Fukumizu, Kenji

79 publications

NeurIPS 2025 An Efficient Orlicz-Sobolev Approach for Transporting Unbalanced Measures on a Graph Tam Le, Truyen Nguyen, Hideitsu Hino, Kenji Fukumizu

ICLR 2025 Compositional Simulation-Based Inference for Time Series Manuel Gloeckler, Shoji Toyota, Kenji Fukumizu, Jakob H. Macke

ICLR 2025 Flow Matching Achieves Almost Minimax Optimal Convergence Kenji Fukumizu, Taiji Suzuki, Noboru Isobe, Kazusato Oko, Masanori Koyama

NeurIPS 2025 Pairwise Optimal Transports for Training All-to-All Flow-Based Condition Transfer Model Kotaro Ikeda, Masanori Koyama, Jinzhe Zhang, Kohei Hayashi, Kenji Fukumizu

ICML 2025 Scalable Sobolev IPM for Probability Measures on a Graph Tam Le, Truyen Nguyen, Hideitsu Hino, Kenji Fukumizu

ICML 2024 Generalized Sobolev Transport for Probability Measures on a Graph Tam Le, Truyen Nguyen, Kenji Fukumizu

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

ICML 2024 Neural-Kernel Conditional Mean Embeddings Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic

AISTATS 2024 Optimal Transport for Measures with Noisy Tree Metric Tam Le, Truyen Nguyen, Kenji Fukumizu

TMLR 2024 Out-of-Distribution Optimality of Invariant Risk Minimization Shoji Toyota, Kenji Fukumizu

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

NeurIPSW 2023 Optimal Transport for Measures with Noisy Tree Metric Tam Le, Truyen Nguyen, Kenji Fukumizu

AISTATS 2023 Scalable Unbalanced Sobolev Transport for Measures on a Graph Tam Le, Truyen Nguyen, Kenji Fukumizu

NeurIPS 2023 Transfer Learning with Affine Model Transformation Shunya Minami, Kenji Fukumizu, Yoshihiro Hayashi, Ryo Yoshida

ICLR 2022 $\beta$-Intact-VAE: Identifying and Estimating Causal Effects Under Limited Overlap Pengzhou Abel Wu, Kenji Fukumizu

ECML-PKDD 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

JMLR 2022 Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces Masaaki Imaizumi, Kenji Fukumizu

NeurIPS 2022 Invariance Learning Based on Label Hierarchy Shoji Toyota, Kenji Fukumizu

NeurIPS 2022 Unsupervised Learning of Equivariant Structure from Sequences Takeru Miyato, Masanori Koyama, Kenji Fukumizu

AAAI 2021 A General Class of Transfer Learning Regression Without Implementation Cost Shunya Minami, Song Liu, Stephen Wu, Kenji Fukumizu, Ryo Yoshida

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

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

ECCV 2020 Exchangeable Deep Neural Networks for Set-to-Set Matching and Learning Yuki Saito, Takuma Nakamura, Hirotaka Hachiya, Kenji Fukumizu

MLJ 2020 Model-Based Kernel Sum Rule: Kernel Bayesian Inference with Probabilistic Models Yu Nishiyama, Motonobu Kanagawa, Arthur Gretton, Kenji Fukumizu

NeurIPS 2020 Robust Persistence Diagrams Using Reproducing Kernels Siddharth Vishwanath, Kenji Fukumizu, Satoshi Kuriki, Bharath K. Sriperumbudur

ICLR 2020 Smoothness and Stability in GANs Casey Chu, Kentaro Minami, Kenji Fukumizu

ICLRW 2020 The Equivalence Between Stein Variational Gradient Descent and Black-Box Variational Inference Casey Chu, Kentaro Minami, Kenji Fukumizu

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

ICLR 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

NeurIPS 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

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

AISTATS 2018 Post Selection Inference with Kernels Makoto Yamada, Yuta Umezu, Kenji Fukumizu, Ichiro Takeuchi

NeurIPS 2018 Variational Learning on Aggregate Outputs with Gaussian Processes Ho Chung Law, Dino Sejdinovic, Ewan Cameron, Tim Lucas, Seth Flaxman, Katherine Battle, Kenji Fukumizu

NeurIPS 2017 A Linear-Time Kernel Goodness-of-Fit Test Wittawat Jitkrittum, Wenkai Xu, Zoltan Szabo, Kenji Fukumizu, Arthur Gretton

JMLR 2017 Density Estimation in Infinite Dimensional Exponential Families Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, Revant Kumar

FnTML 2017 Kernel Mean Embedding of Distributions: A Review and Beyond Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Bernhard Schölkopf

NeurIPS 2017 Trimmed Density Ratio Estimation Song Liu, Akiko Takeda, Taiji Suzuki, Kenji Fukumizu

JMLR 2016 Characteristic Kernels and Infinitely Divisible Distributions Yu Nishiyama, Kenji Fukumizu

NeurIPS 2016 Convergence Guarantees for Kernel-Based Quadrature Rules in Misspecified Settings Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu

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

JMLR 2016 Kernel Mean Shrinkage Estimators Krikamol Muandet, Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf

ICML 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, Taiji Suzuki, Masashi Sugiyama, Kenji Fukumizu

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

ICML 2014 Kernel Mean Estimation and Stein Effect Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur, Arthur Gretton, Bernhard Schoelkopf

AAAI 2014 Monte Carlo Filtering Using Kernel Embedding of Distributions Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu

AISTATS 2014 Recovering Distributions from Gaussian RKHS Embeddings Motonobu Kanagawa, Kenji Fukumizu

JMLR 2013 Kernel Bayes' Rule: Bayesian Inference with Positive Definite Kernels Kenji Fukumizu, Le Song, Arthur Gretton

NeurIPS 2012 Gradient-Based Kernel Method for Feature Extraction and Variable Selection Kenji Fukumizu, Chenlei Leng

UAI 2012 Hilbert Space Embeddings of POMDPs Yu Nishiyama, Abdeslam Boularias, Arthur Gretton, Kenji Fukumizu

ICML 2012 Hypothesis Testing Using Pairwise Distances and Associated Kernels Dino Sejdinovic, Arthur Gretton, Bharath K. Sriperumbudur, Kenji Fukumizu

NeurIPS 2012 Learning from Distributions via Support Measure Machines Krikamol Muandet, Kenji Fukumizu, Francesco Dinuzzo, Bernhard Schölkopf

NeurIPS 2012 Optimal Kernel Choice for Large-Scale Two-Sample Tests Arthur Gretton, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu, Bharath K. Sriperumbudur

NeurIPS 2011 Kernel Bayes' Rule Kenji Fukumizu, Le Song, Arthur Gretton

ACML 2011 Learning Low-Rank Output Kernels Francesco Dinuzzo, Kenji Fukumizu

NeurIPS 2011 Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint Kenji Fukumizu, Gert R. Lanckriet, Bharath K. Sriperumbudur

JMLR 2011 Universality, Characteristic Kernels and RKHS Embedding of Measures Bharath K. Sriperumbudur, Kenji Fukumizu, Gert R.G. Lanckriet

JMLR 2010 Hilbert Space Embeddings and Metrics on Probability Measures Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, Gert R.G. Lanckriet

AISTATS 2010 On the Relation Between Universality, Characteristic Kernels and RKHS Embedding of Measures Bharath Sriperumbudur, Kenji Fukumizu, Gert Lanckriet

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

NeurIPS 2009 Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation Yusuke Watanabe, Kenji Fukumizu

ICML 2009 Hilbert Space Embeddings of Conditional Distributions with Applications to Dynamical Systems Le Song, Jonathan Huang, Alexander J. Smola, Kenji Fukumizu

NeurIPS 2009 Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions Kenji Fukumizu, Arthur Gretton, Gert R. Lanckriet, Bernhard Schölkopf, Bharath K. Sriperumbudur

NeurIPS 2008 Characteristic Kernels on Groups and Semigroups Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf, Bharath K. Sriperumbudur

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

NeurIPS 2007 A Kernel Statistical Test of Independence Arthur Gretton, Kenji Fukumizu, Choon H. Teo, Le Song, Bernhard Schölkopf, Alex J. Smola

ICML 2007 A Kernel-Based Causal Learning Algorithm Xiaohai Sun, Dominik Janzing, Bernhard Schölkopf, Kenji Fukumizu

NeurIPS 2007 Kernel Measures of Conditional Dependence Kenji Fukumizu, Arthur Gretton, Xiaohai Sun, Bernhard Schölkopf

JMLR 2007 Statistical Consistency of Kernel Canonical Correlation Analysis Kenji Fukumizu, Francis R. Bach, Arthur Gretton

NeurIPS 2006 Kernels on Structured Objects Through Nested Histograms Marco Cuturi, Kenji Fukumizu

JMLR 2005 Semigroup Kernels on Measures Marco Cuturi, Kenji Fukumizu, Jean-Philippe Vert

NeurIPS 2005 Statistical Convergence of Kernel CCA Kenji Fukumizu, Arthur Gretton, Francis R. Bach

JMLR 2004 Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces Kenji Fukumizu, Francis R. Bach, Michael I. Jordan

NeurIPS 2003 Kernel Dimensionality Reduction for Supervised Learning Kenji Fukumizu, Francis R. Bach, Michael I. Jordan

NeurIPS 2002 Critical Lines in Symmetry of Mixture Models and Its Application to Component Splitting Kenji Fukumizu, Shotaro Akaho, Shun-ichi Amari

NeCo 2000 Adaptive Method of Realizing Natural Gradient Learning for Multilayer Perceptrons Shun-ichi Amari, Hyeyoung Park, Kenji Fukumizu

ALT 1999 Generalization Error of Limear Neural Networks in Unidentifiable Cases Kenji Fukumizu

NeurIPS 1995 Active Learning in Multilayer Perceptrons Kenji Fukumizu