Active Learning in Multilayer Perceptrons. - Kenji Fukumizu (NIPS, 1995)

Probabilistic design of layered neural networks based on their unified framework. - Sumio Watanabe; Kenji Fukumizu (IEEE Trans. Neural Networks, 1995)

A Regularity Condition of the Information Matrix of a Multilayer Perceptron Network. - Kenji Fukumizu (Neural Networks, 1996)

A network of chaotic elements for information processing. - Shin Ishii; Kenji Fukumizu; Sumio Watanabe (Neural Networks, 1996)

Probabilistic design. - Sumio Watanabe; Kenji Fukumizu (Algorithms and Architectures, 1998)

Effect of Batch Learning in Multilayer Neural Networks. - Kenji Fukumizu (ICONIP, 1998)

Generalization Error of Limear Neural Networks in Unidentifiable Cases. - Kenji Fukumizu (ALT, 1999)

An Efficient Learning Algorithm Using Naturla Gradient and Second Order Information of Error Surface. - Hyeyoung Park; Kenji Fukumizu; Shun-ichi Amari; Yillbyung Lee (PRICAI, 2000)

Adaptive Method of Realizing Natural Gradient Learning for Multilayer Perceptrons. - Shun-ichi Amari; Hyeyoung Park; Kenji Fukumizu (Neural Comput., 2000)

Local minima and plateaus in hierarchical structures of multilayer perceptrons. - Kenji Fukumizu; Shun-ichi Amari (Neural Networks, 2000)

Adaptive natural gradient learning algorithms for various stochastic models. - Hyeyoung Park; Shun-ichi Amari; Kenji Fukumizu (Neural Networks, 2000)

Statistical active learning in multilayer perceptrons. - Kenji Fukumizu (IEEE Trans. Neural Networks Learn. Syst., 2000)

Critical Lines in Symmetry of Mixture Models and its Application to Component Splitting. - Kenji Fukumizu; Shotaro Akaho; Shun-ichi Amari (NIPS, 2002)

Kernel Dimensionality Reduction for Supervised Learning. - Kenji Fukumizu; Francis R. Bach; Michael I. Jordan (NIPS, 2003)

Over-fitting behavior of Gaussian unit under Gaussian noise. - Katsuyuko Hagiwara; Kenji Fukumizu (IJCNN, 2004)

Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces. - Kenji Fukumizu; Francis R. Bach; Michael I. Jordan (J. Mach. Learn. Res., 2004)

Statistical Convergence of Kernel CCA. - Kenji Fukumizu; Francis R. Bach; Arthur Gretton (NIPS, 2005)

Multiresolution Kernels - Marco Cuturi; Kenji Fukumizu (CoRR, 2005)

Semigroup Kernels on Measures. - Marco Cuturi; Kenji Fukumizu; Jean-Philippe Vert (J. Mach. Learn. Res., 2005)

Kernels on Structured Objects Through Nested Histograms. - Marco Cuturi; Kenji Fukumizu (NIPS, 2006)

Active Learning for Network Estimation. - Shotaro Akaho; Kenji Fukumizu (CIBCB, 2007)

A kernel-based causal learning algorithm. - Xiaohai Sun; Dominik Janzing; Bernhard Schölkopf; Kenji Fukumizu (ICML, 2007)

Kernel Measures of Conditional Dependence. - Kenji Fukumizu; Arthur Gretton; Xiaohai Sun; Bernhard Schölkopf (NIPS, 2007)

A Kernel Statistical Test of Independence. - Arthur Gretton; Kenji Fukumizu; Choon Hui Teo; Le Song; Bernhard Schölkopf; Alexander J. Smola (NIPS, 2007)

Parameter estimation for von Mises-Fisher distributions. - Akihiro Tanabe; Kenji Fukumizu; Shigeyuki Oba; Takashi Takenouchi; Shin Ishii (Comput. Stat., 2007)

Statistical Consistency of Kernel Canonical Correlation Analysis. - Kenji Fukumizu; Francis R. Bach; Arthur Gretton (J. Mach. Learn. Res., 2007)

Injective Hilbert Space Embeddings of Probability Measures. - Bharath K. Sriperumbudur; Arthur Gretton; Kenji Fukumizu; Gert R. G. Lanckriet; Bernhard Schölkopf (COLT, 2008)

