Gaussian process nonparametric tensor estimator and its minimax optimality. - Heishiro Kanagawa; Taiji Suzuki; Hayato Kobayashi; Nobuyuki Shimizu; Yukihiro Tagami (ICML, 2016)

Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning. - Taiji Suzuki; Heishiro Kanagawa; Hayato Kobayashi; Nobuyuki Shimizu; Yukihiro Tagami (NIPS, 2016)

Informative Features for Model Comparison. - Wittawat Jitkrittum; Heishiro Kanagawa; Patsorn Sangkloy; James Hays; Bernhard Schölkopf; Arthur Gretton (NeurIPS, 2018)

Cross-Domain Recommendation via Deep Domain Adaptation. - Heishiro Kanagawa; Hayato Kobayashi; Nobuyuki Shimizu; Yukihiro Tagami; Taiji Suzuki (ECIR (2), 2019)

A Kernel Stein Test for Comparing Latent Variable Models. - Heishiro Kanagawa; Wittawat Jitkrittum; Lester Mackey; Kenji Fukumizu; Arthur Gretton (CoRR, 2019)

Amortised Learning by Wake-Sleep. - Li K. Wenliang; Theodore H. Moskovitz; Heishiro Kanagawa; Maneesh Sahani (ICML, 2020)

Testing Goodness of Fit of Conditional Density Models with Kernels. - Wittawat Jitkrittum; Heishiro Kanagawa; Bernhard Schölkopf (UAI, 2020)

Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation. - Liyuan Xu; Heishiro Kanagawa; Arthur Gretton (NeurIPS, 2021)

Controlling Moments with Kernel Stein Discrepancies. - Heishiro Kanagawa; Arthur Gretton; Lester Mackey (CoRR, 2022)

A Kernel Stein Test of Goodness of Fit for Sequential Models. - Jerome Baum; Heishiro Kanagawa; Arthur Gretton (ICML, 2023)

Stein Π-Importance Sampling. - Congye Wang; Wilson Ye Chen; Heishiro Kanagawa; Chris J. Oates (NeurIPS, 2023)

Reinforcement Learning for Adaptive MCMC. - Congye Wang; Wilson Ye Chen; Heishiro Kanagawa; Chris J. Oates (AISTATS, 2025)

Stationary MMD Points for Cubature. - Zonghao Chen; Toni Karvonen; Heishiro Kanagawa; François-Xavier Briol; Chris J. Oates (CoRR, 2025)

Harnessing the Power of Reinforcement Learning for Adaptive MCMC. - Congye Wang; Matthew A. Fisher; Heishiro Kanagawa; Wilson Ye Chen; Chris J. Oates (CoRR, 2025)