K. Fukumizu

发表

Adaptive Method of Realizing Natural Gradient Learning for Multilayer Perceptrons

Kenji Fukumizu, Hyeyoung Park, Shun-ichi Amari, 2000, Neural Computation.

Kernel Dimensionality Reduction for Supervised Learning

Michael I. Jordan, Kenji Fukumizu, Francis R. Bach, 2003, NIPS.

A Kernel Stein Test for Comparing Latent Variable Models pdf

Kenji Fukumizu, Arthur Gretton, Wittawat Jitkrittum, 2019, Journal of the Royal Statistical Society Series B: Statistical Methodology.

On the empirical estimation of integral probability metrics

Gert R. G. Lanckriet, Bharath K. Sriperumbudur, K. Fukumizu, 2012 .

Local minima and plateaus in hierarchical structures of multilayer perceptrons

Kenji Fukumizu, Shun-ichi Amari, S. Amari, 2000, Neural Networks.

Adaptive natural gradient learning algorithms for various stochastic models

Kenji Fukumizu, Shun-ichi Amari, Hyeyoung Park, 2000, Neural Networks.

Kernel dimension reduction in regression

Michael I. Jordan, F. Bach, K. Fukumizu, 2009, 0908.1854.

A Regularity Condition of the Information Matrix of a Multilayer Perceptron Network

Kenji Fukumizu, K. Fukumizu, 1996, Neural Networks.

Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces

Michael I. Jordan, Kenji Fukumizu, Francis R. Bach, 2004, J. Mach. Learn. Res..

Equivalence of distance-based and RKHS-based statistics in hypothesis testing pdf

Kenji Fukumizu, Arthur Gretton, Dino Sejdinovic, 2012, ArXiv.

Discussion of: Brownian distance covariance

Bharath K. Sriperumbudur, K. Fukumizu, A. Gretton, 2009, 1010.0836.

Optimal kernel choice for large-scale two-sample tests

Sivaraman Balakrishnan, Kenji Fukumizu, Arthur Gretton, 2012, NIPS.

Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions

Bernhard Schölkopf, Kenji Fukumizu, Arthur Gretton, 2009, NIPS.

RKHS Representation of Measures Applied to Homogeneity, Independence, and Fourier Optics

Bharath K. Sriperumbudur, S. Smale, B. Schölkopf, 2008 .

Ja n 20 09 A Note on Integral Probability Metrics and φ-divergences

Bharath K. Sriperumbudur, B. Schölkopf, K. Fukumizu, 2009 .

Effect of Batch Learning in Multilayer Neural Networks

Kenji Fukumizu, K. Fukumizu, 1998, ICONIP.

Special Statistical Properties of Neural Network Learning

K. Fukumizu, 1997 .

Learning via Hilbert Space Embedding of Distributions

Karsten M. Borgwardt, K. Fukumizu, Alex Smola, 2007 .

Kernel Measures of Conditional Dependence

Bernhard Schölkopf, Kenji Fukumizu, Arthur Gretton, 2007, NIPS.

Kernel Mean Estimation and Stein Effect

Bernhard Schölkopf, Kenji Fukumizu, Arthur Gretton, 2013, ICML.

Consistency of Kernel Canonical Correlation Analysis

Kenji Fukumizu, Arthur Gretton, Francis Bach, 2005 .

Towards Principled Causal Effect Estimation by Deep Identifiable Models pdf

Kenji Fukumizu, Pengzhou Wu, K. Fukumizu, 2021, ArXiv.

Kernel approximate Bayesian computation in population genetic inferences

K. Fukumizu, S. Mano, S. Nakagome, 2012, Statistical applications in genetics and molecular biology.

Learning low-rank output kernels

Kenji Fukumizu, Francesco Dinuzzo, K. Fukumizu, 2011, ACML.

A Kernel Statistical Test of Independence

Le Song, Bernhard Schölkopf, Alexander J. Smola, 2007, NIPS.

Algebraic and Geometric Methods in Statistics: Exponential manifold by reproducing kernel Hilbert spaces

K. Fukumizu, 2009 .

Learning from Distributions via Support Measure Machines

Bernhard Schölkopf, Kenji Fukumizu, Krikamol Muandet, 2012, NIPS.

