 2019)

Unsupervised Domain Adaptation Based on Source-Guided Discrepancy. - Seiichi Kuroki; Nontawat Charoenphakdee; Han Bao 002; Junya Honda; Issei Sato; Masashi Sugiyama (AAAI, 2019)

Clipped Matrix Completion: A Remedy for Ceiling Effects. - Takeshi Teshima; Miao Xu 001; Issei Sato; Masashi Sugiyama (AAAI, 2019)

Dueling Bandits with Qualitative Feedback. - Liyuan Xu; Junya Honda; Masashi Sugiyama (AAAI, 2019)

Zero-shot Domain Adaptation Based on Attribute Information. - Masato Ishii; Takashi Takenouchi; Masashi Sugiyama (ACML, 2019)

Learning Only from Relevant Keywords and Unlabeled Documents. - Nontawat Charoenphakdee; Jongyeong Lee; Yiping Jin; Dittaya Wanvarie; Masashi Sugiyama (EMNLP/IJCNLP (1), 2019)

Binary Classification Only from Unlabeled Data by Iterative Unlabeled-unlabeled Classification. - Hirotaka Kaji; Masashi Sugiyama (ICASSP, 2019)

Learning Efficient Tensor Representations with Ring-structured Networks. - Qibin Zhao; Masashi Sugiyama; Longhao Yuan; Andrzej Cichocki (ICASSP, 2019)

On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data. - Nan Lu; Gang Niu 001; Aditya Krishna Menon; Masashi Sugiyama (ICLR (Poster), 2019)

Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization. - Takayuki Osa; Voot Tangkaratt; Masashi Sugiyama (ICLR (Poster), 2019)

On Symmetric Losses for Learning from Corrupted Labels. - Nontawat Charoenphakdee; Jongyeong Lee; Masashi Sugiyama (ICML, 2019)

Classification from Positive, Unlabeled and Biased Negative Data. - Yu-Guan Hsieh; Gang Niu 001; Masashi Sugiyama (ICML, 2019)

Complementary-Label Learning for Arbitrary Losses and Models. - Takashi Ishida 001; Gang Niu 001; Aditya Krishna Menon; Masashi Sugiyama (ICML, 2019)

Imitation Learning from Imperfect Demonstration. - Yueh-Hua Wu; Nontawat Charoenphakdee; Han Bao 002; Voot Tangkaratt; Masashi Sugiyama (ICML, 2019)

How does Disagreement Help Generalization against Label Corruption? - Xingrui Yu; Bo Han 003; Jiangchao Yao; Gang Niu 001; Ivor W. Tsang; Masashi Sugiyama (ICML, 2019)

On the Applicability of Registration Uncertainty. - Jie Luo 003; Alireza Sedghi; Karteek Popuri; Dana Cobzas; Miaomiao Zhang 002; Frank Preiswerk; Matthew Toews; Alexandra J. Golby; Masashi Sugiyama; William M. Wells III; Sarah F. Frisken (MICCAI (2), 2019)

On the Calibration of Multiclass Classification with Rejection. - Chenri Ni; Nontawat Charoenphakdee; Junya Honda; Masashi Sugiyama (NeurIPS, 2019)

Are Anchor Points Really Indispensable in Label-Noise Learning? - Xiaobo Xia; Tongliang Liu; Nannan Wang 001; Bo Han 003; Chen Gong 002; Gang Niu 001; Masashi Sugiyama (NeurIPS, 2019)

Uncoupled Regression from Pairwise Comparison Data. - Liyuan Xu; Junya Honda; Gang Niu 001; Masashi Sugiyama (NeurIPS, 2019)

Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error. - Nontawat Charoenphakdee; Masashi Sugiyama (SDM, 2019)

An analytic formulation for positive-unlabeled learning via weighted integral probability metric. - Yongchan Kwon; Wonyoung Kim; Masashi Sugiyama; Myunghee Cho Paik (CoRR, 2019)

Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative. - Miao Xu 001; Bingcong Li; Gang Niu 001; Bo Han 003; Masashi Sugiyama (CoRR, 2019)

Domain Discrepancy Measure Using Complex Models in Unsupervised Domain Adaptation. - Jongyeong Lee; Nontawat Charoenphakdee; Seiichi Kuroki; Masashi Sugiyama (CoRR, 2019)

On Possibility and Impossibility of Multiclass Classification with Rejection. - Chenri Ni; Nontawat Charoenphakdee; Junya Honda; Masashi Sugiyama (CoRR, 2019)

New Tricks for Estimating Gradients of Expectations. - Christian J. Walder; Richard Nock; Cheng Soon Ong; Masashi Sugiyama (CoRR, 2019)

Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization. - Taira Tsuchiya; Nontawat Charoenphakdee; Issei Sato; Masashi Sugiyama (CoRR, 2019)

Online Multiclass Classification Based on Prediction Margin for Partial Feedback. - Takuo Kaneko; Issei Sato; Masashi Sugiyama (CoRR, 2019)

Polynomial-time Algorithms for Combinatorial Pure Exploration with Full-bandit Feedback. - Yuko Kuroki; Liyuan Xu; Atsushi Miyauchi 001; Junya Honda; Masashi Sugiyama (CoRR, 2019)

Butterfly: A Panacea for All Difficulties in Wildly Unsupervised Domain Adaptation. - Feng Liu 003; Jie Lu 001; Bo Han 003; Gang Niu 001; Guangquan Zhang 001; Masashi Sugiyama (CoRR, 2019)

Solving NP-Hard Problems on Graphs by Reinforcement Learning without Domain Knowledge. - Kenshin Abe; Zijian Xu 002; Issei Sato; Masashi Sugiyama (CoRR, 2019)

Pilot Study on Verifying the Monotonic Relationship between Error and Uncertainty in Deformable Registration for Neurosurgery. - Jie Luo 003; Alexandra J. Golby; Masashi Sugiyama; William M. Wells III; Sarah F. Frisken (CoRR, 2019)

VILD: Variational Imitation Learning with Diverse-quality Demonstrations. - Voot Tangkaratt; Bo Han 003; Mohammad Emtiyaz Khan; Masashi Sugiyama (CoRR, 2019)

Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics. - Johannes Ackermann; Volker Gabler; Takayuki Osa; Masashi Sugiyama (CoRR, 2019)

Learning from Indirect Observations. - Yivan Zhang; Nontawat Charoenphakdee; Masashi Sugiyama (CoRR, 2019)

A unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme. - Paavo Parmas; Masashi Sugiyama (CoRR, 2019)

Where is the Bottleneck of Adversarial Learning with Unlabeled Data? - Jingfeng Zhang; Bo Han 003; Gang Niu 001; Tongliang Liu; Masashi Sugiyama (CoRR, 2019)

Millionaire: a hint-guided approach for crowdsourcing. - Bo Han 003; Quanming Yao; Yuangang Pan; Ivor W. Tsang; Xiaokui Xiao; Qiang Yang 001; Masashi Sugiyama (Mach. Learn., 2019)

Good arm identification via bandit feedback. - Hideaki Kano; Junya Honda; Kentaro Sakamaki; Kentaro Matsuura; Atsuyoshi Nakamura; Masashi Sugiyama (Mach. Learn., 2019)