Characteristic Kernels on Groups and Semigroups. - Kenji Fukumizu; Bharath K. Sriperumbudur; Arthur Gretton; Bernhard Schölkopf (NIPS, 2008)

Relation between weight size and degree of over-fitting in neural network regression. - Katsuyuki Hagiwara; Kenji Fukumizu (Neural Networks, 2008)

Hilbert space embeddings of conditional distributions with applications to dynamical systems. - Le Song; Jonathan Huang; Alexander J. Smola; Kenji Fukumizu (ICML, 2009)

A Fast, Consistent Kernel Two-Sample Test. - Arthur Gretton; Kenji Fukumizu; Zaïd Harchaoui; Bharath K. Sriperumbudur (NIPS, 2009)

Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions. - Bharath K. Sriperumbudur; Kenji Fukumizu; Arthur Gretton; Gert R. G. Lanckriet; Bernhard Schölkopf (NIPS, 2009)

Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation. - Yusuke Watanabe; Kenji Fukumizu (NIPS, 2009)

A note on integral probability metrics and $ phi$-divergences - Bharath K. Sriperumbudur; Arthur Gretton; Kenji Fukumizu; Gert R. G. Lanckriet; Bernhard Schölkopf (CoRR, 2009)

Graph polynomials and approximation of partition functions with Loopy Belief Propagation - Yusuke Watanabe; Kenji Fukumizu (CoRR, 2009)

Non-parametric estimation of integral probability metrics. - Bharath K. Sriperumbudur; Kenji Fukumizu; Arthur Gretton; Bernhard Schölkopf; Gert R. G. Lanckriet (ISIT, 2010)

On the relation between universality, characteristic kernels and RKHS embedding of measures. - Bharath K. Sriperumbudur; Kenji Fukumizu; Gert R. G. Lanckriet (AISTATS, 2010)

Hilbert Space Embeddings and Metrics on Probability Measures. - Bharath K. Sriperumbudur; Arthur Gretton; Kenji Fukumizu; Bernhard Schölkopf; Gert R. G. Lanckriet (J. Mach. Learn. Res., 2010)

A Comparative Study of Kernel and Robust Canonical Correlation Analysis. - Md. Ashad Alam; Mohammed Nasser; Kenji Fukumizu (J. Multim., 2010)

Kernel Bayes' Rule. - Kenji Fukumizu; Le Song; Arthur Gretton (NIPS, 2011)

Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint. - Bharath K. Sriperumbudur; Kenji Fukumizu; Gert R. G. Lanckriet (NIPS, 2011)

Loopy Belief Propagation, Bethe Free Energy and Graph Zeta Function - Yusuke Watanabe; Kenji Fukumizu (CoRR, 2011)

Gradient-based kernel dimension reduction for supervised learning - Kenji Fukumizu; Chenlei Leng (CoRR, 2011)

New Graph Polynomials from the Bethe Approximation of the Ising Partition Function. - Yusuke Watanabe; Kenji Fukumizu (Comb. Probab. Comput., 2011)

Statistical approaches to combining binary classifiers for multi-class classification. - Yuichi Shiraishi; Kenji Fukumizu (Neurocomputing, 2011)

Learning low-rank output kernels. - Francesco Dinuzzo; Kenji Fukumizu (ACML, 2011)

Universality, Characteristic Kernels and RKHS Embedding of Measures. - Bharath K. Sriperumbudur; Kenji Fukumizu; Gert R. G. Lanckriet (J. Mach. Learn. Res., 2011)

Hypothesis testing using pairwise distances and associated kernels. - Dino Sejdinovic; Arthur Gretton; Bharath K. Sriperumbudur; Kenji Fukumizu (ICML, 2012)

Gradient-based kernel method for feature extraction and variable selection. - Kenji Fukumizu; Chenlei Leng (NIPS, 2012)

Optimal kernel choice for large-scale two-sample tests. - Arthur Gretton; Bharath K. Sriperumbudur; Dino Sejdinovic; Heiko Strathmann; Sivaraman Balakrishnan; Massimiliano Pontil; Kenji Fukumizu (NIPS, 2012)

Learning from Distributions via Support Measure Machines. - Krikamol Muandet; Kenji Fukumizu; Francesco Dinuzzo; Bernhard Schölkopf (NIPS, 2012)