Universality, Characteristic Kernels and RKHS Embedding of Measures

Kenji Fukumizu, Gert R. G. Lanckriet, Bharath K. Sriperumbudur, 2010, J. Mach. Learn. Res..

Hilbert Space Embeddings of POMDPs

Kenji Fukumizu, Arthur Gretton, Abdeslam Boularias, 2012, UAI.

Kernel Bayes' Rule

Le Song, Kenji Fukumizu, Arthur Gretton, 2010, NIPS.

Kernel Monte Carlo Filter

K. Fukumizu, A. Gretton, Motonobu Kanagawa, 2013 .

Kernel Embeddings of Conditional Distributions: A Unified Kernel Framework for Nonparametric Inference in Graphical Models

K. Fukumizu, Le Song, A. Gretton, 2013, IEEE Signal Process. Mag..

A Fast, Consistent Kernel Two-Sample Test

Zaïd Harchaoui, Kenji Fukumizu, Arthur Gretton, 2009, NIPS.

On integral probability metrics, φ-divergences and binary classification

Kenji Fukumizu, Arthur Gretton, Gert R. G. Lanckriet, 2009, 0901.2698.

Hilbert space embeddings of conditional distributions with applications to dynamical systems

Alexander J. Smola, Kenji Fukumizu, Le Song, 2009, ICML '09.

Critical Lines in Symmetry of Mixture Models and its Application to Component Splitting

Kenji Fukumizu, Shun-ichi Amari, Shotaro Akaho, 2002, NIPS.

Likelihood ratio of unidentifiable models and multilayer neural networks

K. Fukumizu, 2003 .

Statistical Convergence of Kernel CCA

Kenji Fukumizu, Arthur Gretton, Francis R. Bach, 2005, NIPS.

Probabilistic design of layered neural networks based on their unified framework

Kenji Fukumizu, Sumio Watanabe, K. Fukumizu, 1995, IEEE Trans. Neural Networks.

A unified kernel framework for nonparametric inference in graphical models ] Kernel Embeddings of Conditional Distributions

Le Song, Kenji Fukumizu, Arthur Gretton, 2013 .

A network of chaotic elements for information processing

Shin Ishii, Kenji Fukumizu, Sumio Watanabe, 1996, Neural Networks.

Hypothesis testing using pairwise distances and associated kernels pdf

Kenji Fukumizu, Arthur Gretton, Dino Sejdinovic, 2012, ICML.

Kernel Mean Embedding of Distributions: A Review and Beyonds pdf

Bernhard Schölkopf, Kenji Fukumizu, Krikamol Muandet, 2016, Found. Trends Mach. Learn..

Injective Hilbert Space Embeddings of Probability Measures

Bernhard Schölkopf, Kenji Fukumizu, Arthur Gretton, 2008, COLT.

Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint

Kenji Fukumizu, Gert R. G. Lanckriet, Bharath K. Sriperumbudur, 2011, NIPS.

Semigroup Kernels on Measures

Kenji Fukumizu, Jean-Philippe Vert, Marco Cuturi, 2005, J. Mach. Learn. Res..

Active Learning in Multilayer Perceptrons

Kenji Fukumizu, K. Fukumizu, 1995, NIPS.

Hilbert Space Embeddings and Metrics on Probability Measures

Bernhard Schölkopf, Kenji Fukumizu, Arthur Gretton, 2009, J. Mach. Learn. Res..

Selecting the Best in GANs Family: a Post Selection Inference Framework pdf

Kenji Fukumizu, Makoto Yamada, Ruslan Salakhutdinov, 2018, ICLR.

Loopy Belief Propagation, Bethe Free Energy and Graph Zeta Function pdf

Kenji Fukumizu, Yusuke Watanabe, K. Fukumizu, 2011, ArXiv.

A Linear-Time Kernel Goodness-of-Fit Test

Kenji Fukumizu, Arthur Gretton, Wittawat Jitkrittum, 2017, NIPS.

Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings

Kenji Fukumizu, Bharath K. Sriperumbudur, Motonobu Kanagawa, 2017, Foundations of Computational Mathematics.

On Learning Sparse Changes of Markov Networks

Masashi Sugiyama, Kenji Fukumizu, Taiji Suzuki, 2014 .

