Kenji Fukumizu

ORCID

Publication Activity (10 Years)

Years Active: 1995-2024
Publications (10 Years): 99

Top Venues

Publications

Shoji Toyota, Kenji Fukumizu
Out-of-Distribution Optimality of Invariant Risk Minimization. Trans. Mach. Learn. Res. 2024 (2024)
Masanori Koyama, Kenji Fukumizu, Kohei Hayashi, Takeru Miyato
Neural Fourier Transform: A General Approach to Equivariant Representation Learning. ICLR (2024)
Tam Le, Truyen Nguyen, Kenji Fukumizu
Generalized Sobolev Transport for Probability Measures on a Graph. CoRR (2024)
Kenji Fukumizu, Taiji Suzuki, Noboru Isobe, Kazusato Oko, Masanori Koyama
Flow matching achieves minimax optimal convergence. CoRR (2024)
Yuto Tanimoto, Kenji Fukumizu
State-Separated SARSA: A Practical Sequential Decision-Making Algorithm with Recovering Rewards. CoRR (2024)
Tam Le, Truyen Nguyen, Kenji Fukumizu
Optimal Transport for Measures with Noisy Tree Metric. AISTATS (2024)
Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic
Neural-Kernel Conditional Mean Embeddings. CoRR (2024)
Noboru Isobe, Masanori Koyama, Kohei Hayashi, Kenji Fukumizu
Extended Flow Matching: a Method of Conditional Generation with Generalized Continuity Equation. CoRR (2024)
Tam Le, Truyen Nguyen, Kenji Fukumizu
Scalable Unbalanced Sobolev Transport for Measures on a Graph. AISTATS (2023)
Yuri Kinoshita, Kenta Oono, Kenji Fukumizu, Yuichi Yoshida, Shin-ichi Maeda
Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network. ICML (2023)
Yuri Kinoshita, Kenta Oono, Kenji Fukumizu, Yuichi Yoshida, Shin-ichi Maeda
Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network. CoRR (2023)
Masanori Koyama, Kenji Fukumizu, Kohei Hayashi, Takeru Miyato
Neural Fourier Transform: A General Approach to Equivariant Representation Learning. CoRR (2023)
Shunya Minami, Kenji Fukumizu, Yoshihiro Hayashi, Ryo Yoshida
Transfer Learning with Affine Model Transformation. NeurIPS (2023)
Shoji Toyota, Kenji Fukumizu
Out-of-Distribution Optimality of Invariant Risk Minimization. CoRR (2023)
Tam Le, Truyen Nguyen, Kenji Fukumizu
Optimal Transport for Measures with Noisy Tree Metric. CoRR (2023)
Tam Le, Truyen Nguyen, Kenji Fukumizu
Scalable Unbalanced Sobolev Transport for Measures on a Graph. CoRR (2023)
Takeru Miyato, Masanori Koyama, Kenji Fukumizu
Unsupervised Learning of Equivariant Structure from Sequences. CoRR (2022)
Hironori Murase, Kenji Fukumizu
ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables. CoRR (2022)
Hiroaki Mikami, Kenji Fukumizu, Shogo Murai, Shuji Suzuki, Yuta Kikuchi, Taiji Suzuki, Shin-ichi Maeda, Kohei Hayashi
A Scaling Law for Syn2real Transfer: How Much Is Your Pre-training Effective? ECML/PKDD (3) (2022)
Shunya Minami, Kenji Fukumizu, Yoshihiro Hayashi, Ryo Yoshida
Transfer learning with affine model transformation. CoRR (2022)
Shoji Toyota, Kenji Fukumizu
Invariance Learning based on Label Hierarchy. CoRR (2022)
Siddharth Vishwanath, Bharath K. Sriperumbudur, Kenji Fukumizu, Satoshi Kuriki
Robust Topological Inference in the Presence of Outliers. CoRR (2022)
Hironori Murase, Kenji Fukumizu
ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables. IEEE Access 10 (2022)
Takeru Miyato, Masanori Koyama, Kenji Fukumizu
Unsupervised Learning of Equivariant Structure from Sequences. NeurIPS (2022)
Pengzhou Abel Wu, Kenji Fukumizu
$\beta$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap. ICLR (2022)
Masaaki Imaizumi, Kenji Fukumizu
Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces. J. Mach. Learn. Res. 23 (2022)
Masanori Koyama, Takeru Miyato, Kenji Fukumizu