Hilbert Space Embeddings of POMDPs. - Yu Nishiyama; Abdeslam Boularias; Arthur Gretton; Kenji Fukumizu (UAI, 2012)

Hypothesis testing using pairwise distances and associated kernels (with Appendix) - Dino Sejdinovic; Arthur Gretton; Bharath K. Sriperumbudur; Kenji Fukumizu (CoRR, 2012)

Equivalence of distance-based and RKHS-based statistics in hypothesis testing - Dino Sejdinovic; Bharath K. Sriperumbudur; Arthur Gretton; Kenji Fukumizu (CoRR, 2012)

Centering Similarity Measures to Reduce Hubs. - Ikumi Suzuki; Kazuo Hara; Masashi Shimbo; Marco Saerens; Kenji Fukumizu (EMNLP, 2013)

Higher-Order Regularized Kernel CCA. - Md. Ashad Alam; Kenji Fukumizu (ICMLA (1), 2013)

Kernel Mean Estimation and Stein's Effect. - Krikamol Muandet; Kenji Fukumizu; Bharath K. Sriperumbudur; Arthur Gretton; Bernhard Schölkopf (CoRR, 2013)

Kernel Bayes' rule: Bayesian inference with positive definite kernels. - Kenji Fukumizu; Le Song; Arthur Gretton (J. Mach. Learn. Res., 2013)

Special Issue on Advances in Kernel-Based Learning for Signal Processing [From the Guest Editors]. - Klaus-Robert Müller; Tülay Adali; Kenji Fukumizu; José C. Príncipe; Sergios Theodoridis (IEEE Signal Process. Mag., 2013)

Kernel Embeddings of Conditional Distributions: A Unified Kernel Framework for Nonparametric Inference in Graphical Models. - Le Song; Kenji Fukumizu; Arthur Gretton (IEEE Signal Process. Mag., 2013)

Monte Carlo Filtering Using Kernel Embedding of Distributions. - Motonobu Kanagawa; Yu Nishiyama; Arthur Gretton; Kenji Fukumizu (AAAI, 2014)

Recovering Distributions from Gaussian RKHS Embeddings. - Motonobu Kanagawa; Kenji Fukumizu (AISTATS, 2014)

Kernel Mean Estimation and Stein Effect. - Krikamol Muandet; Kenji Fukumizu; Bharath K. Sriperumbudur; Arthur Gretton; Bernhard Schölkopf (ICML, 2014)

Hyperparameter Selection in Kernel Principal Component Analysis. - Md. Ashad Alam; Kenji Fukumizu (J. Comput. Sci., 2014)

Deep Learning: Theory, Algorithms, and Applications (NII Shonan Meeting 2014-5). - Pierre Baldi; Kenji Fukumizu; Tomaso A. Poggio (NII Shonan Meet. Rep., 2014)

Localized Centering: Reducing Hubness in Large-Sample Data. - Kazuo Hara; Ikumi Suzuki; Masashi Shimbo; Kei Kobayashi; Kenji Fukumizu; Milos Radovanovic 001 (AAAI, 2015)

Reducing Hubness: A Cause of Vulnerability in Recommender Systems. - Kazuo Hara; Ikumi Suzuki; Kei Kobayashi; Kenji Fukumizu (SIGIR, 2015)

Reducing Hubness for Kernel Regression. - Kazuo Hara; Ikumi Suzuki; Kei Kobayashi; Kenji Fukumizu; Milos Radovanovic 001 (SISAP, 2015)

Kernel-Based Information Criterion. - Somayeh Danafar; Kenji Fukumizu; Faustino Gomez (Comput. Inf. Sci., 2015)

Lazy Transfer Learning. - Song Liu 002; Kenji Fukumizu (CoRR, 2015)

Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations. - Bernhard Schölkopf; Krikamol Muandet; Kenji Fukumizu; Jonas Peters (CoRR, 2015)

Higher-Order Regularized Kernel Canonical Correlation Analysis. - Md. Ashad Alam; Kenji Fukumizu (Int. J. Pattern Recognit. Artif. Intell., 2015)

Computing functions of random variables via reproducing kernel Hilbert space representations. - Bernhard Schölkopf; Krikamol Muandet; Kenji Fukumizu; Stefan Harmeling; Jonas Peters (Stat. Comput., 2015)