Unsupervised group matching with application to cross-lingual topic matching without alignment information

Tomoharu Iwata, Kenji Fukumizu, Tsutomu Hirao, 2017, Data Mining and Knowledge Discovery.

Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator

Kenji Fukumizu, Makoto Yamada, Ruslan Salakhutdinov, 2018, ICLR.

Active Learning in Multilayer

K. Fukumizu, 2000 .

A Characterization of Minimum Spanning Tree-Like Metric Spaces

Kenji Fukumizu, Hiroshi Endo, Momoko Hayamizu, 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Gradient-based kernel method for feature extraction and variable selection

Kenji Fukumizu, Chenlei Leng, K. Fukumizu, 2012, NIPS.

Generalization Error of Linear Neural Networks in Unidentiable Cases

K. Fukumizu, 1999 .

Generalization Error of Limear Neural Networks in Unidentifiable Cases

Kenji Fukumizu, K. Fukumizu, 1999, ALT.

Associative memory using spatiotemporal chaos

Kenji Fukumizu, Shin Ishii, Sumio Watanabe, 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

Multilocus phylogenetic analysis with gene tree clustering

Kenji Fukumizu, Ruriko Yoshida, Chrysafis Vogiatzis, 2015, Ann. Oper. Res..

Statistical approaches to combining binary classifiers for multi-class classification

Yuichi Shiraishi, Kenji Fukumizu, K. Fukumizu, 2011, Neurocomputing.

Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces

Michael I. Jordan, F. Bach, K. Fukumizu, 2004 .

Gradient-Based Kernel Dimension Reduction for Regression

K. Fukumizu, Chenlei Leng, 2014 .

Robust Kernel (Cross-) Covariance Operators in Reproducing Kernel Hilbert Space toward Kernel Methods

Md. Ashad Alam, K. Fukumizu, Yu-ping Wang, 2016, 1602.05563.

Statistical Invariance of Betti Numbers in the Thermodynamic Regime.

Bharath K. Sriperumbudur, K. Fukumizu, S. Kuriki, 2020 .

Local Kernel Dimension Reduction in Approximate Bayesian Computation

K. Fukumizu, Jin Zhou, 2016, 1609.01022.

Nonparametric Bayesian Inference with Kernel Mean Embedding

K. Fukumizu, 2015 .

Infinite dimensional exponential families by reproducing kernel Hilbert spaces

K. Fukumizu, 2005 .

Loop series expansion with propagation diagrams

K. Fukumizu, Yusuke Watanabe, 2008, 0808.1155.

Dynamics of Batch Learning in Multilayer Networks { Overrealizability and Overtraining {

K. Fukumizu, 2007 .

Likelihood Ratio of Unidenti Þ able Models and Multilayer Neural Networks

K. Fukumizu, 2001 .

Statistical Analysis of UnidentiÞable Models and its Application to Multilayer Neural Networks

Kenji Fukumizu, K. Fukumizu, 2000 .

Learning in neural networks and an integrable system (Applied Mathematics of Discrete Integrable Systems)

K. Fukumizu, 1999 .

Asymptotic Theory of Locally Conic Models and its Application to Multilayer Neural Networks

K. Fukumizu, 2001 .

Asymptotic Theory of Locally Conic Models and its Applications to Neural Networks

K. Fukumizu, 2001 .

Probability density estimation by regularization method

Kenji Fukumizu, Sumio Watanabe, K. Fukumizu, 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

Kernel mean embedding of probability measures and its applications to functional data analysis

K. Fukumizu, Saeed Hayati, A. Parvardeh, 2020, Scandinavian Journal of Statistics.

Game theoretical analysis of combining binary classifiers for multi-class classification problems

K. Fukumizu, Yuichi Shiraishi, 2007 .

Revealing the Mechanism of Magnetic Domain Formation by Topological Data Analysis

K. Fukumizu, M. Mizumaki, Yoh-ichi Mototake, 2022, 2204.12194.

Over-fitting behavior of Gaussian unit under Gaussian noise

K. Fukumizu, K. Hagiwara, 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

No . 858 November 07 , 2002 A general upper bound of likelihood ratio in binary regression

K. Fukumizu, K. Hagiwara, 2002 .

Statistical Active Learning in Multilayer

K. Fukumizu, 2022 .

Kernel Embeddings of Conditional Distributions: A Unified Kernel Framework for Nonparametric Inference in Graphical Models

K. Fukumizu, Le Song, A. Gretton, 2013, IEEE Signal Processing Magazine.

Chapter 17 Geometry of neural networks: Natural gradient for learning

K. Fukumizu, K. Fukumizu, 2001 .

Kernel Algorithms Distribution Embeddings in Reproducing Kernel Hilbert Spaces

B. Schölkopf, K. Fukumizu, Alex Smola, 2009 .

A note on integral probability metrics and $\phi$-divergences pdf

Bernhard Schölkopf, Kenji Fukumizu, Arthur Gretton, 2009, ArXiv.