Invariance-adapted decomposition and Lasso-type contrastive learning. CoRR (2022)
Shoji Toyota, Kenji Fukumizu
Invariance Learning based on Label Hierarchy. NeurIPS (2022)
Pengzhou Wu, Kenji Fukumizu
Towards Principled Causal Effect Estimation by Deep Identifiable Models. CoRR (2021)
Shunya Minami, Song Liu, Stephen Wu, Kenji Fukumizu, Ryo Yoshida
A General Class of Transfer Learning Regression without Implementation Cost. AAAI (2021)
Daniel Andrade, Kenji Fukumizu, Yuzuru Okajima
Convex covariate clustering for classification. Pattern Recognit. Lett. 151 (2021)
Pengzhou Wu, Kenji Fukumizu
Identifying Treatment Effects under Unobserved Confounding by Causal Representation Learning. CoRR (2021)
Hiroaki Mikami, Kenji Fukumizu, Shogo Murai, Shuji Suzuki, Yuta Kikuchi, Taiji Suzuki, Shin-ichi Maeda, Kohei Hayashi
A Scaling Law for Synthetic-to-Real Transfer: A Measure of Pre-Training. CoRR (2021)
Jean-François Ton, Dino Sejdinovic, Kenji Fukumizu
Meta Learning for Causal Direction. AAAI (2021)
Pengzhou Wu, Kenji Fukumizu
β-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap. CoRR (2021)
Shunya Minami, Song Liu, Stephen Wu, Kenji Fukumizu, Ryo Yoshida
A General Class of Transfer Learning Regression without Implementation Cost. CoRR (2020)
Casey Chu, Kentaro Minami, Kenji Fukumizu
Smoothness and Stability in GANs. CoRR (2020)
Casey Chu, Kentaro Minami, Kenji Fukumizu
Smoothness and Stability in GANs. ICLR (2020)
Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu
Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings. Found. Comput. Math. 20 (1) (2020)
Shaogao Lv, Zengyan Fan, Heng Lian, Taiji Suzuki, Kenji Fukumizu
A reproducing kernel Hilbert space approach to high dimensional partially varying coefficient model. Comput. Stat. Data Anal. 152 (2020)
Daniel Andrade, Akiko Takeda, Kenji Fukumizu
Robust Bayesian model selection for variable clustering with the Gaussian graphical model. Stat. Comput. 30 (2) (2020)
Pengzhou Wu, Kenji Fukumizu
Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method. CoRR (2020)
Pengzhou Wu, Kenji Fukumizu
Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method. AISTATS (2020)
Yu Nishiyama, Motonobu Kanagawa, Arthur Gretton, Kenji Fukumizu
Model-based kernel sum rule: kernel Bayesian inference with probabilistic models. Mach. Learn. 109 (5) (2020)
Masaaki Imaizumi, Kenji Fukumizu
Advantage of Deep Neural Networks for Estimating Functions with Singularity on Curves. CoRR (2020)
Siddharth Vishwanath, Kenji Fukumizu, Satoshi Kuriki, Bharath K. Sriperumbudur
Robust Persistence Diagrams using Reproducing Kernels. NeurIPS (2020)
Siddharth Vishwanath, Kenji Fukumizu, Satoshi Kuriki, Bharath K. Sriperumbudur
Robust Persistence Diagrams using Reproducing Kernels. CoRR (2020)
Niko Yasui, Chrysafis Vogiatzis, Ruriko Yoshida, Kenji Fukumizu
imPhy: Imputing Phylogenetic Trees with Missing Information Using Mathematical Programming. IEEE ACM Trans. Comput. Biol. Bioinform. 17 (4) (2020)
Jean-Francois Ton, Dino Sejdinovic, Kenji Fukumizu
Meta Learning for Causal Direction. CoRR (2020)
Casey Chu, Kentaro Minami, Kenji Fukumizu
The equivalence between Stein variational gradient descent and black-box variational inference. CoRR (2020)
Yuki Saito, Takuma Nakamura, Hirotaka Hachiya, Kenji Fukumizu
Exchangeable Deep Neural Networks for Set-to-Set Matching and Learning. ECCV (17) (2020)
Kenji Fukumizu, Shoichiro Yamaguchi, Yoh-ichi Mototake, Mirai Tanaka
Semi-flat minima and saddle points by embedding neural networks to overparameterization. NeurIPS (2019)
