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Kenji Fukumizu
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2020 – today
- 2024
- [j44]Shoji Toyota, Kenji Fukumizu:
Out-of-Distribution Optimality of Invariant Risk Minimization. Trans. Mach. Learn. Res. 2024 (2024) - [c71]Tam Le, Truyen Nguyen, Kenji Fukumizu:
Optimal Transport for Measures with Noisy Tree Metric. AISTATS 2024: 3115-3123 - [c70]Masanori Koyama, Kenji Fukumizu, Kohei Hayashi, Takeru Miyato:
Neural Fourier Transform: A General Approach to Equivariant Representation Learning. ICLR 2024 - [c69]Tam Le, Truyen Nguyen, Kenji Fukumizu:
Generalized Sobolev Transport for Probability Measures on a Graph. ICML 2024 - [c68]Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic:
Neural-Kernel Conditional Mean Embeddings. ICML 2024 - [i57]Tam Le, Truyen Nguyen, Kenji Fukumizu:
Generalized Sobolev Transport for Probability Measures on a Graph. CoRR abs/2402.04516 (2024) - [i56]Noboru Isobe, Masanori Koyama, Kohei Hayashi, Kenji Fukumizu:
Extended Flow Matching: a Method of Conditional Generation with Generalized Continuity Equation. CoRR abs/2402.18839 (2024) - [i55]Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic:
Neural-Kernel Conditional Mean Embeddings. CoRR abs/2403.10859 (2024) - [i54]Yuto Tanimoto, Kenji Fukumizu:
State-Separated SARSA: A Practical Sequential Decision-Making Algorithm with Recovering Rewards. CoRR abs/2403.11520 (2024) - [i53]Kenji Fukumizu, Taiji Suzuki, Noboru Isobe, Kazusato Oko, Masanori Koyama:
Flow matching achieves minimax optimal convergence. CoRR abs/2405.20879 (2024) - [i52]Shunya Minami, Yoshihiro Hayashi, Stephen Wu, Kenji Fukumizu, Hiroki Sugisawa, Masashi Ishii, Isao Kuwajima, Kazuya Shiratori, Ryo Yoshida:
Scaling Law of Sim2Real Transfer Learning in Expanding Computational Materials Databases for Real-World Predictions. CoRR abs/2408.04042 (2024) - [i51]Manuel Glöckler, Shoji Toyota, Kenji Fukumizu, Jakob H. Macke:
Compositional simulation-based inference for time series. CoRR abs/2411.02728 (2024) - 2023
- [c67]Tam Le, Truyen Nguyen, Kenji Fukumizu:
Scalable Unbalanced Sobolev Transport for Measures on a Graph. AISTATS 2023: 8521-8560 - [c66]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: 17041-17060 - [c65]Shunya Minami, Kenji Fukumizu, Yoshihiro Hayashi, Ryo Yoshida:
Transfer Learning with Affine Model Transformation. NeurIPS 2023 - [i50]Tam Le, Truyen Nguyen, Kenji Fukumizu:
Scalable Unbalanced Sobolev Transport for Measures on a Graph. CoRR abs/2302.12498 (2023) - [i49]Yuri Kinoshita, Kenta Oono, Kenji Fukumizu, Yuichi Yoshida, Shin-ichi Maeda:
Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network. CoRR abs/2304.12770 (2023) - [i48]Masanori Koyama, Kenji Fukumizu, Kohei Hayashi, Takeru Miyato:
Neural Fourier Transform: A General Approach to Equivariant Representation Learning. CoRR abs/2305.18484 (2023) - [i47]Shoji Toyota, Kenji Fukumizu:
Out-of-Distribution Optimality of Invariant Risk Minimization. CoRR abs/2307.11972 (2023) - [i46]Tam Le, Truyen Nguyen, Kenji Fukumizu:
Optimal Transport for Measures with Noisy Tree Metric. CoRR abs/2310.13653 (2023) - 2022
- [j43]Hironori Murase
, Kenji Fukumizu
:
ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables. IEEE Access 10: 44259-44270 (2022) - [j42]Masaaki Imaizumi, Kenji Fukumizu:
Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces. J. Mach. Learn. Res. 23: 111:1-111:54 (2022) - [c64]Pengzhou Abel Wu, Kenji Fukumizu:
$\beta$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap. ICLR 2022 - [c63]Takeru Miyato, Masanori Koyama, Kenji Fukumizu:
Unsupervised Learning of Equivariant Structure from Sequences. NeurIPS 2022 - [c62]Shoji Toyota, Kenji Fukumizu:
Invariance Learning based on Label Hierarchy. NeurIPS 2022 - [c61]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: 477-492 - [i45]Hironori Murase, Kenji Fukumizu:
ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables. CoRR abs/2202.10281 (2022) - [i44]Shoji Toyota, Kenji Fukumizu:
Invariance Learning based on Label Hierarchy. CoRR abs/2203.15549 (2022) - [i43]Siddharth Vishwanath, Bharath K. Sriperumbudur, Kenji Fukumizu, Satoshi Kuriki:
Robust Topological Inference in the Presence of Outliers. CoRR abs/2206.01795 (2022) - [i42]Takeru Miyato, Masanori Koyama, Kenji Fukumizu:
Unsupervised Learning of Equivariant Structure from Sequences. CoRR abs/2210.05972 (2022) - [i41]Masanori Koyama, Takeru Miyato, Kenji Fukumizu:
Invariance-adapted decomposition and Lasso-type contrastive learning. CoRR abs/2210.07413 (2022) - [i40]Shunya Minami, Kenji Fukumizu, Yoshihiro Hayashi, Ryo Yoshida:
Transfer learning with affine model transformation. CoRR abs/2210.09745 (2022) - 2021
- [j41]Daniel Andrade, Kenji Fukumizu
, Yuzuru Okajima:
Convex covariate clustering for classification. Pattern Recognit. Lett. 151: 193-199 (2021) - [c60]Shunya Minami, Song Liu, Stephen Wu, Kenji Fukumizu, Ryo Yoshida:
A General Class of Transfer Learning Regression without Implementation Cost. AAAI 2021: 8992-8999 - [c59]Jean-François Ton, Dino Sejdinovic, Kenji Fukumizu:
Meta Learning for Causal Direction. AAAI 2021: 9897-9905 - [e1]Arindam Banerjee, Kenji Fukumizu:
The 24th International Conference on Artificial Intelligence and Statistics, AISTATS 2021, April 13-15, 2021, Virtual Event. Proceedings of Machine Learning Research 130, PMLR 2021 [contents] - [i39]Pengzhou Wu, Kenji Fukumizu:
Identifying Treatment Effects under Unobserved Confounding by Causal Representation Learning. CoRR abs/2101.06662 (2021) - [i38]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 abs/2108.11018 (2021) - [i37]Pengzhou Wu, Kenji Fukumizu:
Towards Principled Causal Effect Estimation by Deep Identifiable Models. CoRR abs/2109.15062 (2021) - [i36]Pengzhou Wu, Kenji Fukumizu:
β-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap. CoRR abs/2110.05225 (2021) - 2020
- [j40]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: 107039 (2020) - [j39]Motonobu Kanagawa
, Bharath K. Sriperumbudur, Kenji Fukumizu
:
Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings. Found. Comput. Math. 20(1): 155-194 (2020) - [j38]Yu Nishiyama
, Motonobu Kanagawa
, Arthur Gretton
, Kenji Fukumizu
:
Model-based kernel sum rule: kernel Bayesian inference with probabilistic models. Mach. Learn. 109(5): 939-972 (2020) - [j37]Daniel Andrade
, Akiko Takeda, Kenji Fukumizu
:
Robust Bayesian model selection for variable clustering with the Gaussian graphical model. Stat. Comput. 30(2): 351-376 (2020) - [j36]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): 1222-1230 (2020) - [c58]Pengzhou Wu, Kenji Fukumizu:
Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method. AISTATS 2020: 1157-1167 - [c57]Yuki Saito
, Takuma Nakamura
, Hirotaka Hachiya
, Kenji Fukumizu
:
Exchangeable Deep Neural Networks for Set-to-Set Matching and Learning. ECCV (17) 2020: 626-646 - [c56]Casey Chu, Kentaro Minami, Kenji Fukumizu:
Smoothness and Stability in GANs. ICLR 2020 - [c55]Siddharth Vishwanath, Kenji Fukumizu, Satoshi Kuriki, Bharath K. Sriperumbudur:
Robust Persistence Diagrams using Reproducing Kernels. NeurIPS 2020 - [i35]Pengzhou Wu, Kenji Fukumizu:
Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method. CoRR abs/2001.01894 (2020) - [i34]Casey Chu, Kentaro Minami, Kenji Fukumizu:
Smoothness and Stability in GANs. CoRR abs/2002.04185 (2020) - [i33]Casey Chu, Kentaro Minami, Kenji Fukumizu:
The equivalence between Stein variational gradient descent and black-box variational inference. CoRR abs/2004.01822 (2020) - [i32]Siddharth Vishwanath, Kenji Fukumizu, Satoshi Kuriki, Bharath K. Sriperumbudur:
Robust Persistence Diagrams using Reproducing Kernels. CoRR abs/2006.10012 (2020) - [i31]Shunya Minami, Song Liu, Stephen Wu, Kenji Fukumizu, Ryo Yoshida:
A General Class of Transfer Learning Regression without Implementation Cost. CoRR abs/2006.13228 (2020) - [i30]Jean-Francois Ton, Dino Sejdinovic, Kenji Fukumizu:
Meta Learning for Causal Direction. CoRR abs/2007.02809 (2020) - [i29]Masaaki Imaizumi, Kenji Fukumizu:
Advantage of Deep Neural Networks for Estimating Functions with Singularity on Curves. CoRR abs/2011.02256 (2020)
2010 – 2019
- 2019
- [j35]Ruriko Yoshida, Kenji Fukumizu
, Chrysafis Vogiatzis
:
Multilocus phylogenetic analysis with gene tree clustering. Ann. Oper. Res. 276(1-2): 293-313 (2019) - [c54]Masaaki Imaizumi, Kenji Fukumizu:
Deep Neural Networks Learn Non-Smooth Functions Effectively. AISTATS 2019: 869-878 - [c53]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 - [c52]Tam Le, Makoto Yamada, Kenji Fukumizu, Marco Cuturi:
Tree-Sliced Variants of Wasserstein Distances. NeurIPS 2019: 12283-12294 - [c51]Kenji Fukumizu, Shoichiro Yamaguchi, Yoh-ichi Mototake, Mirai Tanaka:
Semi-flat minima and saddle points by embedding neural networks to overparameterization. NeurIPS 2019: 13845-13853 - [i28]Tam Le, Makoto Yamada, Kenji Fukumizu, Marco Cuturi:
Tree-Sliced Approximation of Wasserstein Distances. CoRR abs/1902.00342 (2019) - [i27]Takafumi Kajihara, Motonobu Kanagawa, Yuuki Nakaguchi, Kanishka Khandelwal, Kenji Fukumizu:
Model Selection for Simulator-based Statistical Models: A Kernel Approach. CoRR abs/1902.02517 (2019) - [i26]Kenji Fukumizu, Shoichiro Yamaguchi, Yoh-ichi Mototake, Mirai Tanaka:
Semi-flat minima and saddle points by embedding neural networks to overparameterization. CoRR abs/1906.04868 (2019) - [i25]Heishiro Kanagawa, Wittawat Jitkrittum, Lester Mackey, Kenji Fukumizu, Arthur Gretton:
A Kernel Stein Test for Comparing Latent Variable Models. CoRR abs/1907.00586 (2019) - [i24]Yuki Saito, Takuma Nakamura, Hirotaka Hachiya, Kenji Fukumizu:
Deep Set-to-Set Matching and Learning. CoRR abs/1910.09972 (2019) - 2018
- [j34]Md. Ashad Alam
, Kenji Fukumizu
, Yu-Ping Wang:
Influence function and robust variant of kernel canonical correlation analysis. Neurocomputing 304: 12-29 (2018) - [c50]Makoto Yamada, Yuta Umezu, Kenji Fukumizu, Ichiro Takeuchi:
Post Selection Inference with Kernels. AISTATS 2018: 152-160 - [c49]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: 1763-1775 - [c48]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 - [c47]Takafumi Kajihara, Motonobu Kanagawa, Keisuke Yamazaki, Kenji Fukumizu:
Kernel Recursive ABC: Point Estimation with Intractable Likelihood. ICML 2018: 2405-2414 - [c46]Hao Zhang, Shinji Nakadai, Kenji Fukumizu
:
From Black-Box to White-Box: Interpretable Learning with Kernel Machines. MLDM (1) 2018: 213-227 - [c45]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: 6084-6094 - [i23]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 abs/1802.05411 (2018) - [i22]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 abs/1805.08463 (2018) - [i21]Sho Yokoi, Sosuke Kobayashi, Kenji Fukumizu, Jun Suzuki, Kentaro Inui:
Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions. CoRR abs/1809.00800 (2018) - 2017
- [j33]Momoko Hayamizu
, Kenji Fukumizu
:
On minimum spanning tree-like metric spaces. Discret. Appl. Math. 226: 51-57 (2017) - [j32]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): 350-370 (2017) - [j31]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): 1-141 (2017) - [j30]Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, Revant Kumar:
Density Estimation in Infinite Dimensional Exponential Families. J. Mach. Learn. Res. 18: 57:1-57:59 (2017) - [j29]Genki Kusano, Kenji Fukumizu, Yasuaki Hiraoka:
Kernel Method for Persistence Diagrams via Kernel Embedding and Weight Factor. J. Mach. Learn. Res. 18: 189:1-189:41 (2017) - [j28]Momoko Hayamizu, Hiroshi Endo, Kenji Fukumizu
:
A Characterization of Minimum Spanning Tree-Like Metric Spaces. IEEE ACM Trans. Comput. Biol. Bioinform. 14(2): 468-471 (2017) - [c44]Wittawat Jitkrittum, Wenkai Xu, Zoltán Szabó, Kenji Fukumizu, Arthur Gretton:
A Linear-Time Kernel Goodness-of-Fit Test. NIPS 2017: 262-271 - [c43]Song Liu, Akiko Takeda, Taiji Suzuki, Kenji Fukumizu:
Trimmed Density Ratio Estimation. NIPS 2017: 4518-4528 - [i20]Wittawat Jitkrittum, Wenkai Xu, Zoltán Szabó, Kenji Fukumizu, Arthur Gretton:
A Linear-Time Kernel Goodness-of-Fit Test. CoRR abs/1705.07673 (2017) - [i19]Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu:
Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings. CoRR abs/1709.00147 (2017) - 2016
- [j27]Krikamol Muandet, Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf:
Kernel Mean Shrinkage Estimators. J. Mach. Learn. Res. 17: 48:1-48:41 (2016) - [j26]Yu Nishiyama, Kenji Fukumizu:
Characteristic Kernels and Infinitely Divisible Distributions. J. Mach. Learn. Res. 17: 180:1-180:28 (2016) - [j25]Motonobu Kanagawa
, Yu Nishiyama, Arthur Gretton
, Kenji Fukumizu
:
Filtering with State-Observation Examples via Kernel Monte Carlo Filter. Neural Comput. 28(2): 382-444 (2016) - [c42]Kazuo Hara, Ikumi Suzuki, Kei Kobayashi, Kenji Fukumizu, Milos Radovanovic:
Flattening the Density Gradient for Eliminating Spatial Centrality to Reduce Hubness. AAAI 2016: 1659-1665 - [c41]Song Liu, Taiji Suzuki, Masashi Sugiyama, Kenji Fukumizu:
Structure Learning of Partitioned Markov Networks. ICML 2016: 439-448 - [c40]Genki Kusano, Yasuaki Hiraoka, Kenji Fukumizu:
Persistence weighted Gaussian kernel for topological data analysis. ICML 2016: 2004-2013 - [c39]Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu:
Convergence guarantees for kernel-based quadrature rules in misspecified settings. NIPS 2016: 3288-3296 - [c38]Song Liu, Kenji Fukumizu:
Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective. SDM 2016: 747-755 - [i18]Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Bernhard Schölkopf:
Kernel Mean Embedding of Distributions: A Review and Beyonds. CoRR abs/1605.09522 (2016) - 2015
- [j24]Somayeh Danafar, Kenji Fukumizu, Faustino Gomez:
Kernel-Based Information Criterion. Comput. Inf. Sci. 8(1): 10-24 (2015) - [j23]Md. Ashad Alam
, Kenji Fukumizu
:
Higher-Order Regularized Kernel Canonical Correlation Analysis. Int. J. Pattern Recognit. Artif. Intell. 29(4): 1551005:1-1551005:24 (2015) - [j22]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): 755-766 (2015) - [c37]Kazuo Hara, Ikumi Suzuki, Masashi Shimbo, Kei Kobayashi, Kenji Fukumizu, Milos Radovanovic:
Localized Centering: Reducing Hubness in Large-Sample Data. AAAI 2015: 2645-2651 - [c36]Kazuo Hara, Ikumi Suzuki, Kei Kobayashi, Kenji Fukumizu:
Reducing Hubness: A Cause of Vulnerability in Recommender Systems. SIGIR 2015: 815-818 - [c35]Kazuo Hara, Ikumi Suzuki, Kei Kobayashi, Kenji Fukumizu
, Milos Radovanovic
:
Reducing Hubness for Kernel Regression. SISAP 2015: 339-344 - [i17]Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Jonas Peters:
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations. CoRR abs/1501.06794 (2015) - [i16]Song Liu, Kenji Fukumizu:
Lazy Transfer Learning. CoRR abs/1506.02784 (2015) - [i15]Momoko Hayamizu, Hiroshi Endo, Kenji Fukumizu:
A characterization of minimum spanning tree-like metric spaces. CoRR abs/1510.09155 (2015) - 2014
- [j21]Md. Ashad Alam
, Kenji Fukumizu
:
Hyperparameter Selection in Kernel Principal Component Analysis. J. Comput. Sci. 10(7): 1139-1150 (2014) - [c34]Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu:
Monte Carlo Filtering Using Kernel Embedding of Distributions. AAAI 2014: 1897-1903 - [c33]Motonobu Kanagawa, Kenji Fukumizu:
Recovering Distributions from Gaussian RKHS Embeddings. AISTATS 2014: 457-465 - [c32]Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf:
Kernel Mean Estimation and Stein Effect. ICML 2014: 10-18 - [i14]Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf:
Kernel Mean Shrinkage Estimators. CoRR abs/1405.5505 (2014) - [i13]Pierre Baldi, Kenji Fukumizu, Tomaso A. Poggio:
Deep Learning: Theory, Algorithms, and Applications (NII Shonan Meeting 2014-5). NII Shonan Meet. Rep. 2014 (2014) - 2013
- [j20]Kenji Fukumizu, Le Song, Arthur Gretton:
Kernel Bayes' rule: Bayesian inference with positive definite kernels. J. Mach. Learn. Res. 14(1): 3753-3783 (2013) - [j19]Klaus-Robert Müller
, Tülay Adali, Kenji Fukumizu
, José C. Príncipe, Sergios Theodoridis:
Special Issue on Advances in Kernel-Based Learning for Signal Processing [From the Guest Editors]. IEEE Signal Process. Mag. 30(4): 14-15 (2013) - [j18]Le Song, Kenji Fukumizu
, Arthur Gretton
:
Kernel Embeddings of Conditional Distributions: A Unified Kernel Framework for Nonparametric Inference in Graphical Models. IEEE Signal Process. Mag. 30(4): 98-111 (2013) - [c31]Ikumi Suzuki, Kazuo Hara, Masashi Shimbo, Marco Saerens, Kenji Fukumizu:
Centering Similarity Measures to Reduce Hubs. EMNLP 2013: 613-623 - [c30]Md. Ashad Alam
, Kenji Fukumizu:
Higher-Order Regularized Kernel CCA. ICMLA (1) 2013: 374-377 - [i12]Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf:
Kernel Mean Estimation and Stein's Effect. CoRR abs/1306.0842 (2013) - 2012
- [c29]Dino Sejdinovic, Arthur Gretton, Bharath K. Sriperumbudur, Kenji Fukumizu:
Hypothesis testing using pairwise distances and associated kernels. ICML 2012 - [c28]Krikamol Muandet, Kenji Fukumizu, Francesco Dinuzzo, Bernhard Schölkopf:
Learning from Distributions via Support Measure Machines. NIPS 2012: 10-18 - [c27]Arthur Gretton, Bharath K. Sriperumbudur, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu:
Optimal kernel choice for large-scale two-sample tests. NIPS 2012: 1214-1222 - [c26]Kenji Fukumizu, Chenlei Leng:
Gradient-based kernel method for feature extraction and variable selection. NIPS 2012: 2123-2131 - [c25]