Deep Learning: Theory, Algorithms, and Applications (NII Shonan Meeting 2014-5)

Kenji Fukumizu, Pierre Baldi, Tomaso A. Poggio, 2014, NII Shonan Meet. Rep..

Lazy Transfer Leanring

Kenji Fukumizu, Song Liu, K. Fukumizu, 2015 .

Support Vector Machines Statistical Inference with Reproducing Kernel Hilbert Space

Kenji Fukumizu, K. Fukumizu, 2008 .

Understanding GANs via Generalization Analysis for Disconnected Support

Kenji Fukumizu, Masaaki Imaizumi, K. Fukumizu, 2018 .

Meta Learning for Causal Direction

Kenji Fukumizu, Jean-Francois Ton, Dino Sejdinovic, 2020, AAAI.

Probabilistic design

Kenji Fukumizu, Sumio Watanabe, K. Fukumizu, 2020, Algorithms and Architectures.

Support Vector Machine II Statistical Data Analysis with Positive Definite Kernels

Kenji Fukumizu, K. Fukumizu, 2008 .

Kernel Methods for Statistical Learning

Kenji Fukumizu, K. Fukumizu, 2012 .

Geometry of neural networks and models with singularities

K. Fukumizu, 2001 .

ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables

K. Fukumizu, Hironori Murase, 2022, IEEE Access.

Model-based Kernel Sum Rule with Applications to State Space Models

K. Fukumizu, Motonobu Kanagawa, Yu Nishiyama, 2014 .

Painless Embeddings of Distributions: the Function Space View (Part 1)

Alexander J. Smola, K. Fukumizu, A. Gretton, 2008, ICML 2008.

Robust Topological Inference in the Presence of Outliers

Bharath K. Sriperumbudur, K. Fukumizu, S. Kuriki, 2022, ArXiv.

Deformed Toric Ideal Constraints for Stoichiometric Networks

K. Fukumizu, Masamichi Sato, 2012, 1202.5092.

Structure Learning of Partitioned Markov Networks

K. Fukumizu, Masashi Sugiyama, Taiji Suzuki, 2015, ICML.

Robust Persistence Diagrams using Reproducing Kernels

Bharath K. Sriperumbudur, K. Fukumizu, S. Kuriki, 2020, Neural Information Processing Systems.

Transfer learning with affine model transformation

K. Fukumizu, Ryo Yoshida, Shunya Minami, 2022, ArXiv.

Unsupervised Learning of Equivariant Structure from Sequences

K. Fukumizu, Takeru Miyato, Masanori Koyama, 2022, NeurIPS.

Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings

Bharath K. Sriperumbudur, K. Fukumizu, Motonobu Kanagawa, 2019, Foundations of Computational Mathematics.

Multilocus phylogenetic analysis with gene tree clustering

K. Fukumizu, R. Yoshida, Chrysafis Vogiatzis, 2017, Annals of Operations Research.

Graph zeta function and loopy belief propagation

K. Fukumizu, Yusuke Watanabe, 2011 .

Robust Bayesian model selection for variable clustering with the Gaussian graphical model

K. Fukumizu, A. Takeda, Daniel Andrade, 2019, Statistics and Computing.

A Scaling Law for Syn2real Transfer: How Much Is Your Pre-training Effective?

Hiroaki Mikami, K. Fukumizu, Taiji Suzuki, 2022, ECML/PKDD.

On the Limits of Topological Data Analysis for Statistical Inference

Bharath K. Sriperumbudur, K. Fukumizu, S. Kuriki, 2020, 2001.00220.

Tree-Sliced Approximation of Wasserstein Distances pdf

Kenji Fukumizu, Makoto Yamada, Tam Le, 2019, ArXiv.