Yuki Saito, Takuma Nakamura, Hirotaka Hachiya, Kenji Fukumizu
Deep Set-to-Set Matching and Learning. CoRR (2019)
Takafumi Kajihara, Motonobu Kanagawa, Yuuki Nakaguchi, Kanishka Khandelwal, Kenji Fukumizu
Model Selection for Simulator-based Statistical Models: A Kernel Approach. CoRR (2019)
Tam Le, Makoto Yamada, Kenji Fukumizu, Marco Cuturi
Tree-Sliced Variants of Wasserstein Distances. NeurIPS (2019)
Ruriko Yoshida, Kenji Fukumizu, Chrysafis Vogiatzis
Multilocus phylogenetic analysis with gene tree clustering. Ann. Oper. Res. 276 (1-2) (2019)
Tam Le, Makoto Yamada, Kenji Fukumizu, Marco Cuturi
Tree-Sliced Approximation of Wasserstein Distances. CoRR (2019)
Makoto Yamada, Denny Wu, Yao-Hung Hubert Tsai, Hirofumi Ohta, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu
Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator. ICLR (Poster) (2019)
Heishiro Kanagawa, Wittawat Jitkrittum, Lester Mackey, Kenji Fukumizu, Arthur Gretton
A Kernel Stein Test for Comparing Latent Variable Models. CoRR (2019)
Kenji Fukumizu, Shoichiro Yamaguchi, Yoh-ichi Mototake, Mirai Tanaka
Semi-flat minima and saddle points by embedding neural networks to overparameterization. CoRR (2019)
Masaaki Imaizumi, Kenji Fukumizu
Deep Neural Networks Learn Non-Smooth Functions Effectively. AISTATS (2019)
Yao-Hung Hubert Tsai, Makoto Yamada, Denny Wu, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu
Selecting the Best in GANs Family: a Post Selection Inference Framework. CoRR (2018)
Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron, Tim C. D. Lucas, Seth R. Flaxman, Katherine Battle, Kenji Fukumizu
Variational Learning on Aggregate Outputs with Gaussian Processes. CoRR (2018)
Takafumi Kajihara, Motonobu Kanagawa, Keisuke Yamazaki, Kenji Fukumizu
Kernel Recursive ABC: Point Estimation with Intractable Likelihood. ICML (2018)
Md. Ashad Alam, Kenji Fukumizu, Yu-Ping Wang
Influence function and robust variant of kernel canonical correlation analysis. Neurocomputing 304 (2018)
Makoto Yamada, Yuta Umezu, Kenji Fukumizu, Ichiro Takeuchi
Post Selection Inference with Kernels. AISTATS (2018)
Sho Yokoi, Sosuke Kobayashi, Kenji Fukumizu, Jun Suzuki, Kentaro Inui
Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions. EMNLP (2018)
Hao Zhang, Shinji Nakadai, Kenji Fukumizu
From Black-Box to White-Box: Interpretable Learning with Kernel Machines. MLDM (1) (2018)
Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron, Tim C. D. Lucas, Seth R. Flaxman, Katherine Battle, Kenji Fukumizu
Variational Learning on Aggregate Outputs with Gaussian Processes. NeurIPS (2018)
Sho Yokoi, Sosuke Kobayashi, Kenji Fukumizu, Jun Suzuki, Kentaro Inui
Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions. CoRR (2018)
Yao-Hung Hubert Tsai, Denny Wu, Makoto Yamada, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu
Selecting the Best in GANs Family: a Post Selection Inference Framework. ICLR (Workshop) (2018)
Song Liu, Akiko Takeda, Taiji Suzuki, Kenji Fukumizu
Trimmed Density Ratio Estimation. NIPS (2017)
Tomoharu Iwata, Motonobu Kanagawa, Tsutomu Hirao, Kenji Fukumizu
Unsupervised group matching with application to cross-lingual topic matching without alignment information. Data Min. Knowl. Discov. 31 (2) (2017)
Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Bernhard Schölkopf
Kernel Mean Embedding of Distributions: A Review and Beyond. Found. Trends Mach. Learn. 10 (1-2) (2017)
Wittawat Jitkrittum, Wenkai Xu, Zoltán Szabó, Kenji Fukumizu, Arthur Gretton
A Linear-Time Kernel Goodness-of-Fit Test. NIPS (2017)