Foreword: special issue for the journal track of the 10th Asian Conference on Machine Learning (ACML 2018). - Masashi Sugiyama; Yung-Kyun Noh (Mach. Learn., 2019)

ECG-Based Concentration Recognition With Multi-Task Regression. - Hirotaka Kaji; Hisashi Iizuka; Masashi Sugiyama (IEEE Trans. Biomed. Eng., 2019)

Unsupervised Discrete Representation Learning. - Weihua Hu; Takeru Miyato; Seiya Tokui; Eiichi Matsumoto; Masashi Sugiyama (Explainable AI, 2019)

Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification. - Han Bao 002; Masashi Sugiyama (AISTATS, 2020)

Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach. - Nan Lu; Tianyi Zhang; Gang Niu 001; Masashi Sugiyama (AISTATS, 2020)

Calibrated Surrogate Losses for Adversarially Robust Classification. - Han Bao 002; Clayton Scott; Masashi Sugiyama (COLT, 2020)

SIGUA: Forgetting May Make Learning with Noisy Labels More Robust. - Bo Han 003; Gang Niu 001; Xingrui Yu; Quanming Yao; Miao Xu 001; Ivor W. Tsang; Masashi Sugiyama (ICML, 2020)

Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels. - Yu-Ting Chou; Gang Niu 001; Hsuan-Tien Lin; Masashi Sugiyama (ICML, 2020)

Learning with Multiple Complementary Labels. - Lei Feng 006; Takuo Kaneko; Bo Han 003; Gang Niu 001; Bo An 001; Masashi Sugiyama (ICML, 2020)

Accelerating the diffusion-based ensemble sampling by non-reversible dynamics. - Futoshi Futami; Issei Sato; Masashi Sugiyama (ICML, 2020)

Do We Need Zero Training Loss After Achieving Zero Training Error? - Takashi Ishida 001; Ikko Yamane; Tomoya Sakai 001; Gang Niu 001; Masashi Sugiyama (ICML, 2020)

Online Dense Subgraph Discovery via Blurred-Graph Feedback. - Yuko Kuroki; Atsushi Miyauchi 001; Junya Honda; Masashi Sugiyama (ICML, 2020)

Progressive Identification of True Labels for Partial-Label Learning. - Jiaqi Lv; Miao Xu 001; Lei Feng 006; Gang Niu 001; Xin Geng 001; Masashi Sugiyama (ICML, 2020)

Variational Imitation Learning with Diverse-quality Demonstrations. - Voot Tangkaratt; Bo Han 003; Mohammad Emtiyaz Khan; Masashi Sugiyama (ICML, 2020)

Few-shot Domain Adaptation by Causal Mechanism Transfer. - Takeshi Teshima; Issei Sato; Masashi Sugiyama (ICML, 2020)

Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis. - Yusuke Tsuzuku; Issei Sato; Masashi Sugiyama (ICML, 2020)

Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. - Jingfeng Zhang; Xilie Xu; Bo Han 003; Gang Niu 001; Lizhen Cui; Masashi Sugiyama; Mohan S. Kankanhalli (ICML, 2020)

Binary Classification from Positive Data with Skewed Confidence. - Kazuhiko Shinoda; Hirotaka Kaji; Masashi Sugiyama (IJCAI, 2020)

Simultaneous Planning for Item Picking and Placing by Deep Reinforcement Learning. - Tatsuya Tanaka; Toshimitsu Kaneko; Masahiro Sekine; Voot Tangkaratt; Masashi Sugiyama (IROS, 2020)

Are Registration Uncertainty and Error Monotonically Associated? - Jie Luo 003; Sarah F. Frisken; Duo Wang; Alexandra J. Golby; Masashi Sugiyama; William M. Wells III (MICCAI (3), 2020)

Calibrated Surrogate Maximization of Dice. - Marcus Nordström; Han Bao 002; Fredrik Löfman; Henrik Hult; Atsuto Maki; Masashi Sugiyama (MICCAI (4), 2020)

Rethinking Importance Weighting for Deep Learning under Distribution Shift. - Tongtong Fang; Nan Lu; Gang Niu 001; Masashi Sugiyama (NeurIPS, 2020)

Provably Consistent Partial-Label Learning. - Lei Feng 006; Jiaqi Lv; Bo Han 003; Miao Xu 001; Gang Niu 001; Xin Geng 001; Bo An 001; Masashi Sugiyama (NeurIPS, 2020)

Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators. - Takeshi Teshima; Isao Ishikawa; Koichi Tojo; Kenta Oono; Masahiro Ikeda; Masashi Sugiyama (NeurIPS, 2020)

Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring. - Taira Tsuchiya; Junya Honda; Masashi Sugiyama (NeurIPS, 2020)

Part-dependent Label Noise: Towards Instance-dependent Label Noise. - Xiaobo Xia; Tongliang Liu; Bo Han 003; Nannan Wang 001; Mingming Gong; Haifeng Liu; Gang Niu 001; Dacheng Tao; Masashi Sugiyama (NeurIPS, 2020)

Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. - Yu Yao 005; Tongliang Liu; Bo Han 003; Mingming Gong; Jiankang Deng; Gang Niu 001; Masashi Sugiyama (NeurIPS, 2020)

Learning from Aggregate Observations. - Yivan Zhang; Nontawat Charoenphakdee; Zhenguo Wu; Masashi Sugiyama (NeurIPS, 2020)

Partially Zero-shot Domain Adaptation from Incomplete Target Data with Missing Classes. - Masato Ishii; Takashi Takenouchi; Masashi Sugiyama (WACV, 2020)

A Diffusion Theory for Deep Learning Dynamics: Stochastic Gradient Descent Escapes From Sharp Minima Exponentially Fast. - Zeke Xie; Issei Sato; Masashi Sugiyama (CoRR, 2020)

Towards Mixture Proportion Estimation without Irreducibility. - Yu Yao 005; Tongliang Liu; Bo Han 003; Mingming Gong; Gang Niu 001; Masashi Sugiyama; Dacheng Tao (CoRR, 2020)

Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time. - Hideaki Imamura; Nontawat Charoenphakdee; Futoshi Futami; Issei Sato; Junya Honda; Masashi Sugiyama (CoRR, 2020)

Do Public Datasets Assure Unbiased Comparisons for Registration Evaluation? - Jie Luo 003; Guangshen Ma; Sarah F. Frisken; Parikshit Juvekar; Nazim Haouchine; Zhe Xu 012; Yiming Xiao; Alexandra J. Golby; Patrick J. Codd; Masashi Sugiyama; William M. Wells III (CoRR, 2020)

Similarity-based Classification: Connecting Similarity Learning to Binary Classification. - Han Bao 002; Takuya Shimada; Liyuan Xu; Issei Sato; Masashi Sugiyama (CoRR, 2020)

Parts-dependent Label Noise: Towards Instance-dependent Label Noise. - Xiaobo Xia; Tongliang Liu; Bo Han 003; Nannan Wang 001; Mingming Gong; Haifeng Liu; Gang Niu 001; Dacheng Tao; Masashi Sugiyama (CoRR, 2020)