Tree-Sliced Variants of Wasserstein Distances

Kenji Fukumizu, Makoto Yamada, Marco Cuturi, 2019, NeurIPS.

Active Learning for Network Estimation

Kenji Fukumizu, Shotaro Akaho, K. Fukumizu, 2007, 2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology.

Statistical active learning in multilayer perceptrons

Kenji Fukumizu, K. Fukumizu, 2000, IEEE Trans. Neural Networks Learn. Syst..

Kernel Mean Shrinkage Estimators pdf

Bernhard Schölkopf, Kenji Fukumizu, Arthur Gretton, 2014, J. Mach. Learn. Res..

Supplementary Material to Kernel Mean Estimation and Stein Effect

Bharath K. Sriperumbudur, B. Schölkopf, K. Fukumizu, 2014 .

Semi-flat minima and saddle points by embedding neural networks to overparameterization

Yoh-ichi Mototake, Kenji Fukumizu, Mirai Tanaka, 2019, NeurIPS.

Deep Neural Networks Learn Non-Smooth Functions Effectively

Kenji Fukumizu, Masaaki Imaizumi, K. Fukumizu, 2018, AISTATS.

Chapter 17 Geometry of neural networks: Natural gradient for learning

K. Fukumizu, 2001 .

Filtering with State-Observation Examples via Kernel Monte Carlo Filter

Kenji Fukumizu, Arthur Gretton, Yu Nishiyama, 2013, Neural Computation.

A reproducing kernel Hilbert space approach to high dimensional partially varying coefficient model

Kenji Fukumizu, Taiji Suzuki, Shaogao Lv, 2020, Comput. Stat. Data Anal..

Model-based kernel sum rule: kernel Bayesian inference with probabilistic models

Kenji Fukumizu, Arthur Gretton, Yu Nishiyama, 2014, Machine Learning.

Taxonomy matching between asteroids and meteorites: supervised clustering approach

Kenji Fukumizu, Peng Hong, Yuki Saito, 2018, Journal of Physics: Conference Series.

Influence function and robust variant of kernel canonical correlation analysis

Kenji Fukumizu, Yu-Ping Wang, Md. Ashad Alam, 2017, Neurocomputing.

Persistence weighted Gaussian kernel for topological data analysis

Kenji Fukumizu, Yasuaki Hiraoka, Genki Kusano, 2016, ICML.

Special Issue on Advances in Kernel-Based Learning for Signal Processing [From the Guest Editors]

Sergios Theodoridis, Klaus-Robert Müller, Kenji Fukumizu, 2013, IEEE Signal Process. Mag..

Special Issue on Advances in Kernel-Based Learning for Signal Processing

Sergios Theodoridis, Klaus-Robert Müller, Kenji Fukumizu, 2013 .

imPhy: Imputing Phylogenetic Trees with Missing Information Using Mathematical Programming

Kenji Fukumizu, Ruriko Yoshida, Chrysafis Vogiatzis, 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Variational Learning on Aggregate Outputs with Gaussian Processes

Kenji Fukumizu, Dino Sejdinovic, Ho Chung Leon Law, 2018, NeurIPS.

Kernel Bayes' rule: Bayesian inference with positive definite kernels

Le Song, Kenji Fukumizu, Arthur Gretton, 2013, J. Mach. Learn. Res..

Recovering Distributions from Gaussian RKHS Embeddings

Kenji Fukumizu, Motonobu Kanagawa, K. Fukumizu, 2014, AISTATS.

Kernel Recursive ABC: Point Estimation with Intractable Likelihood

Kenji Fukumizu, Keisuke Yamazaki, Motonobu Kanagawa, 2018, ICML.

Statistical Consistency of Kernel Canonical Correlation Analysis

Kenji Fukumizu, Arthur Gretton, Francis R. Bach, 2007 .

Smoothness and Stability in GANs pdf

Kenji Fukumizu, Casey Chu, Kentaro Minami, 2020, ICLR.

Reflectance spectra of Asteroids and Meteorites: their classifications and statistical comparisons

K. Fukumizu, S. Akaho, M. Okada, 2018, Journal of Physics: Conference Series.

New Graph Polynomials from the Bethe Approximation of the Ising Partition Function

Kenji Fukumizu, Yusuke Watanabe, K. Fukumizu, 2009, Combinatorics, Probability and Computing.

Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces

K. Fukumizu, M. Imaizumi, 2020, J. Mach. Learn. Res..

Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method

K. Fukumizu, Pengzhou (Abel) Wu, 2020, AISTATS.

Marco Saerens, Kenji Fukumizu, Ikumi Suzuki, 2013, EMNLP.

Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation

Kenji Fukumizu, Yusuke Watanabe, K. Fukumizu, 2009, NIPS.

Parameter estimation for von Mises–Fisher distributions

Shin Ishii, Kenji Fukumizu, Takashi Takenouchi, 2007, Comput. Stat..

Neural Fourier Transform: A General Approach to Equivariant Representation Learning

K. Fukumizu, Takeru Miyato, Masanori Koyama, 2023, ArXiv.

A general upper bound of likelihood ratio for regression

Kenji Fukumizu, Katsuyuki Hagiwara, K. Fukumizu, 2003 .

On minimum spanning tree-like metric spaces

Kenji Fukumizu, Momoko Hayamizu, K. Fukumizu, 2015, Discret. Appl. Math..

A General Class of Transfer Learning Regression without Implementation Cost

K. Fukumizu, Song Liu, Stephen Wu, 2020, AAAI.

Density Estimation in Infinite Dimensional Exponential Families

Aapo Hyvärinen, Kenji Fukumizu, Arthur Gretton, 2013, J. Mach. Learn. Res..

Convergence guarantees for kernel-based quadrature rules in misspecified settings

Kenji Fukumizu, Bharath K. Sriperumbudur, Motonobu Kanagawa, 2016, NIPS.

Effects of molecular diffusion on trapped gas composition in polar ice cores

Kenji Fukumizu, Kenji Kawamura, Takeo Hondoh, 2005 .

Data‐driven taxonomy matching of asteroid and meteorite

Kenji Fukumizu, Hideaki Miyamoto, Takafumi Niihara, 2020, Meteoritics & Planetary Science.

Inference and Learning with Graphical Models

K. Fukumizu, S. Akaho, H. Asoh, 2000 .

Towards Principled Causal Effect Estimation by Deep Identifiable Models

K. Fukumizu, Pengzhou (Abel) Wu, 2021, ArXiv.

β-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap

K. Fukumizu, Pengzhou (Abel) Wu, 2021, ArXiv.

Trimmed Density Ratio Estimation

Akiko Takeda, Kenji Fukumizu, Taiji Suzuki, 2017, NIPS.

The equivalence between Stein variational gradient descent and black-box variational inference pdf

Kenji Fukumizu, Casey Chu, Kentaro Minami, 2020, ICLR 2020.

Characteristic Kernels on Groups and Semigroups

Bernhard Schölkopf, Kenji Fukumizu, Arthur Gretton, 2008, NIPS.

Exchangeable Deep Neural Networks for Set-to-Set Matching and Learning

K. Fukumizu, H. Hachiya, Yuki Saito, 2020, ECCV.

A kernel-based causal learning algorithm

Bernhard Schölkopf, Kenji Fukumizu, Dominik Janzing, 2007, ICML '07.

Intact-VAE: Estimating Treatment Effects under Unobserved Confounding

K. Fukumizu, Pengzhou (Abel) Wu, 2021, 2101.06662.

Intact-VAE: Estimating Treatment Effects under Unobserved Confounding pdf

Kenji Fukumizu, Pengzhou Wu, K. Fukumizu, 2021, ArXiv.

Meta Learning for Causal Direction pdf

Kenji Fukumizu, Jean-Francois Ton, Dino Sejdinovic, 2020, AAAI.

Kernel Method for Persistence Diagrams via Kernel Embedding and Weight Factor

Kenji Fukumizu, Yasuaki Hiraoka, Genki Kusano, 2017, J. Mach. Learn. Res..

Post Selection Inference with Kernels

Kenji Fukumizu, Makoto Yamada, Ichiro Takeuchi, 2016, AISTATS.

Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network

K. Fukumizu, Yuichi Yoshida, S. Maeda, 2023, International Conference on Machine Learning.

Deep Set-to-Set Matching and Learning pdf

Kenji Fukumizu, Hirotaka Hachiya, Yuki Saito, 2019, ArXiv.