Flattening the Density Gradient for Eliminating Spatial Centrality to Reduce Hubness. - Kazuo Hara; Ikumi Suzuki; Kei Kobayashi; Kenji Fukumizu; Milos Radovanovic 001 (AAAI, 2016)

Persistence weighted Gaussian kernel for topological data analysis. - Genki Kusano; Yasuaki Hiraoka; Kenji Fukumizu (ICML, 2016)

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

Convergence guarantees for kernel-based quadrature rules in misspecified settings. - Motonobu Kanagawa; Bharath K. Sriperumbudur; Kenji Fukumizu (NIPS, 2016)

Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective. - Song Liu 002; Kenji Fukumizu (SDM, 2016)

Kernel Mean Embedding of Distributions: A Review and Beyonds. - Krikamol Muandet; Kenji Fukumizu; Bharath K. Sriperumbudur; Bernhard Schölkopf (CoRR, 2016)

Kernel Mean Shrinkage Estimators. - Krikamol Muandet; Bharath K. Sriperumbudur; Kenji Fukumizu; Arthur Gretton; Bernhard Schölkopf (J. Mach. Learn. Res., 2016)

Characteristic Kernels and Infinitely Divisible Distributions. - Yu Nishiyama; Kenji Fukumizu (J. Mach. Learn. Res., 2016)

Filtering with State-Observation Examples via Kernel Monte Carlo Filter. - Motonobu Kanagawa; Yu Nishiyama; Arthur Gretton; Kenji Fukumizu (Neural Comput., 2016)

A Linear-Time Kernel Goodness-of-Fit Test. - Wittawat Jitkrittum; Wenkai Xu; Zoltán Szabó 001; Kenji Fukumizu; Arthur Gretton (NIPS, 2017)

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

On minimum spanning tree-like metric spaces. - Momoko Hayamizu; Kenji Fukumizu (Discret. Appl. Math., 2017)

Unsupervised group matching with application to cross-lingual topic matching without alignment information. - Tomoharu Iwata; Motonobu Kanagawa; Tsutomu Hirao; Kenji Fukumizu (Data Min. Knowl. Discov., 2017)

Kernel Mean Embedding of Distributions: A Review and Beyond. - Krikamol Muandet; Kenji Fukumizu; Bharath K. Sriperumbudur; Bernhard Schölkopf (Found. Trends Mach. Learn., 2017)

Kernel Method for Persistence Diagrams via Kernel Embedding and Weight Factor. - Genki Kusano; Kenji Fukumizu; Yasuaki Hiraoka (J. Mach. Learn. Res., 2017)

Density Estimation in Infinite Dimensional Exponential Families. - Bharath K. Sriperumbudur; Kenji Fukumizu; Arthur Gretton; Aapo Hyvärinen; Revant Kumar (J. Mach. Learn. Res., 2017)

A Characterization of Minimum Spanning Tree-Like Metric Spaces. - Momoko Hayamizu; Hiroshi Endo; Kenji Fukumizu (IEEE ACM Trans. Comput. Biol. Bioinform., 2017)

Post Selection Inference with Kernels. - Makoto Yamada; Yuta Umezu; Kenji Fukumizu; Ichiro Takeuchi (AISTATS, 2018)

Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions. - Sho Yokoi; Sosuke Kobayashi; Kenji Fukumizu; Jun Suzuki 001; Kentaro Inui (EMNLP, 2018)

Selecting the Best in GANs Family: a Post Selection Inference Framework. - Yao-Hung Hubert Tsai; Denny Wu; Makoto Yamada; Ruslan Salakhutdinov; Ichiro Takeuchi; Kenji Fukumizu (ICLR (Workshop), 2018)

Kernel Recursive ABC: Point Estimation with Intractable Likelihood. - Takafumi Kajihara; Motonobu Kanagawa; Keisuke Yamazaki; Kenji Fukumizu (ICML, 2018)

From Black-Box to White-Box: Interpretable Learning with Kernel Machines. - Hao Zhang; Shinji Nakadai; Kenji Fukumizu (MLDM (1), 2018)

Variational Learning on Aggregate Outputs with Gaussian Processes. - Ho Chung Leon Law; Dino Sejdinovic; Ewan Cameron; Tim C. D. Lucas; Seth R. Flaxman; Katherine Battle; Kenji Fukumizu (NeurIPS, 2018)