LFD-ProtoNet: Prototypical Network Based on Local Fisher Discriminant Analysis for Few-shot Learning. - Kei Mukaiyama; Issei Sato; Masashi Sugiyama (CoRR, 2020)

Generalisation Guarantees for Continual Learning with Orthogonal Gradient Descent. - Mehdi Abbana Bennani; Masashi Sugiyama (CoRR, 2020)

Adai: Separating the Effects of Adaptive Learning Rate and Momentum Inertia. - Zeke Xie; Xinrui Wang; Huishuai Zhang; Issei Sato; Masashi Sugiyama (CoRR, 2020)

Maximum Mean Discrepancy is Aware of Adversarial Attacks. - Ruize Gao; Feng Liu 003; Jingfeng Zhang; Bo Han 003; Tongliang Liu; Gang Niu 001; Masashi Sugiyama (CoRR, 2020)

A Survey of Label-noise Representation Learning: Past, Present and Future. - Bo Han 003; Quanming Yao; Tongliang Liu; Gang Niu 001; Ivor W. Tsang; James T. Kwok; Masashi Sugiyama (CoRR, 2020)

Stable Weight Decay Regularization. - Zeke Xie; Issei Sato; Masashi Sugiyama (CoRR, 2020)

Combinatorial Pure Exploration with Full-bandit Feedback and Beyond: Solving Combinatorial Optimization under Uncertainty with Limited Observation. - Yuko Kuroki; Junya Honda; Masashi Sugiyama (CoRR, 2020)

Unsupervised key frame selection using information theory and colour histogram difference. - Janya Sainui; Masashi Sugiyama (Int. J. Bus. Intell. Data Min., 2020)

Active deep Q-learning with demonstration. - Si-An Chen; Voot Tangkaratt; Hsuan-Tien Lin; Masashi Sugiyama (Mach. Learn., 2020)

Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric. - Yongchan Kwon; Wonyoung Kim; Masashi Sugiyama; Myunghee Cho Paik (Mach. Learn., 2020)

Binary classification with ambiguous training data. - Naoya Otani; Yosuke Otsubo; Tetsuya Koike; Masashi Sugiyama (Mach. Learn., 2020)

Classification from Triplet Comparison Data. - Zhenghang Cui; Nontawat Charoenphakdee; Issei Sato; Masashi Sugiyama (Neural Comput., 2020)

Polynomial-Time Algorithms for Multiple-Arm Identification with Full-Bandit Feedback. - Yuko Kuroki; Liyuan Xu; Atsushi Miyauchi 001; Junya Honda; Masashi Sugiyama (Neural Comput., 2020)

Stochastic Multichannel Ranking with Brain Dynamics Preferences. - Yuangang Pan; Ivor W. Tsang; Avinash Kumar Singh; Chin-Teng Lin; Masashi Sugiyama (Neural Comput., 2020)

Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation. - Han Bao 002; Masashi Sugiyama (AISTATS, 2021)

γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator. - Masahiro Fujisawa; Takeshi Teshima; Issei Sato; Masashi Sugiyama (AISTATS, 2021)

A unified view of likelihood ratio and reparameterization gradients. - Paavo Parmas; Masashi Sugiyama (AISTATS, 2021)

Robust Imitation Learning from Noisy Demonstrations. - Voot Tangkaratt; Nontawat Charoenphakdee; Masashi Sugiyama (AISTATS, 2021)

Mixture Proportion Estimation in Weakly Supervised Learning. - Masashi Sugiyama (CIKM Workshops, 2021)

On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective. - Nontawat Charoenphakdee; Jayakorn Vongkulbhisal; Nuttapong Chairatanakul; Masashi Sugiyama (CVPR, 2021)

Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning. - Alon Jacovi; Gang Niu 001; Yoav Goldberg; Masashi Sugiyama (EACL, 2021)

A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima. - Zeke Xie; Issei Sato; Masashi Sugiyama (ICLR, 2021)

Geometry-aware Instance-reweighted Adversarial Training. - Jingfeng Zhang; Jianing Zhu; Gang Niu 001; Bo Han 003; Masashi Sugiyama; Mohan S. Kankanhalli (ICLR, 2021)

Large-Margin Contrastive Learning with Distance Polarization Regularizer. - Shuo Chen 003; Gang Niu 001; Chen Gong 002; Jun Li 027; Jian Yang 003; Masashi Sugiyama (ICML, 2021)

Confidence Scores Make Instance-dependent Label-noise Learning Possible. - Antonin Berthon; Bo Han 003; Gang Niu 001; Tongliang Liu; Masashi Sugiyama (ICML, 2021)

Learning from Similarity-Confidence Data. - Yuzhou Cao; Lei Feng 006; Yitian Xu; Bo An 001; Gang Niu 001; Masashi Sugiyama (ICML, 2021)

Classification with Rejection Based on Cost-sensitive Classification. - Nontawat Charoenphakdee; Zhenghang Cui; Yivan Zhang; Masashi Sugiyama (ICML, 2021)

Learning Diverse-Structured Networks for Adversarial Robustness. - Xuefeng Du; Jingfeng Zhang; Bo Han 003; Tongliang Liu; Yu Rong 001; Gang Niu 001; Junzhou Huang; Masashi Sugiyama (ICML, 2021)

Pointwise Binary Classification with Pairwise Confidence Comparisons. - Lei Feng 006; Senlin Shu; Nan Lu; Bo Han 003; Miao Xu 001; Gang Niu 001; Bo An 001; Masashi Sugiyama (ICML, 2021)

Maximum Mean Discrepancy Test is Aware of Adversarial Attacks. - Ruize Gao; Feng Liu 003; Jingfeng Zhang; Bo Han 003; Tongliang Liu; Gang Niu 001; Masashi Sugiyama (ICML, 2021)

Provably End-to-end Label-noise Learning without Anchor Points. - Xuefeng Li; Tongliang Liu; Bo Han 003; Gang Niu 001; Masashi Sugiyama (ICML, 2021)

Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. - Nan Lu; Shida Lei; Gang Niu 001; Issei Sato; Masashi Sugiyama (ICML, 2021)

Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization. - Zeke Xie; Li Yuan; Zhanxing Zhu; Masashi Sugiyama (ICML, 2021)

Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences. - Ikko Yamane; Junya Honda; Florian Yger; Masashi Sugiyama (ICML, 2021)

CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection. - Hanshu Yan; Jingfeng Zhang; Gang Niu 001; Jiashi Feng; Vincent Y. F. Tan; Masashi Sugiyama (ICML, 2021)

Lower-Bounded Proper Losses for Weakly Supervised Classification. - Shuhei M. Yoshida; Takashi Takenouchi; Masashi Sugiyama (ICML, 2021)

Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. - Yivan Zhang; Gang Niu 001; Masashi Sugiyama (ICML, 2021)

Loss function based second-order Jensen inequality and its application to particle variational inference. - Futoshi Futami; Tomoharu Iwata; Naonori Ueda; Issei Sato; Masashi Sugiyama (NeurIPS, 2021)

Probabilistic Margins for Instance Reweighting in Adversarial Training. - Qizhou Wang; Feng Liu 003; Bo Han 003; Tongliang Liu; Chen Gong 002; Gang Niu 001; Mingyuan Zhou; Masashi Sugiyama (NeurIPS, 2021)

