Kenji Fukumizu

发表

Active Learning in Multilayer Perceptrons

Kenji Fukumizu, K. Fukumizu, 1995, NIPS.

Kernel Bayes' rule: Bayesian inference with positive definite kernels

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

Kernel Dimensionality Reduction for Supervised Learning

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

Probabilistic design of layered neural networks based on their unified framework

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

Kernel Bayes' Rule

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

A Fast, Consistent Kernel Two-Sample Test

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

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

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

Universality, Characteristic Kernels and RKHS Embedding of Measures

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

Convergence guarantees for kernel-based quadrature rules in misspecified settings

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

Kernel Recursive ABC: Point Estimation with Intractable Likelihood

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

Advantage of Deep Neural Networks for Estimating Functions with Singularity on Curves pdf

Kenji Fukumizu, Masaaki Imaizumi, 2020, ArXiv.

Robust Persistence Diagrams using Reproducing Kernels pdf

Kenji Fukumizu, Bharath Sriperumbudur, Siddharth Vishwanath, 2020, 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 approaches to combining binary classifiers for multi-class classification

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

Lazy Transfer Learning pdf

Kenji Fukumizu, Song Liu, 2015, ArXiv.

A General Class of Transfer Learning Regression without Implementation Cost pdf

Kenji Fukumizu, Song Liu, Stephen Wu, 2020, ArXiv.

Taxonomy matching between asteroids and meteorites: supervised clustering approach

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

Statistical Consistency of Kernel Canonical Correlation Analysis

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

Kernel Methods for Statistical Learning

Kenji Fukumizu, K. Fukumizu, 2012 .

Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces

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

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

Deep Neural Networks Learn Non-Smooth Functions Effectively

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

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

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

Tree-Sliced Approximation of Wasserstein Distances pdf

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

Optimal kernel choice for large-scale two-sample tests

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

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

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

Hilbert Space Embeddings of POMDPs

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

Persistence weighted Gaussian kernel for topological data analysis

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

Reducing Hubness for Kernel Regression

Kenji Fukumizu, Ikumi Suzuki, Milos Radovanovic, 2015, SISAP.

Reducing Hubness: A Cause of Vulnerability in Recommender Systems

Kenji Fukumizu, Ikumi Suzuki, Kei Kobayashi, 2015, SIGIR.

A network of chaotic elements for information processing

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

Smoothness and Stability in GANs pdf

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

Support Vector Machines Statistical Inference with Reproducing Kernel Hilbert Space

Kenji Fukumizu, K. Fukumizu, 2008 .

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..

Kernel Measures of Conditional Dependence

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

Gradient-based kernel method for feature extraction and variable selection

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

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

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

On integral probability metrics, φ-divergences and binary classification

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

Local minima and plateaus in hierarchical structures of multilayer perceptrons

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

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

Kenji Fukumizu, Hirotaka Hachiya, Yuki Saito, 2020, ECCV.

Local minima and plateaus in multilayer neural networks

Kenji Fukumizu, Shun-ichi Amari, 1999 .

Kernel-Based Information Criterion

Kenji Fukumizu, Faustino Gomez, Somayeh Danafar, 2015, Comput. Inf. Sci..

Learning sparse structural changes in high-dimensional Markov networks

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

Kernel Method for Persistence Diagrams via Kernel Embedding and Weight Factor

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

Dynamics of Batch Learning in Multilayer Neural Networks

Kenji Fukumizu, 1998 .

A Characterization of Minimum Spanning Tree-Like Metric Spaces

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

Kernel Mean Embedding of Distributions: A Review and Beyonds pdf

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

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

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

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

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

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

Kenji Fukumizu, Pengzhou Wu, 2020, AISTATS.

Learning from Distributions via Support Measure Machines

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

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

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

Hypothesis testing using pairwise distances and associated kernels pdf

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

Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions

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

Convex covariate clustering for classification

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

A Kernel Statistical Test of Independence

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

Gradient-based kernel dimension reduction for supervised learning pdf

Kenji Fukumizu, Chenlei Leng, 2011, ArXiv.

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

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

Filtering with State-Observation Examples via Kernel Monte Carlo Filter

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

Kernel Mean Shrinkage Estimators pdf

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

Adaptive natural gradient learning algorithms for various stochastic models

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

Associative memory using spatiotemporal chaos

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

Lazy Transfer Leanring

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

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

Le Song, Kenji Fukumizu, Arthur Gretton, 2013 .

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

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

Higher-Order Regularized Kernel Canonical Correlation Analysis

Kenji Fukumizu, Md. Ashad Alam, 2015, Int. J. Pattern Recognit. Artif. Intell..

Hyperparameter Selection in Kernel Principal Component Analysis

Kenji Fukumizu, Md. Ashad Alam, 2014, J. Comput. Sci..

Tree-Sliced Variants of Wasserstein Distances

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

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

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

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

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

A Linear-Time Kernel Goodness-of-Fit Test

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

Deep Set-to-Set Matching and Learning pdf

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

Structure Learning of Partitioned Markov Networks

Masashi Sugiyama, Kenji Fukumizu, Taiji Suzuki, 2016, ICML.