Influence function and robust variant of kernel canonical correlation analysis. - Md. Ashad Alam; Kenji Fukumizu; Yu-Ping Wang 002 (Neurocomputing, 2018)

Deep Neural Networks Learn Non-Smooth Functions Effectively. - Masaaki Imaizumi; Kenji Fukumizu (AISTATS, 2019)

Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator. - Makoto Yamada; Denny Wu; Yao-Hung Hubert Tsai; Hirofumi Ohta; Ruslan Salakhutdinov; Ichiro Takeuchi; Kenji Fukumizu (ICLR (Poster), 2019)

Semi-flat minima and saddle points by embedding neural networks to overparameterization. - Kenji Fukumizu; Shoichiro Yamaguchi; Yoh-ichi Mototake; Mirai Tanaka (NeurIPS, 2019)

Tree-Sliced Variants of Wasserstein Distances. - Tam Le; Makoto Yamada; Kenji Fukumizu; Marco Cuturi (NeurIPS, 2019)

Multilocus phylogenetic analysis with gene tree clustering. - Ruriko Yoshida; Kenji Fukumizu; Chrysafis Vogiatzis (Ann. Oper. Res., 2019)

Tree-Sliced Approximation of Wasserstein Distances. - Tam Le; Makoto Yamada; Kenji Fukumizu; Marco Cuturi (CoRR, 2019)

Model Selection for Simulator-based Statistical Models: A Kernel Approach. - Takafumi Kajihara; Motonobu Kanagawa; Yuuki Nakaguchi; Kanishka Khandelwal; Kenji Fukumizu (CoRR, 2019)

A Kernel Stein Test for Comparing Latent Variable Models. - Heishiro Kanagawa; Wittawat Jitkrittum; Lester Mackey; Kenji Fukumizu; Arthur Gretton (CoRR, 2019)

Deep Set-to-Set Matching and Learning. - Yuki Saito 002; Takuma Nakamura; Hirotaka Hachiya; Kenji Fukumizu (CoRR, 2019)

Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method. - Pengzhou Wu; Kenji Fukumizu (AISTATS, 2020)

Exchangeable Deep Neural Networks for Set-to-Set Matching and Learning. - Yuki Saito 002; Takuma Nakamura; Hirotaka Hachiya; Kenji Fukumizu (ECCV (17), 2020)

Smoothness and Stability in GANs. - Casey Chu; Kentaro Minami; Kenji Fukumizu (ICLR, 2020)

Robust Persistence Diagrams using Reproducing Kernels. - Siddharth Vishwanath; Kenji Fukumizu; Satoshi Kuriki; Bharath K. Sriperumbudur (NeurIPS, 2020)

The equivalence between Stein variational gradient descent and black-box variational inference. - Casey Chu; Kentaro Minami; Kenji Fukumizu (CoRR, 2020)

Advantage of Deep Neural Networks for Estimating Functions with Singularity on Curves. - Masaaki Imaizumi; Kenji Fukumizu (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)

Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings. - Motonobu Kanagawa; Bharath K. Sriperumbudur; Kenji Fukumizu (Found. Comput. Math., 2020)

Model-based kernel sum rule: kernel Bayesian inference with probabilistic models. - Yu Nishiyama; Motonobu Kanagawa; Arthur Gretton; Kenji Fukumizu (Mach. Learn., 2020)

Robust Bayesian model selection for variable clustering with the Gaussian graphical model. - Daniel Andrade; Akiko Takeda; Kenji Fukumizu (Stat. Comput., 2020)

imPhy: Imputing Phylogenetic Trees with Missing Information Using Mathematical Programming. - Niko Yasui; Chrysafis Vogiatzis; Ruriko Yoshida; Kenji Fukumizu (IEEE ACM Trans. Comput. Biol. Bioinform., 2020)

A General Class of Transfer Learning Regression without Implementation Cost. - Shunya Minami; Song Liu 002; Stephen Wu 001; Kenji Fukumizu; Ryo Yoshida (AAAI, 2021)

Meta Learning for Causal Direction. - Jean-François Ton; Dino Sejdinovic; Kenji Fukumizu (AAAI, 2021)

Identifying Treatment Effects under Unobserved Confounding by Causal Representation Learning. - Pengzhou Wu; Kenji Fukumizu (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)

Towards Principled Causal Effect Estimation by Deep Identifiable Models. - Pengzhou Wu; Kenji Fukumizu (CoRR, 2021)

