Learning to estimate user interest utilizing the variational Bayes estimator. - Taiji Suzuki; Takamasa Koshizen; Kazuyuki Aihara; Hiroshi Tsujino (ISDA, 2005)

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

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

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

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

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

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

Regularization Strategies and Empirical Bayesian Learning for MKL. - Ryota Tomioka; Taiji Suzuki (CoRR, 2010)

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

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

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

Unifying Framework for Fast Learning Rate of Non-Sparse Multiple Kernel Learning. - Taiji Suzuki (NIPS, 2011)

Statistical Performance of Convex Tensor Decomposition. - Ryota Tomioka; Taiji Suzuki; Kohei Hayashi; Hisashi Kashima (NIPS, 2011)

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

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

SpicyMKL: a fast algorithm for Multiple Kernel Learning with thousands of kernels. - Taiji Suzuki; Ryota Tomioka (Mach. Learn., 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)

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

PAC-Bayesian Bound for Gaussian Process Regression and Multiple Kernel Additive Model. - Taiji Suzuki (COLT, 2012)

A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems. - Takafumi Kanamori; Akiko Takeda; Taiji Suzuki (COLT, 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)

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

Dual Averaging and Proximal Gradient Descent for Online Alternating Direction Multiplier Method. - Taiji Suzuki (ICML (1), 2013)

Convex Tensor Decomposition via Structured Schatten Norm Regularization. - Ryota Tomioka; Taiji Suzuki (NIPS, 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)

Conjugate relation between loss functions and uncertainty sets in classification problems. - Takafumi Kanamori; Akiko Takeda; Taiji Suzuki (J. Mach. Learn. Res., 2013)

Improvement of multiple kernel learning using adaptively weighted regularization. - Taiji Suzuki (JSIAM Lett., 2013)

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

Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers. - Taiji Suzuki (ICML, 2014)

Convergence rate of Bayesian tensor estimator: Optimal rate without restricted strong convexity. - Taiji Suzuki (CoRR, 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)

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

A Consistent Method for Graph Based Anomaly Localization. - Satoshi Hara 001; Tetsuro Morimura; Toshihiro Takahashi; Hiroki Yanagisawa; Taiji Suzuki (AISTATS, 2015)

Convergence rate of Bayesian tensor estimator and its minimax optimality. - Taiji Suzuki (ICML, 2015)

Gaussian process nonparametric tensor estimator and its minimax optimality. - Heishiro Kanagawa; Taiji Suzuki; Hayato Kobayashi; Nobuyuki Shimizu; Yukihiro Tagami (ICML, 2016)

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

Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning. - Taiji Suzuki; Heishiro Kanagawa; Hayato Kobayashi; Nobuyuki Shimizu; Yukihiro Tagami (NIPS, 2016)

Stochastic dual averaging methods using variance reduction techniques for regularized empirical risk minimization problems. - Tomoya Murata; Taiji Suzuki (CoRR, 2016)

System identification and parameter estimation in mathematical medicine: examples demonstrated for prostate cancer. - Yoshito Hirata; Kai Morino; Taiji Suzuki; Qian Guo 002; Hiroshi Fukuhara; Kazuyuki Aihara (Quant. Biol., 2016)

Stochastic Difference of Convex Algorithm and its Application to Training Deep Boltzmann Machines. - Atsushi Nitanda; Taiji Suzuki (AISTATS, 2017)

Trimmed Density Ratio Estimation. - Song Liu 002; Akiko Takeda; Taiji Suzuki; Kenji Fukumizu (NIPS, 2017)

Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization. - Tomoya Murata; Taiji Suzuki (NIPS, 2017)

Fast learning rate of deep learning via a kernel perspective. - Taiji Suzuki (CoRR, 2017)

Stochastic Particle Gradient Descent for Infinite Ensembles. - Atsushi Nitanda; Taiji Suzuki (CoRR, 2017)

Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models. - Atsushi Nitanda; Taiji Suzuki (AISTATS, 2018)

Fast generalization error bound of deep learning from a kernel perspective. - Taiji Suzuki (AISTATS, 2018)

Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables. - Masaaki Takada; Taiji Suzuki; Hironori Fujisawa (AISTATS, 2018)

Short-term local weather forecast using dense weather station by deep neural network. - Kazuo Yonekura; Hitoshi Hattori; Taiji Suzuki (IEEE BigData, 2018)

Functional Gradient Boosting based on Residual Network Perception. - Atsushi Nitanda; Taiji Suzuki (ICML, 2018)

Adam Induces Implicit Weight Sparsity in Rectifier Neural Networks. - Atsushi Yaguchi; Taiji Suzuki; Wataru Asano; Shuhei Nitta; Yukinobu Sakata; Akiyuki Tanizawa (ICMLA, 2018)

Sample Efficient Stochastic Gradient Iterative Hard Thresholding Method for Stochastic Sparse Linear Regression with Limited Attribute Observation. - Tomoya Murata; Taiji Suzuki (NeurIPS, 2018)

Spectral-Pruning: Compressing deep neural network via spectral analysis. - Taiji Suzuki; Hiroshi Abe; Tomoya Murata; Shingo Horiuchi; Kotaro Ito; Tokuma Wachi; So Hirai; Masatoshi Yukishima; Tomoaki Nishimura (CoRR, 2018)

Generalized ridge estimator and model selection criteria in multivariate linear regression. - Yuichi Mori; Taiji Suzuki (J. Multivar. Anal., 2018)

Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors. - Atsushi Nitanda; Taiji Suzuki (AISTATS, 2019)

Cross-Domain Recommendation via Deep Domain Adaptation. - Heishiro Kanagawa; Hayato Kobayashi; Nobuyuki Shimizu; Yukihiro Tagami; Taiji Suzuki (ECIR (2), 2019)

Understanding the Effects of Pre-Training for Object Detectors via Eigenspectrum. - Yosuke Shinya; Edgar Simo-Serra; Taiji Suzuki (ICCV Workshops, 2019)

Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality. - Taiji Suzuki (ICLR (Poster), 2019)

Approximation and non-parametric estimation of ResNet-type convolutional neural networks. - Kenta Oono; Taiji Suzuki (ICML, 2019)

Refined Generalization Analysis of Gradient Descent for Over-parameterized Two-layer Neural Networks with Smooth Activations on Classification Problems. - Atsushi Nitanda; Taiji Suzuki (CoRR, 2019)

On Asymptotic Behaviors of Graph CNNs from Dynamical Systems Perspective. - Kenta Oono; Taiji Suzuki (CoRR, 2019)

Accelerated Sparsified SGD with Error Feedback. - Tomoya Murata; Taiji Suzuki (CoRR, 2019)

Gradient Noise Convolution (GNC): Smoothing Loss Function for Distributed Large-Batch SGD. - Kosuke Haruki; Taiji Suzuki; Yohei Hamakawa; Takeshi Toda; Ryuji Sakai; Masahiro Ozawa; Mitsuhiro Kimura (CoRR, 2019)

Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network. - Taiji Suzuki (CoRR, 2019)

Scalable Deep Neural Networks via Low-Rank Matrix Factorization. - Atsushi Yaguchi; Taiji Suzuki; Shuhei Nitta; Yukinobu Sakata; Akiyuki Tanizawa (CoRR, 2019)

Understanding Generalization in Deep Learning via Tensor Methods. - Jingling Li; Yanchao Sun; Jiahao Su; Taiji Suzuki; Furong Huang (AISTATS, 2020)

Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees. - Atsushi Nitanda; Taiji Suzuki (AISTATS, 2020)

Domain Adaptation Regularization for Spectral Pruning. - Laurent Dillard; Yosuke Shinya; Taiji Suzuki (BMVC, 2020)

Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint. - Jimmy Ba; Murat A. Erdogdu; Taiji Suzuki; Denny Wu; Tianzong Zhang (ICLR, 2020)

Graph Neural Networks Exponentially Lose Expressive Power for Node Classification. - Kenta Oono; Taiji Suzuki (ICLR, 2020)

Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network. - Taiji Suzuki; Hiroshi Abe; Tomoaki Nishimura (ICLR, 2020)

Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and its Generalization Error. - Taiji Suzuki; Hiroshi Abe; Tomoya Murata; Shingo Horiuchi; Kotaro Ito; Tokuma Wachi; So Hirai; Masatoshi Yukishima; Tomoaki Nishimura (IJCAI, 2020)

Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks. - Kenta Oono; Taiji Suzuki (NeurIPS, 2020)

Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics. - Taiji Suzuki (NeurIPS, 2020)

Meta Cyclical Annealing Schedule: A Simple Approach to Avoiding Meta-Amortization Error. - Yusuke Hayashi; Taiji Suzuki (CoRR, 2020)

Selective Inference for Latent Block Models. - Chihiro Watanabe; Taiji Suzuki (CoRR, 2020)

Neural Architecture Search Using Stable Rank of Convolutional Layers. - Kengo Machida; Kuniaki Uto; Koichi Shinoda; Taiji Suzuki (CoRR, 2020)

Estimation error analysis of deep learning on the regression problem on the variable exponent Besov space. - Kazuma Tsuji; Taiji Suzuki (CoRR, 2020)

Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis. - Atsushi Nitanda; Denny Wu; Taiji Suzuki (CoRR, 2020)

A reproducing kernel Hilbert space approach to high dimensional partially varying coefficient model. - Shaogao Lv; Zengyan Fan; Heng Lian; Taiji Suzuki; Kenji Fukumizu (Comput. Stat. Data Anal., 2020)

Independently Interpretable Lasso for Generalized Linear Models. - Masaaki Takada; Taiji Suzuki; Hironori Fujisawa (Neural Comput., 2020)

On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces. - Satoshi Hayakawa; Taiji Suzuki (Neural Networks, 2020)

Gradient Descent in RKHS with Importance Labeling. - Tomoya Murata; Taiji Suzuki (AISTATS, 2021)

Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features. - Shingo Yashima; Atsushi Nitanda; Taiji Suzuki (AISTATS, 2021)

When does preconditioning help or hurt generalization? - Shun-ichi Amari; Jimmy Ba; Roger Baker Grosse; Xuechen Li 005; Atsushi Nitanda; Taiji Suzuki; Denny Wu; Ji Xu (ICLR, 2021)

Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime. - Atsushi Nitanda; Taiji Suzuki (ICLR, 2021)

Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods. - Taiji Suzuki; Shunta Akiyama (ICLR, 2021)

On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting. - Shunta Akiyama; Taiji Suzuki (ICML, 2021)

Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning. - Tomoya Murata; Taiji Suzuki (ICML, 2021)

Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding. - Akira Nakagawa; Keizo Kato; Taiji Suzuki (ICML, 2021)

Decomposable-Net: Scalable Low-Rank Compression for Neural Networks. - Atsushi Yaguchi; Taiji Suzuki; Shuhei Nitta; Yukinobu Sakata; Akiyuki Tanizawa (IJCAI, 2021)

Differentiable Multiple Shooting Layers. - Stefano Massaroli; Michael Poli; Sho Sonoda; Taiji Suzuki; Jinkyoo Park; Atsushi Yamashita; Hajime Asama (NeurIPS, 2021)

Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis. - Atsushi Nitanda; Denny Wu; Taiji Suzuki (NeurIPS, 2021)

Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space. - Taiji Suzuki; Atsushi Nitanda (NeurIPS, 2021)

AutoLL: Automatic Linear Layout of Graphs based on Deep Neural Network. - Chihiro Watanabe; Taiji Suzuki (SSCI, 2021)

Goodness-of-fit Test on the Number of Biclusters in Relational Data Matrix. - Chihiro Watanabe; Taiji Suzuki (CoRR, 2021)

Adaptive and Interpretable Graph Convolution Networks Using Generalized Pagerank. - Kishan Wimalawarne; Taiji Suzuki (CoRR, 2021)

A Scaling Law for Synthetic-to-Real Transfer: A Measure of Pre-Training. - Hiroaki Mikami; Kenji Fukumizu; Shogo Murai; Shuji Suzuki; Yuta Kikuchi; Taiji Suzuki; Shin-ichi Maeda; Kohei Hayashi (CoRR, 2021)