Learning from Noisy Similar and Dissimilar Data. - Soham Dan; Han Bao 002; Masashi Sugiyama (ECML/PKDD (2), 2021)

Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation. - Takeshi Teshima; Masashi Sugiyama (UAI, 2021)

Constraint learning for control tasks with limited duration barrier functions. - Motoya Ohnishi; Gennaro Notomista; Masashi Sugiyama; Magnus Egerstedt (Autom., 2021)

A Symmetric Loss Perspective of Reliable Machine Learning. - Nontawat Charoenphakdee; Jongyeong Lee; Masashi Sugiyama (CoRR, 2021)

Source-free Domain Adaptation via Distributional Alignment by Matching Batch Normalization Statistics. - Masato Ishii; Masashi Sugiyama (CoRR, 2021)

Understanding the Interaction of Adversarial Training with Noisy Labels. - Jianing Zhu; Jingfeng Zhang; Bo Han 003; Tongliang Liu; Gang Niu 001; Hongxia Yang; Mohan S. Kankanhalli; Masashi Sugiyama (CoRR, 2021)

Guided Interpolation for Adversarial Training. - Chen Chen 043; Jingfeng Zhang; Xilie Xu; Tianlei Hu; Gang Niu 001; Gang Chen 001; Masashi Sugiyama (CoRR, 2021)

Discovering Diverse Solutions in Deep Reinforcement Learning. - Takayuki Osa; Voot Tangkaratt; Masashi Sugiyama (CoRR, 2021)

Approximating Instance-Dependent Noise via Instance-Confidence Embedding. - Yivan Zhang; Masashi Sugiyama (CoRR, 2021)

NoiLIn: Do Noisy Labels Always Hurt Adversarial Training? - Jingfeng Zhang; Xilie Xu; Bo Han 003; Tongliang Liu; Gang Niu 001; Lizhen Cui; Masashi Sugiyama (CoRR, 2021)

Instance Correction for Learning with Open-set Noisy Labels. - Xiaobo Xia; Tongliang Liu; Bo Han 003; Mingming Gong; Jun Yu 001; Gang Niu 001; Masashi Sugiyama (CoRR, 2021)

On the Robustness of Average Losses for Partial-Label Learning. - Jiaqi Lv; Lei Feng 006; Miao Xu 001; Bo An 001; Gang Niu 001; Xin Geng 001; Masashi Sugiyama (CoRR, 2021)

Multi-Class Classification from Single-Class Data with Confidences. - Yuzhou Cao; Lei Feng 006; Senlin Shu; Yitian Xu; Bo An 001; Gang Niu 001; Masashi Sugiyama (CoRR, 2021)

Seeing Differently, Acting Similarly: Imitation Learning with Heterogeneous Observations. - Xin-Qiang Cai; Yao-Xiang Ding 001; Zi-Xuan Chen; Yuan Jiang 001; Masashi Sugiyama; Zhi-Hua Zhou (CoRR, 2021)

Active Refinement for Multi-Label Learning: A Pseudo-Label Approach. - Cheng-Yu Hsieh; Wei-I Lin; Miao Xu 001; Gang Niu 001; Hsuan-Tien Lin; Masashi Sugiyama (CoRR, 2021)

Rethinking Importance Weighting for Transfer Learning. - Nan Lu; Tianyi Zhang; Tongtong Fang; Takeshi Teshima; Masashi Sugiyama (CoRR, 2021)

Learning with Proper Partial Labels. - Zhenguo Wu; Masashi Sugiyama (CoRR, 2021)

Information-Theoretic Representation Learning for Positive-Unlabeled Classification. - Tomoya Sakai 001; Gang Niu 001; Masashi Sugiyama (Neural Comput., 2021)

Classification From Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization. - Takuya Shimada; Han Bao 002; Issei Sato; Masashi Sugiyama (Neural Comput., 2021)

Semisupervised Ordinal Regression Based on Empirical Risk Minimization. - Taira Tsuchiya; Nontawat Charoenphakdee; Issei Sato; Masashi Sugiyama (Neural Comput., 2021)

Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting. - Zeke Xie; Fengxiang He; Shaopeng Fu; Issei Sato; Dacheng Tao; Masashi Sugiyama (Neural Comput., 2021)

Direction Matters: On Influence-Preserving Graph Summarization and Max-Cut Principle for Directed Graphs. - Wenkai Xu; Gang Niu 001; Aapo Hyvärinen; Masashi Sugiyama (Neural Comput., 2021)

A One-Step Approach to Covariate Shift Adaptation. - Tianyi Zhang; Ikko Yamane; Nan Lu; Masashi Sugiyama (SN Comput. Sci., 2021)

Robust computation of optimal transport by β-potential regularization. - Shintaro Nakamura; Han Bao 002; Masashi Sugiyama (ACML, 2022)

Multi-class Classification from Multiple Unlabeled Datasets with Partial Risk Regularization. - Yuting Tang; Nan Lu; Tianyi Zhang; Masashi Sugiyama (ACML, 2022)

Pairwise Supervision Can Provably Elicit a Decision Boundary. - Han Bao 002; Takuya Shimada; Liyuan Xu; Issei Sato; Masashi Sugiyama (AISTATS, 2022)

Predictive variational Bayesian inference as risk-seeking optimization. - Futoshi Futami; Tomoharu Iwata; Naonori Ueda; Issei Sato; Masashi Sugiyama (AISTATS, 2022)

Learning and Mining with Noisy Labels. - Masashi Sugiyama; Tongliang Liu; Bo Han 003; Yang Liu 018; Gang Niu 001 (CIKM, 2022)

Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. - De Cheng; Tongliang Liu; Yixiong Ning; Nannan Wang 001; Bo Han 003; Gang Niu 001; Xinbo Gao 001; Masashi Sugiyama (CVPR, 2022)

Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. - Haoang Chi; Feng Liu 003; Wenjing Yang 002; Long Lan; Tongliang Liu; Bo Han 003; Gang Niu 001; Mingyuan Zhou; Masashi Sugiyama (ICLR, 2022)

Federated Learning from Only Unlabeled Data with Class-conditional-sharing Clients. - Nan Lu; Zhao Wang 006; Xiaoxiao Li; Gang Niu 001; Qi Dou 001; Masashi Sugiyama (ICLR, 2022)

Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. - Xiaobo Xia; Tongliang Liu; Bo Han 003; Mingming Gong; Jun Yu 001; Gang Niu 001; Masashi Sugiyama (ICLR, 2022)

Rethinking Class-Prior Estimation for Positive-Unlabeled Learning. - Yu Yao 005; Tongliang Liu; Bo Han 003; Mingming Gong; Gang Niu 001; Masashi Sugiyama; Dacheng Tao (ICLR, 2022)

Exploiting Class Activation Value for Partial-Label Learning. - Fei Zhang; Lei Feng 006; Bo Han 003; Tongliang Liu; Gang Niu 001; Tao Qin; Masashi Sugiyama (ICLR, 2022)

To Smooth or Not? When Label Smoothing Meets Noisy Labels. - Jiaheng Wei; Hangyu Liu; Tongliang Liu; Gang Niu 001; Masashi Sugiyama; Yang Liu 018 (ICML, 2022)

Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum. - Zeke Xie; Xinrui Wang; Huishuai Zhang; Issei Sato; Masashi Sugiyama (ICML, 2022)

Adversarial Attack and Defense for Non-Parametric Two-Sample Tests. - Xilie Xu; Jingfeng Zhang; Feng Liu 003; Masashi Sugiyama; Mohan S. Kankanhalli (ICML, 2022)

Towards Adversarially Robust Deep Image Denoising. - Hanshu Yan; Jingfeng Zhang; Jiashi Feng; Masashi Sugiyama; Vincent Y. F. Tan (IJCAI, 2022)

Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. - Jianan Zhou 002; Jianing Zhu; Jingfeng Zhang; Tongliang Liu; Gang Niu 001; Bo Han 003; Masashi Sugiyama (NeurIPS, 2022)

Learning Contrastive Embedding in Low-Dimensional Space. - Shuo Chen 003; Chen Gong 002; Jun Li 027; Jian Yang 003; Gang Niu 001; Masashi Sugiyama (NeurIPS, 2022)

Adapting to Online Label Shift with Provable Guarantees. - Yong Bai; Yu-Jie Zhang; Peng Zhao 006; Masashi Sugiyama; Zhi-Hua Zhou (NeurIPS, 2022)

Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses. - Yuzhou Cao; Tianchi Cai; Lei Feng 006; Lihong Gu; Jinjie Gu; Bo An 001; Gang Niu 001; Masashi Sugiyama (NeurIPS, 2022)

Synergy-of-Experts: Collaborate to Improve Adversarial Robustness. - Sen Cui; Jingfeng Zhang; Jian Liang 002; Bo Han 003; Masashi Sugiyama; Changshui Zhang (NeurIPS, 2022)

Adversarial Attacks and Defense for Non-Parametric Two-Sample Tests. - Xilie Xu; Jingfeng Zhang; Feng Liu 003; Masashi Sugiyama; Mohan S. Kankanhalli (CoRR, 2022)

Excess risk analysis for epistemic uncertainty with application to variational inference. - Futoshi Futami; Tomoharu Iwata; Naonori Ueda; Issei Sato; Masashi Sugiyama (CoRR, 2022)

Learning from Multiple Unlabeled Datasets with Partial Risk Regularization. - Yuting Tang; Nan Lu; Tianyi Zhang; Masashi Sugiyama (CoRR, 2022)

Equivariant Disentangled Transformation for Domain Generalization under Combination Shift. - Yivan Zhang; Jindong Wang 001; Xing Xie 001; Masashi Sugiyama (CoRR, 2022)

Audio Signal Enhancement with Learning from Positive and Unlabelled Data. - Nobutaka Ito; Masashi Sugiyama (CoRR, 2022)

Improving imbalanced classification using near-miss instances. - Akira Tanimoto; So Yamada; Takashi Takenouchi; Masashi Sugiyama; Hisashi Kashima (Expert Syst. Appl., 2022)

Learning from Noisy Complementary Labels with Robust Loss Functions. - Hiroki Ishiguro; Takashi Ishida 001; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2022)

Fast and Robust Rank Aggregation against Model Misspecification. - Yuangang Pan; Ivor W. Tsang; Weijie Chen 001; Gang Niu 001; Masashi Sugiyama (J. Mach. Learn. Res., 2022)

Learning from Noisy Pairwise Similarity and Unlabeled Data. - Songhua Wu; Tongliang Liu; Bo Han 003; Jun Yu 001; Gang Niu 001; Masashi Sugiyama (J. Mach. Learn. Res., 2022)

Neural Networks special issue on Artificial Intelligence and Brain Science. - Kenji Doya; Karl J. Friston; Masashi Sugiyama; Joshua B. Tenenbaum (Neural Networks, 2022)

Deep learning, reinforcement learning, and world models. - Yutaka Matsuo; Yann LeCun; Maneesh Sahani; Doina Precup; David Silver; Masashi Sugiyama; Eiji Uchibe; Jun Morimoto (Neural Networks, 2022)

Discovering diverse solutions in deep reinforcement learning by maximizing state-action-based mutual information. - Takayuki Osa; Voot Tangkaratt; Masashi Sugiyama (Neural Networks, 2022)

Centroid Estimation With Guaranteed Efficiency: A General Framework for Weakly Supervised Learning. - Chen Gong 002; Jian Yang 003; Jane You; Masashi Sugiyama (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

LocalDrop: A Hybrid Regularization for Deep Neural Networks. - Ziqing Lu; Chang Xu 002; Bo Du 001; Takashi Ishida 001; Lefei Zhang; Masashi Sugiyama (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

NoiLin: Improving adversarial training and correcting stereotype of noisy labels. - Jingfeng Zhang; Xilie Xu; Bo Han 003; Tongliang Liu; Lizhen Cui; Gang Niu 001; Masashi Sugiyama (Trans. Mach. Learn. Res., 2022)

Thompson Exploration with Best Challenger Rule in Best Arm Identification. - Jongyeong Lee; Junya Honda; Masashi Sugiyama (ACML, 2023)

Audio Signal Enhancement with Learning from Positive and Unlabeled Data. - Nobutaka Ito; Masashi Sugiyama (ICASSP, 2023)

Distribution Shift Matters for Knowledge Distillation with Webly Collected Images. - Jialiang Tang; Shuo Chen 003; Gang Niu 001; Masashi Sugiyama; Chen Gong 002 (ICCV, 2023)

Multi-Label Knowledge Distillation. - Penghui Yang 001; Ming-Kun Xie; Chen-Chen Zong; Lei Feng 006; Gang Niu 001; Masashi Sugiyama; Sheng-Jun Huang (ICCV, 2023)

Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification. - Takashi Ishida 001; Ikko Yamane; Nontawat Charoenphakdee; Gang Niu 001; Masashi Sugiyama (ICLR, 2023)

Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning. - Xin-Qiang Cai; Yao-Xiang Ding 001; Zi-Xuan Chen; Yuan Jiang 001; Masashi Sugiyama; Zhi-Hua Zhou (ICLR, 2023)

Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. - Ruijiang Dong; Feng Liu 003; Haoang Chi; Tongliang Liu; Mingming Gong; Gang Niu 001; Masashi Sugiyama; Bo Han 003 (ICML, 2023)

GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks. - Salah Ghamizi; Jingfeng Zhang; Maxime Cordy; Mike Papadakis; Masashi Sugiyama; Yves Le Traon (ICML, 2023)

Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits. - Jongyeong Lee; Junya Honda; Chao-Kai Chiang; Masashi Sugiyama (ICML, 2023)

A Category-theoretical Meta-analysis of Definitions of Disentanglement. - Yivan Zhang; Masashi Sugiyama (ICML, 2023)

Distributional Pareto-Optimal Multi-Objective Reinforcement Learning. - Xin-Qiang Cai; Pushi Zhang; Li Zhao 007; Jiang Bian 002; Masashi Sugiyama; Ashley Llorens (NeurIPS, 2023)

Imitation Learning from Vague Feedback. - Xin-Qiang Cai; Yu-Jie Zhang; Chao-Kai Chiang; Masashi Sugiyama (NeurIPS, 2023)

Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems. - Tongtong Fang; Nan Lu; Gang Niu 001; Masashi Sugiyama (NeurIPS, 2023)

Binary Classification with Confidence Difference. - Wei Wang 373; Lei Feng 006; Yuchen Jiang; Gang Niu 001; Min-Ling Zhang; Masashi Sugiyama (NeurIPS, 2023)

Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning. - Ming-Kun Xie; Jiahao Xiao; Hao-Zhe Liu; Gang Niu 001; Masashi Sugiyama; Sheng-Jun Huang (NeurIPS, 2023)

On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective. - Zeke Xie; Zhiqiang Xu; Jingzhao Zhang; Issei Sato; Masashi Sugiyama (NeurIPS, 2023)

Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization. - Xilie Xu; Jingfeng Zhang; Feng Liu 003; Masashi Sugiyama; Mohan S. Kankanhalli (NeurIPS, 2023)

Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection. - Xilie Xu; Jingfeng Zhang; Feng Liu 003; Masashi Sugiyama; Mohan S. Kankanhalli (NeurIPS, 2023)

Online (Multinomial) Logistic Bandit: Improved Regret and Constant Computation Cost. - Yu-Jie Zhang; Masashi Sugiyama (NeurIPS, 2023)

Adapting to Continuous Covariate Shift via Online Density Ratio Estimation. - Yu-Jie Zhang; Zhen-Yu Zhang; Peng Zhao; Masashi Sugiyama (NeurIPS, 2023)

Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation. - Jianing Zhu; Yu Geng; Jiangchao Yao; Tongliang Liu; Gang Niu 001; Masashi Sugiyama; Bo Han 003 (NeurIPS, 2023)

Adapting to Continuous Covariate Shift via Online Density Ratio Estimation. - Yu-Jie Zhang; Zhen-Yu Zhang; Peng Zhao 006; Masashi Sugiyama (CoRR, 2023)

Asymptotically Optimal Thompson Sampling Based Policy for the Uniform Bandits and the Gaussian Bandits. - Jongyeong Lee; Chao-Kai Chiang; Masashi Sugiyama (CoRR, 2023)

Fairness Improves Learning from Noisily Labeled Long-Tailed Data. - Jiaheng Wei; Zhaowei Zhu; Gang Niu 001; Tongliang Liu; Sijia Liu 001; Masashi Sugiyama; Yang Liu 018 (CoRR, 2023)

Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks. - Jingfeng Zhang; Bo Song; Bo Han 003; Lei Liu 003; Gang Niu 001; Masashi Sugiyama (CoRR, 2023)

Enhancing Label Sharing Efficiency in Complementary-Label Learning with Label Augmentation. - Wei-I Lin; Gang Niu 001; Hsuan-Tien Lin; Masashi Sugiyama (CoRR, 2023)

Analysis of Pleasantness Evoked by Various Airborne Ultrasound Tactile Stimuli Using Pairwise Comparisons and the Bradley-Terry Model. - Sora Satake; Yoshihiro Nagano; Masashi Sugiyama; Masahiro Fujiwara; Yasutoshi Makino; Hiroyuki Shinoda 001 (CoRR, 2023)

Enriching Disentanglement: Definitions to Metrics. - Yivan Zhang; Masashi Sugiyama (CoRR, 2023)

Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations. - Hao Chen 102; Ankit Shah 001; Jindong Wang 001; Ran Tao 013; Yidong Wang; Xing Xie 001; Masashi Sugiyama; Rita Singh; Bhiksha Raj (CoRR, 2023)

Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision. - Yuhao Wu; Xiaobo Xia; Jun Yu 001; Bo Han 003; Gang Niu 001; Masashi Sugiyama; Tongliang Liu (CoRR, 2023)

Combinatorial Pure Exploration of Multi-Armed Bandit with a Real Number Action Class. - Shintaro Nakamura; Masashi Sugiyama (CoRR, 2023)

Atom-Motif Contrastive Transformer for Molecular Property Prediction. - Wentao Yu; Shuo Chen 003; Chen Gong 002; Gang Niu 001; Masashi Sugiyama (CoRR, 2023)

Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation. - Jianing Zhu; Geng Yu; Jiangchao Yao; Tongliang Liu; Gang Niu 001; Masashi Sugiyama; Bo Han 003 (CoRR, 2023)

Learning with Complementary Labels Revisited: A Consistent Approach via Negative-Unlabeled Learning. - Wei Wang 373; Takashi Ishida 001; Yu-Jie Zhang; Gang Niu 001; Masashi Sugiyama (CoRR, 2023)

Universal Approximation Property of Invertible Neural Networks. - Isao Ishikawa; Takeshi Teshima; Koichi Tojo; Kenta Oono; Masahiro Ikeda; Masashi Sugiyama (J. Mach. Learn. Res., 2023)

Learning With Proper Partial Labels. - Zhenguo Wu; Jiaqi Lv; Masashi Sugiyama (Neural Comput., 2023)

Representation learning for continuous action spaces is beneficial for efficient policy learning. - Tingting Zhao 001; Ying Wang; Wei Sun; Yarui Chen; Gang Niu 001; Masashi Sugiyama (Neural Networks, 2023)

Root cause estimation of faults in production processes: a novel approach inspired by approximate Bayesian computation. - Yosuke Otsubo; Naoya Otani; Megumi Chikasue; Mineyuki Nishino; Masashi Sugiyama (Int. J. Prod. Res., 2023)

Positive-unlabeled classification under class-prior shift: a prior-invariant approach based on density ratio estimation. - Shota Nakajima; Masashi Sugiyama (Mach. Learn., 2023)

Class-Wise Denoising for Robust Learning Under Label Noise. - Chen Gong 002; Yongliang Ding; Bo Han 003; Gang Niu 001; Jian Yang 003; Jane You; Dacheng Tao; Masashi Sugiyama (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Learning Intention-Aware Policies in Deep Reinforcement Learning. - Tingting Zhao 001; S. Wu; G. Li; Y. Chen; Gang Niu 001; Masashi Sugiyama (Neural Comput., 2023)

Boundary-restricted metric learning. - Shuo Chen 003; Chen Gong 002; Xiang Li 041; Jian Yang 003; Gang Niu 001; Masashi Sugiyama (Mach. Learn., 2023)

The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models. - Jongyeong Lee; Chao-Kai Chiang; Masashi Sugiyama (AAAI, 2024)

Thompson Sampling for Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit. - Shintaro Nakamura; Masashi Sugiyama (AAAI, 2024)

VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates. - Guillaume Braun; Masashi Sugiyama (AISTATS, 2024)

Fixed-Budget Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit. - Shintaro Nakamura; Masashi Sugiyama (AISTATS, 2024)

Overcoming Continuous Distribution Shifts: Challenges in Online Machine Learning. - Masashi Sugiyama (BCI, 2024)

Direct Distillation Between Different Domains. - Jialiang Tang; Shuo Chen 003; Gang Niu 001; Hongyuan Zhu; Joey Tianyi Zhou; Chen Gong 002; Masashi Sugiyama (ECCV (80), 2024)

Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-supervised Multi-label Learning. - Jiahao Xiao; Ming-Kun Xie; Heng-Bo Fan; Gang Niu 001; Masashi Sugiyama; Sheng-Jun Huang (ECCV (52), 2024)

Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification. - Ming Li; Jike Zhong; Chenxin Li; Liuzhuozheng Li; Nie Lin; Masashi Sugiyama (EMNLP, 2024)

Robust Similarity Learning with Difference Alignment Regularization. - Shuo Chen 003; Gang Niu 001; Chen Gong 002; Okan Koc; Jian Yang 003; Masashi Sugiyama (ICLR, 2024)

Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks. - Hao Chen 102; Jindong Wang 001; Ankit Shah 001; Ran Tao 013; Hongxin Wei; Xing Xie 001; Masashi Sugiyama; Bhiksha Raj (ICLR, 2024)

Accurate Forgetting for Heterogeneous Federated Continual Learning. - Abudukelimu Wuerkaixi; Sen Cui; Jingfeng Zhang; Kunda Yan; Bo Han 003; Gang Niu 001; Lei Fang; Changshui Zhang; Masashi Sugiyama (ICLR, 2024)

A General Framework for Learning from Weak Supervision. - Hao Chen 102; Jindong Wang 001; Lei Feng 006; Xiang Li 106; Yidong Wang; Xing Xie 001; Masashi Sugiyama; Rita Singh; Bhiksha Raj (ICML, 2024)

Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization. - Ziqing Fan; Shengchao Hu; Jiangchao Yao; Gang Niu 001; Ya Zhang 002; Masashi Sugiyama; Yanfeng Wang 001 (ICML, 2024)

Efficient Non-stationary Online Learning by Wavelets with Applications to Online Distribution Shift Adaptation. - Yu-Yang Qian 001; Peng Zhao 006; Yu-Jie Zhang; Masashi Sugiyama; Zhi-Hua Zhou (ICML, 2024)

Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical. - Wei Wang 373; Takashi Ishida 001; Yu-Jie Zhang; Gang Niu 001; Masashi Sugiyama (ICML, 2024)

Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training. - Ming-Kun Xie; Jiahao Xiao; Pei Peng 005; Gang Niu 001; Masashi Sugiyama; Sheng-Jun Huang (ICML, 2024)

Balancing Similarity and Complementarity for Federated Learning. - Kunda Yan; Sen Cui; Abudukelimu Wuerkaixi; Jingfeng Zhang; Bo Han 003; Gang Niu 001; Masashi Sugiyama; Changshui Zhang (ICML, 2024)

Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought. - Zhen-Yu Zhang; Siwei Han; Huaxiu Yao; Gang Niu 001; Masashi Sugiyama (ICML, 2024)

An offline learning of behavior correction policy for vision-based robotic manipulation. - Qingxiuxiong Dong; Toshimitsu Kaneko; Masashi Sugiyama (ICRA, 2024)

Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations. - Hao Chen 102; Ankit Shah 001; Jindong Wang 001; Ran Tao 013; Yidong Wang; Xiang Li 106; Xing Xie 001; Masashi Sugiyama; Rita Singh; Bhiksha Raj (NeurIPS, 2024)

Slight Corruption in Pre-training Data Makes Better Diffusion Models. - Hao Chen 102; Yujin Han; Diganta Misra; Xiang Li 106; Kai Hu 010; Difan Zou; Masashi Sugiyama; Jindong Wang 001; Bhiksha Raj (NeurIPS, 2024)

What Makes Partial-Label Learning Algorithms Effective? - Jiaqi Lv; Yangfan Liu; Shiyu Xia; Ning Xu 009; Miao Xu 001; Gang Niu 001; Min-Ling Zhang; Masashi Sugiyama; Xin Geng 001 (NeurIPS, 2024)

Enriching Disentanglement: From Logical Definitions to Quantitative Metrics. - Yivan Zhang; Masashi Sugiyama (NeurIPS, 2024)

Test-time Adaptation in Non-stationary Environments via Adaptive Representation Alignment. - Zhen-Yu Zhang; Zhiyu Xie 001; Huaxiu Yao; Masashi Sugiyama (NeurIPS, 2024)

Offline Reinforcement Learning from Datasets with Structured Non-Stationarity. - Johannes Ackermann; Takayuki Osa; Masashi Sugiyama (RLJ, 2024)

Appearance-Based Curriculum for Semi-Supervised Learning with Multi-Angle Unlabeled Data. - Yuki Tanaka; Shuhei M. Yoshida; Takashi Shibata 001; Makoto Terao; Takayuki Okatani; Masashi Sugiyama (WACV, 2024)

Reinforcement Learning from Bagged Reward: A Transformer-based Approach for Instance-Level Reward Redistribution. - Yuting Tang; Xin-Qiang Cai; Yao-Xiang Ding 001; Qiyu Wu 001; Guoqing Liu; Masashi Sugiyama (CoRR, 2024)

Generating Chain-of-Thoughts with a Direct Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought. - Zhen-Yu Zhang; Siwei Han; Huaxiu Yao; Gang Niu 001; Masashi Sugiyama (CoRR, 2024)

Learning with Noisy Foundation Models. - Hao Chen 102; Jindong Wang 001; Zihan Wang; Ran Tao 013; Hongxin Wei; Xing Xie 001; Masashi Sugiyama; Bhiksha Raj (CoRR, 2024)

Reinforcement Learning with Options and State Representation. - Ayoub Ghriss; Masashi Sugiyama; Alessandro Lazaric (CoRR, 2024)

Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning across Two Domains. - Soichiro Nishimori; Xin-Qiang Cai; Johannes Ackermann; Masashi Sugiyama (CoRR, 2024)

Decoupling the Class Label and the Target Concept in Machine Unlearning. - Jianing Zhu; Bo Han 003; Jiangchao Yao; Jianliang Xu; Gang Niu 001; Masashi Sugiyama (CoRR, 2024)

Unlearning with Control: Assessing Real-world Utility for Large Language Model Unlearning. - Qizhou Wang; Bo Han 003; Puning Yang; Jianing Zhu; Tongliang Liu; Masashi Sugiyama (CoRR, 2024)

Adaptive complexity of log-concave sampling. - Huanjian Zhou; Baoxiang Wang 001; Masashi Sugiyama (CoRR, 2024)

On Unsupervised Prompt Learning for Classification with Black-box Language Models. - Zhen-Yu Zhang; Jiandong Zhang; Huaxiu Yao; Gang Niu 001; Masashi Sugiyama (CoRR, 2024)

Beyond Simple Sum of Delayed Rewards: Non-Markovian Reward Modeling for Reinforcement Learning. - Yuting Tang; Xin-Qiang Cai; Jing-Cheng Pang; Qiyu Wu 001; Yao-Xiang Ding 001; Masashi Sugiyama (CoRR, 2024)

Parallel simulation for sampling under isoperimetry and score-based diffusion models. - Huanjian Zhou; Masashi Sugiyama (CoRR, 2024)

Learning explainable task-relevant state representation for model-free deep reinforcement learning. - Tingting Zhao 001; Guixi Li; Tuo Zhao; Yarui Chen; Ning Xie 003; Gang Niu 001; Masashi Sugiyama (Neural Networks, 2024)