Graph polynomials and approximation of partition functions with Loopy Belief Propagation pdf

Kenji Fukumizu, Yusuke Watanabe, K. Fukumizu, 2009, ArXiv.

Out-of-Distribution Optimality of Invariant Risk Minimization

K. Fukumizu, S. Toyota, 2023, Trans. Mach. Learn. Res..

Learning sparse structural changes in high-dimensional Markov networks

Kenji Fukumizu, Taiji Suzuki, Song Liu, 2017, Behaviormetrika.

Characteristic Kernels and Infinitely Divisible Distributions

Kenji Fukumizu, Yu Nishiyama, K. Fukumizu, 2014, J. Mach. Learn. Res..

Scalable Unbalanced Sobolev Transport for Measures on a Graph

K. Fukumizu, Tam Le, Truyen V. Nguyen, 2023, AISTATS.

A Scaling Law for Synthetic-to-Real Transfer: A Measure of Pre-Training pdf

Kenji Fukumizu, Shin-ichi Maeda, Shogo Murai, 2021, ArXiv.

Kernels on Structured Objects Through Nested Histograms

Kenji Fukumizu, Marco Cuturi, K. Fukumizu, 2006, NIPS.

Multiresolution Kernels pdf

Kenji Fukumizu, Marco Cuturi, K. Fukumizu, 2005, ArXiv.

Invariance-adapted decomposition and Lasso-type contrastive learning

K. Fukumizu, Takeru Miyato, Masanori Koyama, 2022, ArXiv.

An Efficient Learning Algorithm Using Naturla Gradient and Second Order Information of Error Surface

Kenji Fukumizu, Shun-ichi Amari, Hyeyoung Park, 2000, PRICAI.

Non-parametric estimation of integral probability metrics

Bernhard Schölkopf, Kenji Fukumizu, Arthur Gretton, 2010, 2010 IEEE International Symposium on Information Theory.

Latent Gaussian process with composite likelihoods and numerical quadrature

K. Fukumizu, A. Banerjee, 2021 .

Painless Embeddings of Distributions: the Function Space View (Part 1)

Alexander J. Smola, K. Fukumizu, A. Gretton, 2008, ICML 2008.

Semigroup Kernels on Measures Kenji Fukumizu Jean-philippe Vert

K. Fukumizu, Marco Cuturi, Jean-Philippe Vert, 2022 .

M L ] 1 4 O ct 2 01 6 Post Selection Inference with Kernels

K. Fukumizu, I. Takeuchi, M. Yamada, 2021 .

Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces

Michael I. Jordan, F. Bach, K. Fukumizu, 2004, J. Mach. Learn. Res..

Model Selection for Simulator-based Statistical Models: A Kernel Approach pdf

Kenji Fukumizu, Motonobu Kanagawa, Takafumi Kajihara, 2019, ArXiv.

Convex covariate clustering for classification

Kenji Fukumizu, Daniel Andrade, Yuzuru Okajima, 2019, Pattern Recognit. Lett..

Convex Feature Clustering and Selection With Class Label Information

K. Fukumizu, Daniel Andrade, 2017 .

Statistical Consistency of Kernel Canonical Correlation Analysis

K. Fukumizu, Arthur Gretton, Francis R. Bach, 2007, J. Mach. Learn. Res..

Equivalence of distance-based and RKHS-based statistics in hypothesis testing

Bharath K. Sriperumbudur, K. Fukumizu, A. Gretton, 2012, ArXiv.

Optimal Transport for Measures with Noisy Tree Metric

K. Fukumizu, Tam Le, Truyen Nguyen, 2023, AISTATS.

Computing functions of random variables via reproducing kernel Hilbert space representations

Bernhard Schölkopf, Kenji Fukumizu, Krikamol Muandet, 2015, Statistics and Computing.

From Black-Box to White-Box: Interpretable Learning with Kernel Machines

Kenji Fukumizu, Hao Zhang, Shinji Nakadai, 2018, MLDM.

Invariance Learning based on Label Hierarchy

K. Fukumizu, S. Toyota, 2022, NeurIPS.

Universality , Characteristic Kernels and RKHS Embedding of Measures

Bharath K. Sriperumbudur, K. Fukumizu, 2011 .