Neural Network Module Decomposition and Recomposition. - Hiroaki Kingetsu; Kenichi Kobayashi 001; Taiji Suzuki (CoRR, 2021)

Goodness-of-fit test for latent block models. - Chihiro Watanabe; Taiji Suzuki (Comput. Stat. Data Anal., 2021)

Sharp characterization of optimal minibatch size for stochastic finite sum convex optimization. - Atsushi Nitanda; Tomoya Murata; Taiji Suzuki (Knowl. Inf. Syst., 2021)

Layer-wise Adaptive Graph Convolution Networks Using Generalized Pagerank. - Kishan Wimalawarne; Taiji Suzuki (ACML, 2022)

Convex Analysis of the Mean Field Langevin Dynamics. - Atsushi Nitanda; Denny Wu; Taiji Suzuki (AISTATS, 2022)

Dimension-free convergence rates for gradient Langevin dynamics in RKHS. - Boris Muzellec; Kanji Sato; Mathurin Massias; Taiji Suzuki (COLT, 2022)

Understanding the Variance Collapse of SVGD in High Dimensions. - Jimmy Ba; Murat A. Erdogdu; Marzyeh Ghassemi; Shengyang Sun; Taiji Suzuki; Denny Wu; Tianzong Zhang (ICLR, 2022)

Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization. - Kazusato Oko; Taiji Suzuki; Atsushi Nitanda; Denny Wu (ICLR, 2022)

Learnability of convolutional neural networks for infinite dimensional input via mixed and anisotropic smoothness. - Sho Okumoto; Taiji Suzuki (ICLR, 2022)

Data-Parallel Momentum Diagonal Empirical Fisher (DP-MDEF):Adaptive Gradient Method is Affected by Hessian Approximation and Multi-Class Data. - Chenyuan Xu; Kosuke Haruki; Taiji Suzuki; Masahiro Ozawa; Kazuki Uematsu; Ryuji Sakai (ICMLA, 2022)

MSR-DARTS: Minimum Stable Rank of Differentiable Architecture Search. - Kengo Machida; Kuniaki Uto; Koichi Shinoda; Taiji Suzuki (IJCNN, 2022)

High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation. - Jimmy Ba; Murat A. Erdogdu; Taiji Suzuki; Zhichao Wang; Denny Wu; Greg Yang (NeurIPS, 2022)

Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization. - Yuri Kinoshita; Taiji Suzuki (NeurIPS, 2022)

Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning. - Tomoya Murata; Taiji Suzuki (NeurIPS, 2022)

Two-layer neural network on infinite dimensional data: global optimization guarantee in the mean-field regime. - Naoki Nishikawa; Taiji Suzuki; Atsushi Nitanda; Denny Wu (NeurIPS, 2022)

A Scaling Law for Syn2real Transfer: How Much Is Your Pre-training Effective? - Hiroaki Mikami; Kenji Fukumizu; Shogo Murai; Shuji Suzuki; Yuta Kikuchi; Taiji Suzuki; Shin-ichi Maeda; Kohei Hayashi (ECML/PKDD (3), 2022)

Versatile Single-Loop Method for Gradient Estimator: First and Second Order Optimality, and its Application to Federated Learning. - Kazusato Oko; Shunta Akiyama; Tomoya Murata; Taiji Suzuki (CoRR, 2022)

Graph Polynomial Convolution Models for Node Classification of Non-Homophilous Graphs. - Kishan Wimalawarne; Taiji Suzuki (CoRR, 2022)

Deep two-way matrix reordering for relational data analysis. - Chihiro Watanabe; Taiji Suzuki (Neural Networks, 2022)

Excess Risk of Two-Layer ReLU Neural Networks in Teacher-Student Settings and its Superiority to Kernel Methods. - Shunta Akiyama; Taiji Suzuki (ICLR, 2023)

Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics. - Taiji Suzuki; Atsushi Nitanda; Denny Wu (ICLR, 2023)

DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning. - Tomoya Murata; Taiji Suzuki (ICML, 2023)

Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems. - Atsushi Nitanda; Kazusato Oko; Denny Wu; Nobuhito Takenouchi; Taiji Suzuki (ICML, 2023)

Diffusion Models are Minimax Optimal Distribution Estimators. - Kazusato Oko; Shunta Akiyama; Taiji Suzuki (ICML, 2023)

Tight and fast generalization error bound of graph embedding in metric space. - Atsushi Suzuki; Atsushi Nitanda; Taiji Suzuki; Jing Wang 023; Feng Tian 006; Kenji Yamanishi (ICML, 2023)

Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input. - Shokichi Takakura; Taiji Suzuki (ICML, 2023)

Scalable Federated Learning for Clients with Different Input Image Sizes and Numbers of Output Categories. - Shuhei Nitta; Taiji Suzuki; Albert Rodríguez Mulet; Atsushi Yaguchi; Ryusuke Hirai (ICMLA, 2023)

Neural Network Module Decomposition and Recomposition with Superimposed Masks. - Hiroaki Kingetsu; Kenichi Kobayashi 001; Taiji Suzuki (IJCNN, 2023)

Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective. - Jimmy Ba; Murat A. Erdogdu; Taiji Suzuki; Zhichao Wang; Denny Wu (NeurIPS, 2023)

Gradient-Based Feature Learning under Structured Data. - Alireza Mousavi Hosseini; Denny Wu; Taiji Suzuki; Murat A. Erdogdu (NeurIPS, 2023)

Mean-field Langevin dynamics: Time-space discretization, stochastic gradient, and variance reduction. - Taiji Suzuki; Denny Wu; Atsushi Nitanda (NeurIPS, 2023)

Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond. - Taiji Suzuki; Denny Wu; Kazusato Oko; Atsushi Nitanda (NeurIPS, 2023)

Koopman-Based Bound for Generalization: New Aspect of Neural Networks Regarding Nonlinear Noise Filtering. - Yuka Hashimoto; Sho Sonoda; Isao Ishikawa; Atsushi Nitanda; Taiji Suzuki (CoRR, 2023)

Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reduction. - Taiji Suzuki; Denny Wu; Atsushi Nitanda (CoRR, 2023)

Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective. - Wei Huang 034; Yuan Cao 006; Haonan Wang; Xin Cao 001; Taiji Suzuki (CoRR, 2023)

Learning Green's Function Efficiently Using Low-Rank Approximations. - Kishan Wimalawarne; Taiji Suzuki; Sophie Langer (CoRR, 2023)

Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations. - Kazusato Oko; Yujin Song; Taiji Suzuki; Denny Wu (COLT, 2024)

Koopman-based generalization bound: New aspect for full-rank weights. - Yuka Hashimoto; Sho Sonoda; Isao Ishikawa; Atsushi Nitanda; Taiji Suzuki (ICLR, 2024)

Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory. - Wei Huang 034; Ye Shi 001; Zhongyi Cai; Taiji Suzuki (ICLR, 2024)

Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems. - Juno Kim; Kakei Yamamoto; Kazusato Oko; Zhuoran Yang; Taiji Suzuki (ICLR, 2024)

Minimax optimality of convolutional neural networks for infinite dimensional input-output problems and separation from kernel methods. - Yuto Nishimura; Taiji Suzuki (ICLR, 2024)

Improved statistical and computational complexity of the mean-field Langevin dynamics under structured data. - Atsushi Nitanda; Kazusato Oko; Taiji Suzuki; Denny Wu (ICLR, 2024)

Optimal criterion for feature learning of two-layer linear neural network in high dimensional interpolation regime. - Keita Suzuki; Taiji Suzuki (ICLR, 2024)

Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples. - Dake Bu; Wei Huang 034; Taiji Suzuki; Ji Cheng 001; Qingfu Zhang 001; Zhiqiang Xu 003; Hau-San Wong (ICML, 2024)

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization. - Yihang Chen; Fanghui Liu 001; Taiji Suzuki; Volkan Cevher (ICML, 2024)

Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape. - Juno Kim; Taiji Suzuki (ICML, 2024)