Statistical Convergence of Kernel CCA

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

Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions pdf

Kentaro Inui, Sho Yokoi, Kenji Fukumizu, 2018, EMNLP.

Influence function and robust variant of kernel canonical correlation analysis

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

Multiresolution Kernels pdf

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

Flattening the Density Gradient for Eliminating Spatial Centrality to Reduce Hubness

Kenji Fukumizu, Ikumi Suzuki, Milos Radovanovic, 2016, AAAI.

On Learning Sparse Changes of Markov Networks

Masashi Sugiyama, Kenji Fukumizu, Taiji Suzuki, 2014 .

Intact-VAE: Estimating Treatment Effects under Unobserved Confounding pdf

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

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

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

Statistical active learning in multilayer perceptrons

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

Probabilistic design

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

On minimum spanning tree-like metric spaces

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

Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective

Kenji Fukumizu, Song Liu, 2016, SDM.

Adaptive Method of Realizing Natural Gradient Learning for Multilayer Perceptrons

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

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.

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

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

Understanding GANs via Generalization Analysis for Disconnected Support

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

Post Selection Inference with Kernels

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

Hilbert Space Embeddings and Metrics on Probability Measures

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

Multilocus phylogenetic analysis with gene tree clustering

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

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..

Monte Carlo Filtering Using Kernel Embedding of Distributions

Kenji Fukumizu, Arthur Gretton, Yu Nishiyama, 2014, AAAI.

Characteristic Kernels and Infinitely Divisible Distributions

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

Effect of Batch Learning in Multilayer Neural Networks

Kenji Fukumizu, K. Fukumizu, 1998, ICONIP.

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

Akiko Takeda, Kenji Fukumizu, Daniel Andrade, 2020, Stat. Comput..

Consistency of Kernel Canonical Correlation Analysis

Kenji Fukumizu, Arthur Gretton, Francis Bach, 2005 .

Density Estimation in Infinite Dimensional Exponential Families

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

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

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

Recovering Distributions from Gaussian RKHS Embeddings

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

A Comparative Study of Kernel and Robust Canonical Correlation Analysis

Kenji Fukumizu, Mohammed Nasser, Md. Ashad Alam, 2010, J. Multim..

Kernels on Structured Objects Through Nested Histograms

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

Support Vector Machine II Statistical Data Analysis with Positive Definite Kernels

Kenji Fukumizu, K. Fukumizu, 2008 .

Relation between weight size and degree of over-fitting in neural network regression

Kenji Fukumizu, Katsuyuki Hagiwara, 2008, Neural Networks.

Variational Learning on Aggregate Outputs with Gaussian Processes

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

Non-parametric estimation of integral probability metrics

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

Meta Learning for Causal Direction pdf

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

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

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

Generalization Error of Limear Neural Networks in Unidentifiable Cases

Kenji Fukumizu, K. Fukumizu, 1999, ALT.

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.

Hilbert space embeddings of conditional distributions with applications to dynamical systems

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

Parameter estimation for von Mises–Fisher distributions

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

Kernel Mean Estimation and Stein Effect

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

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

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

Characteristic Kernels on Groups and Semigroups

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

Injective Hilbert Space Embeddings of Probability Measures

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

Learning low-rank output kernels

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

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

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

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

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

A kernel-based causal learning algorithm

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

Higher-Order Regularized Kernel CCA

Kenji Fukumizu, Md. Ashad Alam, 2013, 2013 12th International Conference on Machine Learning and Applications.

Trimmed Density Ratio Estimation

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

Computing functions of random variables via reproducing kernel Hilbert space representations

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

Localized Centering: Reducing Hubness in Large-Sample Data

Kenji Fukumizu, Ikumi Suzuki, Masashi Shimbo, 2015, AAAI.

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

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

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

Kenji Fukumizu, Arthur Gretton, Alexander J. Smola, 2008, ICML 2008.

Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator

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

Meta Learning for Causal Direction

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

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

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

Towards Principled Causal Effect Estimation by Deep Identifiable Models pdf

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

A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?

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

A general upper bound of likelihood ratio for regression

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

Data‐driven taxonomy matching of asteroid and meteorite

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

Robust Density Ratio Estimation: Trimming the Likelihood Ratio

Akiko Takeda, Kenji Fukumizu, Taiji Suzuki, 2017 .

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).

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

Kenji Fukumizu, K. Fukumizu, 2000 .

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

Kenji Fukumizu, Kenji Kawamura, Takeo Hondoh, 2005 .

Model-based Kernel Sum Rule

Kenji Fukumizu, Arthur Gretton, Motonobu Kanagawa, 2014 .

混合 von Mises-Fisher 分布に対する変分法的ベイズ推定 Variational Bayes Inference of Mixtured von Mises-Fisher Distribution

Shin Ishii, Kenji Fukumizu, Shigeyuki Oba, 2004 .

Statistical Estimation in Singular Models.

Kenji Fukumizu, 2003 .