← Authors

Kenji Fukumizu

61 papers · 2004–2026 · 10 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🌈 Renaissance Researcher (9) 🐝 Cross-Pollinator (13) πŸƒ Academic Marathon (21) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (9) 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (27) 🌟 Keyword Trendsetter Combo (8) 🏠 Conference Loyalist (22) πŸ† Keyword Champion 🧬 Topic Evolution 🀝 Dynamic Duo (14) πŸ† Grand Slam πŸ”¬ Deep Specialist (13) 🌱 Topic Pioneer ⚑ Prolific Year (5) πŸ”₯ Unstoppable (11) πŸ’Ž Century Club (60) πŸš€ Conference Pioneer πŸ—ƒοΈ Keyword Collector (119) πŸ“ˆ Trend Setter

Conferences

NIPS (22) JMLR (11) ICML (8) AISTATS (7) ICLR (6) AAAI (2) EMNLP (2) ACML (1) EACL (1) ECCV (1)

Top co-authors

Arthur Gretton (14) Bharath K. Sriperumbudur (9) Bernhard SchΓΆlkopf (7) Tam Le (5) Bharath Sriperumbudur (4) Dino Sejdinovic (4) Truyen Nguyen (4) Taiji Suzuki (3) Makoto Yamada (3) Song Liu (3)

Research topics

Statistics (2) Probability (2)

Keywords

kernel methods (22) reproducing kernel hilbert space (17) characteristic kernel (6) kernel mean (5) topological data analysis (4) probability measure (4) maximum mean discrepancy (4) probability distribution (4) hilbert-schmidt independence criterion (3) two-sample test (3) nonparametric inference (3) transfer learning (3) optimal transport (3) bayesian inference (3) domain adaptation (3) kernel embedding (3) persistence diagram (3) bayesian computation (2) causal inference (2) wasserstein distance (2)

Papers

Look Before You Leap: A Lookahead Reasoning Quality Gate for Speculative Decoding EACL 2026
Scalable Sobolev IPM for Probability Measures on a Graph ICML 2025
Flow matching achieves almost minimax optimal convergence ICLR 2025
Compositional simulation-based inference for time series ICLR 2025
Generalized Sobolev Transport for Probability Measures on a Graph ICML 2024
Optimal Transport for Measures with Noisy Tree Metric AISTATS 2024
Neural-Kernel Conditional Mean Embeddings ICML 2024
Neural Fourier Transform: A General Approach to Equivariant Representation Learning ICLR 2024
Scalable Unbalanced Sobolev Transport for Measures on a Graph AISTATS 2023
Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network ICML 2023
Transfer Learning with Affine Model Transformation NIPS 2023
$\beta$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap ICLR 2022
Unsupervised Learning of Equivariant Structure from Sequences NIPS 2022
Invariance Learning based on Label Hierarchy NIPS 2022
Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces JMLR 2022
Meta Learning for Causal Direction AAAI 2021
A General Class of Transfer Learning Regression without Implementation Cost AAAI 2021
Exchangeable Deep Neural Networks for Set-to-Set Matching and Learning ECCV 2020
Smoothness and Stability in GANs ICLR 2020
Robust Persistence Diagrams using Reproducing Kernels NIPS 2020
Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method AISTATS 2020
Tree-Sliced Variants of Wasserstein Distances NIPS 2019
Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator ICLR 2019
Deep Neural Networks Learn Non-Smooth Functions Effectively AISTATS 2019
Semi-flat minima and saddle points by embedding neural networks to overparameterization NIPS 2019
Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions EMNLP 2018
Post Selection Inference with Kernels AISTATS 2018
Variational Learning on Aggregate Outputs with Gaussian Processes NIPS 2018
Kernel Method for Persistence Diagrams via Kernel Embedding and Weight Factor JMLR 2018
Kernel Recursive ABC: Point Estimation with Intractable Likelihood ICML 2018
Trimmed Density Ratio Estimation NIPS 2017
Density Estimation in Infinite Dimensional Exponential Families JMLR 2017
A Linear-Time Kernel Goodness-of-Fit Test NIPS 2017
Structure Learning of Partitioned Markov Networks ICML 2016
Convergence guarantees for kernel-based quadrature rules in misspecified settings NIPS 2016
Characteristic Kernels and Infinitely Divisible Distributions JMLR 2016
Kernel Mean Shrinkage Estimators JMLR 2016
Persistence weighted Gaussian kernel for topological data analysis ICML 2016
Kernel Mean Estimation and Stein Effect ICML 2014
Recovering Distributions from Gaussian RKHS Embeddings AISTATS 2014
Centering Similarity Measures to Reduce Hubs EMNLP 2013
Kernel Bayes' Rule: Bayesian Inference with Positive Definite Kernels JMLR 2013
Gradient-based kernel method for feature extraction and variable selection NIPS 2012
Optimal kernel choice for large-scale two-sample tests NIPS 2012
Learning from Distributions via Support Measure Machines NIPS 2012
Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint NIPS 2011
Kernel Bayes' Rule NIPS 2011
Learning low-rank output kernels ACML 2011
Universality, Characteristic Kernels and RKHS Embedding of Measures JMLR 2011
On the relation between universality, characteristic kernels and RKHS embedding of measures AISTATS 2010
Hilbert Space Embeddings and Metrics on Probability Measures JMLR 2010
Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation NIPS 2009
A Fast, Consistent Kernel Two-Sample Test NIPS 2009
Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions NIPS 2009
Characteristic Kernels on Groups and Semigroups NIPS 2008
Statistical Consistency of Kernel Canonical Correlation Analysis JMLR 2007
Kernel Measures of Conditional Dependence NIPS 2007
A Kernel Statistical Test of Independence NIPS 2007
Kernels on Structured Objects Through Nested Histograms NIPS 2006
Semigroup Kernels on Measures JMLR 2005
Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces JMLR 2004