SILVER: Single-loop variance reduction and application to federated learning. - Kazusato Oko; Shunta Akiyama; Denny Wu; Tomoya Murata; Taiji Suzuki (ICML, 2024)

State-Free Inference of State-Space Models: The *Transfer Function* Approach. - Rom N. Parnichkun; Stefano Massaroli; Alessandro Moro; Jimmy T. H. Smith; Ramin M. Hasani; Mathias Lechner; Qi An; Christopher Ré; Hajime Asama; Stefano Ermon; Taiji Suzuki; Michael Poli; et al. (ICML, 2024)

Mechanistic Design and Scaling of Hybrid Architectures. - Michael Poli; Armin W. Thomas; Eric Nguyen; Pragaash Ponnusamy; Björn Deiseroth; Kristian Kersting; Taiji Suzuki; Brian L. Hie; Stefano Ermon; Christopher Ré; Ce Zhang 001; Stefano Massaroli (ICML, 2024)

How do Transformers Perform In-Context Autoregressive Learning ? - Michael Eli Sander; Raja Giryes; Taiji Suzuki; Mathieu Blondel; Gabriel Peyré (ICML, 2024)

Mean-field Analysis on Two-layer Neural Networks from a Kernel Perspective. - Shokichi Takakura; Taiji Suzuki (ICML, 2024)

Mean Field Langevin Actor-Critic: Faster Convergence and Global Optimality beyond Lazy Learning. - Kakei Yamamoto; Kazusato Oko; Zhuoran Yang; Taiji Suzuki (ICML, 2024)

Graph Polynomial Convolution Models for Node Classification of Non-Homophilous Graphs. - Kishan Wimalawarne; Taro Sawaki; Motokiyo Hirayama; Takanobu Kawahara; Taiji Suzuki (ICMLA, 2024)

Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning. - Dake Bu; Wei Huang 034; Andi Han; Atsushi Nitanda; Taiji Suzuki; Qingfu Zhang 001; Hau-San Wong (NeurIPS, 2024)

On the Comparison between Multi-modal and Single-modal Contrastive Learning. - Wei Huang 034; Andi Han; Yongqiang Chen; Yuan Cao 006; Zhiqiang Xu 003; Taiji Suzuki (NeurIPS, 2024)

Unveil Benign Overfitting for Transformer in Vision: Training Dynamics, Convergence, and Generalization. - Jiarui Jiang; Wei Huang 034; Miao Zhang; Taiji Suzuki; Liqiang Nie (NeurIPS, 2024)

Transformers are Minimax Optimal Nonparametric In-Context Learners. - Juno Kim; Tai Nakamaki; Taiji Suzuki (NeurIPS, 2024)

Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit. - Jason D. Lee; Kazusato Oko; Taiji Suzuki; Denny Wu (NeurIPS, 2024)

Pretrained Transformer Efficiently Learns Low-Dimensional Target Functions In-Context. - Kazusato Oko; Yujin Song; Taiji Suzuki; Denny Wu (NeurIPS, 2024)

Dimensionality-Induced Information Loss of Outliers in Deep Neural Networks. - Kazuki Uematsu; Kosuke Haruki; Taiji Suzuki; Mitsuhiro Kimura; Takahiro Takimoto; Hideyuki Nakagawa (ECML/PKDD (1), 2024)

Understanding Multimodal Contrastive Learning Through Pointwise Mutual Information. - Toshimitsu Uesaka; Taiji Suzuki; Yuhta Takida; Chieh-Hsin Lai; Naoki Murata; Yuki Mitsufuji (CoRR, 2024)

State Space Models are Comparable to Transformers in Estimating Functions with Dynamic Smoothness. - Naoki Nishikawa; Taiji Suzuki (CoRR, 2024)

Flow matching achieves minimax optimal convergence. - Kenji Fukumizu; Taiji Suzuki; Noboru Isobe; Kazusato Oko; Masanori Koyama (CoRR, 2024)

Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons. - Wei Huang 034; Yuan Cao 006; Haonan Wang; Xin Cao 001; Taiji Suzuki (AISTATS, 2025)