Momoko Hayamizu, Hiroshi Endo, Kenji Fukumizu
A Characterization of Minimum Spanning Tree-Like Metric Spaces. IEEE ACM Trans. Comput. Biol. Bioinform. 14 (2) (2017)
Wittawat Jitkrittum, Wenkai Xu, Zoltán Szabó, Kenji Fukumizu, Arthur Gretton
A Linear-Time Kernel Goodness-of-Fit Test. CoRR (2017)
Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu
Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings. CoRR (2017)
Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, Revant Kumar
Density Estimation in Infinite Dimensional Exponential Families. J. Mach. Learn. Res. 18 (2017)
Momoko Hayamizu, Kenji Fukumizu
On minimum spanning tree-like metric spaces. Discret. Appl. Math. 226 (2017)
Genki Kusano, Kenji Fukumizu, Yasuaki Hiraoka
Kernel Method for Persistence Diagrams via Kernel Embedding and Weight Factor. J. Mach. Learn. Res. 18 (2017)
Kazuo Hara, Ikumi Suzuki, Kei Kobayashi, Kenji Fukumizu, Milos Radovanovic
Flattening the Density Gradient for Eliminating Spatial Centrality to Reduce Hubness. AAAI (2016)
Song Liu, Kenji Fukumizu
Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective. SDM (2016)
Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Bernhard Schölkopf
Kernel Mean Embedding of Distributions: A Review and Beyonds. CoRR (2016)
Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu
Filtering with State-Observation Examples via Kernel Monte Carlo Filter. Neural Comput. 28 (2) (2016)
Song Liu, Taiji Suzuki, Masashi Sugiyama, Kenji Fukumizu
Structure Learning of Partitioned Markov Networks. ICML (2016)
Krikamol Muandet, Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf
Kernel Mean Shrinkage Estimators. J. Mach. Learn. Res. 17 (2016)
Genki Kusano, Yasuaki Hiraoka, Kenji Fukumizu
Persistence weighted Gaussian kernel for topological data analysis. ICML (2016)
Yu Nishiyama, Kenji Fukumizu
Characteristic Kernels and Infinitely Divisible Distributions. J. Mach. Learn. Res. 17 (2016)
Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu
Convergence guarantees for kernel-based quadrature rules in misspecified settings. NIPS (2016)
Momoko Hayamizu, Hiroshi Endo, Kenji Fukumizu
A characterization of minimum spanning tree-like metric spaces. CoRR (2015)
Song Liu, Kenji Fukumizu
Lazy Transfer Learning. CoRR (2015)
Somayeh Danafar, Kenji Fukumizu, Faustino Gomez
Kernel-Based Information Criterion. Comput. Inf. Sci. 8 (1) (2015)
Kazuo Hara, Ikumi Suzuki, Kei Kobayashi, Kenji Fukumizu, Milos Radovanovic
Reducing Hubness for Kernel Regression. SISAP (2015)
Kazuo Hara, Ikumi Suzuki, Kei Kobayashi, Kenji Fukumizu
Reducing Hubness: A Cause of Vulnerability in Recommender Systems. SIGIR (2015)
Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Jonas Peters
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations. CoRR (2015)
Md. Ashad Alam, Kenji Fukumizu
Higher-Order Regularized Kernel Canonical Correlation Analysis. Int. J. Pattern Recognit. Artif. Intell. 29 (4) (2015)
Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Stefan Harmeling, Jonas Peters
Computing functions of random variables via reproducing kernel Hilbert space representations. Stat. Comput. 25 (4) (2015)
Kazuo Hara, Ikumi Suzuki, Masashi Shimbo, Kei Kobayashi, Kenji Fukumizu, Milos Radovanovic
Localized Centering: Reducing Hubness in Large-Sample Data. AAAI (2015)
Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu
Monte Carlo Filtering Using Kernel Embedding of Distributions. AAAI (2014)

Kenji Fukumizu.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Kenji Fukumizu