β-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap. - Pengzhou Wu; Kenji Fukumizu (CoRR, 2021)

Convex covariate clustering for classification. - Daniel Andrade; Kenji Fukumizu; Yuzuru Okajima (Pattern Recognit. Lett., 2021)

$ beta$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap. - Pengzhou Abel Wu; Kenji Fukumizu (ICLR, 2022)

Unsupervised Learning of Equivariant Structure from Sequences. - Takeru Miyato; Masanori Koyama; Kenji Fukumizu (NeurIPS, 2022)

Invariance Learning based on Label Hierarchy. - Shoji Toyota; Kenji Fukumizu (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)

ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables. - Hironori Murase; Kenji Fukumizu (IEEE Access, 2022)

Robust Topological Inference in the Presence of Outliers. - Siddharth Vishwanath; Bharath K. Sriperumbudur; Kenji Fukumizu; Satoshi Kuriki (CoRR, 2022)

Invariance-adapted decomposition and Lasso-type contrastive learning. - Masanori Koyama; Takeru Miyato; Kenji Fukumizu (CoRR, 2022)

Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces. - Masaaki Imaizumi; Kenji Fukumizu (J. Mach. Learn. Res., 2022)

Scalable Unbalanced Sobolev Transport for Measures on a Graph. - Tam Le; Truyen Nguyen; Kenji Fukumizu (AISTATS, 2023)

Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network. - Yuri Kinoshita; Kenta Oono; Kenji Fukumizu; Yuichi Yoshida; Shin-ichi Maeda (ICML, 2023)

Transfer Learning with Affine Model Transformation. - Shunya Minami; Kenji Fukumizu; Yoshihiro Hayashi; Ryo Yoshida (NeurIPS, 2023)

Optimal Transport for Measures with Noisy Tree Metric. - Tam Le; Truyen Nguyen; Kenji Fukumizu (AISTATS, 2024)

Neural Fourier Transform: A General Approach to Equivariant Representation Learning. - Masanori Koyama; Kenji Fukumizu; Kohei Hayashi; Takeru Miyato (ICLR, 2024)

Generalized Sobolev Transport for Probability Measures on a Graph. - Tam Le; Truyen Nguyen; Kenji Fukumizu (ICML, 2024)

Neural-Kernel Conditional Mean Embeddings. - Eiki Shimizu; Kenji Fukumizu; Dino Sejdinovic (ICML, 2024)

Extended Flow Matching: a Method of Conditional Generation with Generalized Continuity Equation. - Noboru Isobe; Masanori Koyama; Kohei Hayashi; Kenji Fukumizu (CoRR, 2024)

State-Separated SARSA: A Practical Sequential Decision-Making Algorithm with Recovering Rewards. - Yuto Tanimoto; Kenji Fukumizu (CoRR, 2024)

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

Scaling Law of Sim2Real Transfer Learning in Expanding Computational Materials Databases for Real-World Predictions. - Shunya Minami; Yoshihiro Hayashi; Stephen Wu 001; Kenji Fukumizu; Hiroki Sugisawa; Masashi Ishii; Isao Kuwajima; Kazuya Shiratori; Ryo Yoshida (CoRR, 2024)

Out-of-Distribution Optimality of Invariant Risk Minimization. - Shoji Toyota; Kenji Fukumizu (Trans. Mach. Learn. Res., 2024)

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

Compositional simulation-based inference for time series. - Manuel Glöckler; Shoji Toyota; Kenji Fukumizu; Jakob H. Macke (ICLR, 2025)

Scalable Sobolev IPM for Probability Measures on a Graph. - Tam Le; Truyen Nguyen; Hideitsu Hino; Kenji Fukumizu (CoRR, 2025)

Orlicz-Sobolev Transport for Unbalanced Measures on a Graph. - Tam Le; Truyen Nguyen; Hideitsu Hino; Kenji Fukumizu (CoRR, 2025)

Simultaneous Learning of Optimal Transports for Training All-to-All Flow-Based Condition Transfer Model. - Kotaro Ikeda; Masanori Koyama; Jinzhe Zhang; Kohei Hayashi; Kenji Fukumizu (CoRR, 2025)

Diffusion Models with Double Guidance: Generate with aggregated datasets. - Yanfeng Yang; Kenji Fukumizu (CoRR, 2025)