Clustered Invariant Risk Minimization. - Tomoya Murata; Atsushi Nitanda; Taiji Suzuki (AISTATS, 2025)

Exact and Rich Feature Learning Dynamics of Two-Layer Linear Networks. - Wei Huang 034; Wuyang Chen 001; Zhiqiang Xu 003; Zhangyang Wang; Taiji Suzuki (CPAL, 2025)

Flow matching achieves almost minimax optimal convergence. - Kenji Fukumizu; Taiji Suzuki; Noboru Isobe; Kazusato Oko; Masanori Koyama (ICLR, 2025)

Direct Distributional Optimization for Provable Alignment of Diffusion Models. - Ryotaro Kawata; Kazusato Oko; Atsushi Nitanda; Taiji Suzuki (ICLR, 2025)

Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression. - Juno Kim; Dimitri Meunier; Arthur Gretton; Taiji Suzuki; Zhu Li (ICLR, 2025)

Transformers Provably Solve Parity Efficiently with Chain of Thought. - Juno Kim; Taiji Suzuki (ICLR, 2025)

On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent. - Bingrui Li; Wei Huang 034; Andi Han; Zhanpeng Zhou; Taiji Suzuki; Jun Zhu; Jianfei Chen (ICLR, 2025)

State Space Models are Provably Comparable to Transformers in Dynamic Token Selection. - Naoki Nishikawa; Taiji Suzuki (ICLR, 2025)

Weighted Point Set Embedding for Multimodal Contrastive Learning Toward Optimal Similarity Metric. - Toshimitsu Uesaka; Taiji Suzuki; Yuhta Takida; Chieh-Hsin Lai; Naoki Murata; Yuki Mitsufuji (ICLR, 2025)

Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation. - Juno Kim; Denny Wu; Jason Lee; Taiji Suzuki (CoRR, 2025)

Propagation of Chaos for Mean-Field Langevin Dynamics and its Application to Model Ensemble. - Atsushi Nitanda; Anzelle Lee; Damian Tan Xing Kai; Mizuki Sakaguchi; Taiji Suzuki (CoRR, 2025)

When Does Metadata Conditioning (NOT) Work for Language Model Pre-Training? A Study with Context-Free Grammars. - Rei Higuchi; Ryotaro Kawata; Naoki Nishikawa; Kazusato Oko; Shoichiro Yamaguchi; Sosuke Kobayashi; Seiya Tokui; Kohei Hayashi; Daisuke Okanohara; Taiji Suzuki (CoRR, 2025)

Quantifying Memory Utilization with Effective State-Size. - Rom N. Parnichkun; Neehal Tumma; Armin W. Thomas; Alessandro Moro; Qi An; Taiji Suzuki; Atsushi Yamashita; Michael Poli; Stefano Massaroli (CoRR, 2025)

Direct Density Ratio Optimization: A Statistically Consistent Approach to Aligning Large Language Models. - Rei Higuchi; Taiji Suzuki (CoRR, 2025)

On the Role of Label Noise in the Feature Learning Process. - Andi Han; Wei Huang 034; Zhanpeng Zhou; Gang Niu 001; Wuyang Chen 001; Junchi Yan; Akiko Takeda; Taiji Suzuki (CoRR, 2025)

Mixture of Experts Provably Detect and Learn the Latent Cluster Structure in Gradient-Based Learning. - Ryotaro Kawata; Kohsei Matsutani; Yuri Kinoshita; Naoki Nishikawa; Taiji Suzuki (CoRR, 2025)

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel. - Yilan Chen 002; Zhichao Wang; Wei Huang 034; Andi Han; Taiji Suzuki; Arya Mazumdar (CoRR, 2025)

Degrees of Freedom for Linear Attention: Distilling Softmax Attention with Optimal Feature Efficiency. - Naoki Nishikawa; Rei Higuchi; Taiji Suzuki (CoRR, 2025)

Provable In-Context Vector Arithmetic via Retrieving Task Concepts. - Dake Bu; Wei Huang 034; Andi Han; Atsushi Nitanda; Qingfu Zhang 001; Hau-San Wong; Taiji Suzuki (CoRR, 2025)