Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions O Li*, H Liu*, C Chen, C Rudin Thirty-Second AAAI Conference on Artificial Intelligence, 2018 | 589 | 2018 |
Alice: Towards understanding adversarial learning for joint distribution matching C Li, H Liu, C Chen, Y Pu, L Chen, R Henao, L Carin NIPS, 2017 | 276 | 2017 |
Triangle Generative Adversarial Networks Z Gan, L Chen, W Wang, Y Pu, Y Zhang, H Liu, C Li, L Carin arXiv preprint arXiv:1709.06548, 2017 | 156 | 2017 |
Towards more practical stochastic gradient mcmc in differential privacy B Li, C Chen, H Liu, L Carin Artificial Intelligence and Statistics (AISTATS), 2019 | 48* | 2019 |
Out-of-distribution Prediction with Invariant Risk Minimization: The Limitation and An Effective Fix R Guo, P Zhang, H Liu, E Kiciman arXiv preprint arXiv:2101.07732, 2021 | 33 | 2021 |
Triply Robust Off-Policy Evaluation H Liu, A Anandkumar, Y Yue, A Liu arXiv preprint arXiv:1911.05811, 2019 | 9 | 2019 |
Disentangling Observed Causal Effects from Latent Confounders using Method of Moments A Liu*, H Liu*, T Li*, S Karimi-Bidhendi, Y Yue, A Anandkumar arXiv preprint arXiv:2101.06614 NeurIPS 2019 CausalML Workshop, 2021 | 5 | 2021 |
Scaling Fair Learning to Hundreds of Intersectional Groups E Zhao, DA Huang, H Liu, Z Yu, A Liu, O Russakovsky, A Anandkumar | 3 | 2022 |
Distributionally Robust Policy Evaluation under General Covariate Shift in Contextual Bandits Y Guo*, H Liu*, Y Yue, A Liu tmlr, 2024 | | 2024 |
Supplementary Material of ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching C Li, H Liu, C Chen, Y Pu, L Chen, R Henao, L Carin | | |