The Survival Bandit Problem. - Charles Riou; Junya Honda; Masashi Sugiyama (Trans. Mach. Learn. Res., 2024)

On the Robustness of Average Losses for Partial-Label Learning. - Jiaqi Lv; Biao Liu; Lei Feng 006; Ning Xu 009; Miao Xu 001; Bo An 001; Gang Niu 001; Xin Geng 001; Masashi Sugiyama (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise Learning. - Jingfeng Zhang; Bo Song; Haohan Wang; Bo Han 003; Tongliang Liu; Lei Liu 003; Masashi Sugiyama (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

On the Effectiveness of Adversarial Training Against Backdoor Attacks. - Yinghua Gao; Dongxian Wu; Jingfeng Zhang; Guanhao Gan; Shu-Tao Xia; Gang Niu 001; Masashi Sugiyama (IEEE Trans. Neural Networks Learn. Syst., 2024)

Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review. - Satoshi Takahashi; Yusuke Sakaguchi; Nobuji Kouno; Ken Takasawa; Kenichi Ishizu; Yu Akagi; Rina Aoyama; Naoki Teraya; Amina Bolatkan; Norio Shinkai; Hidenori Machino; Kazuma Kobayashi; et al. (J. Medical Syst., 2024)

Action-Agnostic Point-Level Supervision for Temporal Action Detection. - Shuhei M. Yoshida; Takashi Shibata 001; Makoto Terao; Takayuki Okatani; Masashi Sugiyama (AAAI, 2025)

Domain Adaptation and Entanglement: an Optimal Transport Perspective. - Okan Koc; Alexander Soen; Chao-Kai Chiang; Masashi Sugiyama (AISTATS, 2025)

Multi-Player Approaches for Dueling Bandits. - Or Raveh; Junya Honda; Masashi Sugiyama (AISTATS, 2025)

The Adaptive Complexity of Finding a Stationary Point. - Huanjian Zhou; Andi Han; Akiko Takeda; Masashi Sugiyama (COLT, 2025)

Sharpness-Aware Black-Box Optimization. - Feiyang Ye 001; Yueming Lyu; Xuehao Wang; Masashi Sugiyama; Yu Zhang 006; Ivor W. Tsang (ICLR, 2025)

Realistic Evaluation of Deep Partial-Label Learning Algorithms. - Wei Wang 373; Dong-Dong Wu; Jindong Wang 001; Gang Niu 001; Min-Ling Zhang; Masashi Sugiyama (ICLR, 2025)

Towards Out-of-Modal Generalization without Instance-level Modal Correspondence. - Zhuo Huang; Gang Niu 001; Bo Han 003; Masashi Sugiyama; Tongliang Liu (ICLR, 2025)

Learning View-invariant World Models for Visual Robotic Manipulation. - Jing-Cheng Pang; Nan Tang; Kaiyuan Li; Yuting Tang; Xin-Qiang Cai; Zhen-Yu Zhang; Gang Niu 001; Masashi Sugiyama; Yang Yu 001 (ICLR, 2025)

Towards Effective Evaluations and Comparisons for LLM Unlearning Methods. - Qizhou Wang; Bo Han 003; Puning Yang; Jianing Zhu; Tongliang Liu; Masashi Sugiyama (ICLR, 2025)

The adaptive complexity of parallelized log-concave sampling. - Huanjian Zhou; Baoxiang Wang 001; Masashi Sugiyama (ICLR, 2025)

A Fast Algorithm for the Real-Valued Combinatorial Pure Exploration of the Multi-Armed Bandit. - Shintaro Nakamura; Masashi Sugiyama (Neural Comput., 2025)

Unified Risk Analysis for Weakly Supervised Learning. - Chao-Kai Chiang; Masashi Sugiyama (Trans. Mach. Learn. Res., 2025)

Importance Weighting for Aligning Language Models under Deployment Distribution Shift. - Thanawat Lodkaew; Tongtong Fang; Takashi Ishida 001; Masashi Sugiyama (Trans. Mach. Learn. Res., 2025)

Reinforcement Learning from Bagged Reward. - Yuting Tang; Xin-Qiang Cai; Yao-Xiang Ding 001; Qiyu Wu 001; Guoqing Liu; Masashi Sugiyama (Trans. Mach. Learn. Res., 2025)

Estimating Per-Class Statistics for Label Noise Learning. - Wenshui Luo; Shuo Chen 003; Tongliang Liu; Bo Han 003; Gang Niu 001; Masashi Sugiyama; Dacheng Tao; Chen Gong 002 (IEEE Trans. Pattern Anal. Mach. Intell., 2025)

Weak-to-Strong Diffusion with Reflection. - Lichen Bai; Masashi Sugiyama; Zeke Xie (CoRR, 2025)

Label Distribution Learning with Biased Annotations by Learning Multi-Label Representation. - Zhiqiang Kou; Si Qin; Hailin Wang; Ming-Kun Xie; Shuo Chen 003; Yuheng Jia; Tongliang Liu; Masashi Sugiyama; Xin Geng 001 (CoRR, 2025)

Safety at Scale: A Comprehensive Survey of Large Model Safety. - Xingjun Ma; Yifeng Gao; Yixu Wang; Ruofan Wang; Xin Wang; Ye Sun; Yifan Ding; Hengyuan Xu; Yunhao Chen; Yunhan Zhao; Hanxun Huang; Yige Li; et al. (CoRR, 2025)

Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation. - Jie Xu 044; Na Zhao; Gang Niu 001; Masashi Sugiyama; Xiaofeng Zhu 001 (CoRR, 2025)

Practical estimation of the optimal classification error with soft labels and calibration. - Ryota Ushio; Takashi Ishida 001; Masashi Sugiyama (CoRR, 2025)

On Symmetric Losses for Robust Policy Optimization with Noisy Preferences. - Soichiro Nishimori; Yu-Jie Zhang; Thanawat Lodkaew; Masashi Sugiyama (CoRR, 2025)

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability. - Yu-Jie Zhang; Peng Zhao; Masashi Sugiyama (CoRR, 2025)

Recursive Reward Aggregation. - Yuting Tang; Yivan Zhang; Johannes Ackermann; Yu-Jie Zhang; Soichiro Nishimori; Masashi Sugiyama (CoRR, 2025)

Generalized Linear Bandits: Almost Optimal Regret with One-Pass Update. - Yu-Jie Zhang; Sheng-An Xu; Peng Zhao; Masashi Sugiyama (CoRR, 2025)

Off-Policy Corrected Reward Modeling for Reinforcement Learning from Human Feedback. - Johannes Ackermann; Takashi Ishida 001; Masashi Sugiyama (CoRR, 2025)

Impact of Noisy Supervision in Foundation Model Learning. - Hao Chen 102; Zihan Wang; Ran Tao 013; Hongxin Wei; Xing Xie 001; Masashi Sugiyama; Bhiksha Raj; Jindong Wang 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2025)

What Makes "Good" Distractors for Object Hallucination Evaluation in Large Vision-Language Models? - Ming-Kun Xie; Jiahao Xiao; Gang Niu 001; Lei Feng 006; Zhiqiang Kou; Min-Ling Zhang; Masashi Sugiyama (CoRR, 2025)