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ORCIDHeishiro Kanagawa
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2020 – today
- 2024
[i9]Congye Wang, Wilson Ye Chen, Heishiro Kanagawa, Chris J. Oates:
Reinforcement Learning for Adaptive MCMC. CoRR abs/2405.13574 (2024)- 2023
- [c9]Jerome Baum, Heishiro Kanagawa, Arthur Gretton:
A Kernel Stein Test of Goodness of Fit for Sequential Models. ICML 2023: 1936-1953 - [c8]Congye Wang, Ye Chen, Heishiro Kanagawa, Chris J. Oates:
Stein Π-Importance Sampling. NeurIPS 2023 - 2022
- [i8]Jerome Baum, Heishiro Kanagawa, Arthur Gretton
:
A kernel Stein test of goodness of fit for sequential models. CoRR abs/2210.10741 (2022) - [i7]Heishiro Kanagawa, Arthur Gretton, Lester Mackey:
Controlling Moments with Kernel Stein Discrepancies. CoRR abs/2211.05408 (2022) - 2021
- [c7]Liyuan Xu, Heishiro Kanagawa, Arthur Gretton:
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation. NeurIPS 2021: 26264-26275 - [i6]Liyuan Xu, Heishiro Kanagawa, Arthur Gretton:
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation. CoRR abs/2106.03907 (2021) - 2020
- [c6]Li K. Wenliang, Theodore H. Moskovitz, Heishiro Kanagawa, Maneesh Sahani:
Amortised Learning by Wake-Sleep. ICML 2020: 10236-10247 - [c5]Wittawat Jitkrittum, Heishiro Kanagawa, Bernhard Schölkopf:
Testing Goodness of Fit of Conditional Density Models with Kernels. UAI 2020: 221-230 - [i5]Li Kevin Wenliang, Theodore H. Moskovitz, Heishiro Kanagawa, Maneesh Sahani:
Amortised Learning by Wake-Sleep. CoRR abs/2002.09737 (2020) - [i4]Wittawat Jitkrittum, Heishiro Kanagawa, Bernhard Schölkopf:
Testing Goodness of Fit of Conditional Density Models with Kernels. CoRR abs/2002.10271 (2020)
2010 – 2019
- 2019
- [c4]Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami, Taiji Suzuki:
Cross-Domain Recommendation via Deep Domain Adaptation. ECIR (2) 2019: 20-29 - [i3]Heishiro Kanagawa, Wittawat Jitkrittum, Lester Mackey, Kenji Fukumizu, Arthur Gretton:
A Kernel Stein Test for Comparing Latent Variable Models. CoRR abs/1907.00586 (2019) - 2018
- [c3]Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton:
Informative Features for Model Comparison. NeurIPS 2018: 816-827 - [i2]Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami, Taiji Suzuki:
Cross-domain Recommendation via Deep Domain Adaptation. CoRR abs/1803.03018 (2018) - [i1]Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton:
Informative Features for Model Comparison. CoRR abs/1810.11630 (2018) - 2016
- [c2]Heishiro Kanagawa, Taiji Suzuki, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami:
Gaussian process nonparametric tensor estimator and its minimax optimality. ICML 2016: 1632-1641 - [c1]Taiji Suzuki, Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami:
Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning. NIPS 2016: 3783-3791
Coauthor Index
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