| A fully adaptive algorithm for pure exploration in linear bandits L Xu, J Honda, M Sugiyama International Conference on Artificial Intelligence and Statistics, 843-851, 2018 | 96 | 2018 |
| Learning deep features in instrumental variable regression L Xu, Y Chen, S Srinivasan, N de Freitas, A Doucet, A Gretton arXiv preprint arXiv:2010.07154, 2020 | 71 | 2020 |
| Deep proxy causal learning and its application to confounded bandit policy evaluation L Xu, H Kanagawa, A Gretton Advances in Neural Information Processing Systems 34, 26264-26275, 2021 | 36 | 2021 |
| Kernel methods for causal functions: dose, heterogeneous and incremental response curves R Singh, L Xu, A Gretton Biometrika 111 (2), 497-516, 2024 | 21 | 2024 |
| Polynomial-time algorithms for multiple-arm identification with full-bandit feedback Y Kuroki, L Xu, A Miyauchi, J Honda, M Sugiyama Neural Computation 32 (9), 1733-1773, 2020 | 17 | 2020 |
| Kernel methods for multistage causal inference: Mediation analysis and dynamic treatment effects R Singh, L Xu, A Gretton arXiv preprint arXiv:2111.03950, 2021 | 15 | 2021 |
| Similarity-based classification: Connecting similarity learning to binary classification H Bao, T Shimada, L Xu, I Sato, M Sugiyama arXiv preprint arXiv:2006.06207, 2020 | 15 | 2020 |
| On instrumental variable regression for deep offline policy evaluation Y Chen, L Xu, C Gulcehre, T Le Paine, A Gretton, N De Freitas, A Doucet Journal of Machine Learning Research 23 (302), 1-40, 2022 | 14 | 2022 |
| Uncoupled regression from pairwise comparison data L Xu, J Honda, G Niu, M Sugiyama Advances in Neural Information Processing Systems 32, 2019 | 14 | 2019 |
| Alternate estimation of a classifier and the class-prior from positive and unlabeled data M Kato, L Xu, G Niu, M Sugiyama arXiv preprint arXiv:1809.05710, 2018 | 13 | 2018 |
| Kernel methods for policy evaluation: Treatment effects, mediation analysis, and off-policy planning R Singh, L Xu, A Gretton arXiv preprint arXiv:2010.04855 725, 2020 | 11 | 2020 |
| Pairwise supervision can provably elicit a decision boundary H Bao, T Shimada, L Xu, I Sato, M Sugiyama arXiv preprint arXiv:2006.06207, 2020 | 10 | 2020 |
| Reproducing kernel methods for nonparametric and semiparametric treatment effects R Singh, L Xu, A Gretton arXiv preprint arXiv:2010.04855, 2020 | 7 | 2020 |
| A neural mean embedding approach for back-door and front-door adjustment L Xu, A Gretton arXiv preprint arXiv:2210.06610, 2022 | 6 | 2022 |
| Polynomial-time algorithms for combinatorial pure exploration with full-bandit feedback Y Kuroki, L Xu, A Miyauchi, J Honda, M Sugiyama arXiv preprint arXiv:1902.10582, 2019 | 5 | 2019 |
| Dueling bandits with qualitative feedback L Xu, J Honda, M Sugiyama Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 5549-5556, 2019 | 4 | 2019 |
| Importance weighted kernel Bayes’ rule L Xu, Y Chen, A Doucet, A Gretton International Conference on Machine Learning, 24524-24538, 2022 | 3 | 2022 |
| Generalized kernel ridge regression for nonparametric structural functions and semiparametric treatment effects R Singh, L Xu, A Gretton arXiv preprint arXiv:2010.04855, 2020 | 3 | 2020 |
| Importance Weighting Approach in Kernel Bayes' Rule L Xu, Y Chen, A Doucet, A Gretton arXiv preprint arXiv:2202.02474, 2022 | 1 | 2022 |
| Kernel Single Proxy Control for Deterministic Confounding L Xu, A Gretton arXiv preprint arXiv:2308.04585, 2023 | | 2023 |
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of Tokyo在 k.u-tokyo.ac.jp 的电子邮件经过验证
