| A semantic loss function for deep learning with symbolic knowledge J Xu, Z Zhang, T Friedman, Y Liang, GV Broeck Proceedings of the 35th International Conference on Machine Learning (ICML), 2018 | 268 | 2018 |
| State of the art control of atari games using shallow reinforcement learning Y Liang, MC Machado, E Talvitie, M Bowling Proceedings of the 15th International Conference on Autonomous Agents …, 2016 | 119 | 2016 |
| Horizon: Facebook's open source applied reinforcement learning platform J Gauci, E Conti, Y Liang, K Virochsiri, Y He, Z Kaden, V Narayanan, X Ye, ... arXiv preprint arXiv:1811.00260, 2018 | 114 | 2018 |
| Learning the structure of probabilistic sentential decision diagrams Y Liang, J Bekker, G Van den Broeck Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence …, 2017 | 87 | 2017 |
| Learning Logistic Circuits Y Liang, G Van den Broeck Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence …, 2019 | 45 | 2019 |
| What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features P Khosravi, Y Liang, YJ Choi, GV Broeck Proceedings of the 26th International Joint Conference on Artificial …, 2019 | 41 | 2019 |
| On Tractable Computation of Expected Predictions P Khosravi, YJ Choi, Y Liang, A Vergari, GV Broeck Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2019 | 32 | 2019 |
| Handling missing data in decision trees: A probabilistic approach P Khosravi, A Vergari, YJ Choi, Y Liang, GV Broeck arXiv preprint arXiv:2006.16341, 2020 | 22 | 2020 |
| SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning A Zhao, T He, Y Liang, H Huang, G Van den Broeck, S Soatto | 18* | 2020 |
| Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing Z Hu, Y Liang, J Zhang, Z Li, Y Liu Advances in Neural Information Processing Systems 31 (NeurIPS 2018), 2018 | 18 | 2018 |
| Towards compact interpretable models: shrinking of learned probabilistic sentential decision diagrams Y Liang, G Van den Broeck IJCAI-17 Workshop on Explainable AI (XAI), 31, 2017 | 16 | 2017 |
| Juice: A julia package for logic and probabilistic circuits M Dang, P Khosravi, Y Liang, A Vergari, G Van den Broeck Proceedings of the AAAI Conference on Artificial Intelligence 35 (18), 16020 …, 2021 | 15 | 2021 |
| Semantic and generalized entropy loss functions for semi-supervised deep learning K Gajowniczek, Y Liang, T Friedman, T Ząbkowski, G Van den Broeck Entropy 22 (3), 334, 2020 | 6 | 2020 |
| On effective parallelization of monte carlo tree search A Liu, Y Liang, J Liu, GV Broeck, J Chen arXiv preprint arXiv:2006.08785, 2020 | 2 | 2020 |
| Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration A Liu, Y Liang, G Van den Broeck Proceedings of the 19th International Conference on Autonomous Agents and …, 2020 | 2 | 2020 |
| IL-Strudel: independence-based learning of structured-decomposable probabilistic circuit ensembles S Kowshik, Y Liang, G Van den Broeck The 4th Workshop on Tractable Probabilistic Modeling, 2021 | 1 | 2021 |
| Towards an Interpretable Latent Space in Structured Models for Video Prediction R Gupta, V Sharma, Y Jain, Y Liang, GV Broeck, P Singla arXiv preprint arXiv:2107.07713, 2021 | | 2021 |
| Unifying Probabilistic Reasoning, Learning, and Classification with Circuit Representations Y Liang University of California, Los Angeles, 2021 | | 2021 |
| Unified Representations for Learning and Reasoning Y Liang | | |
Guy Van den BroeckAssociate Professor and Samueli Fellow, UCLAVerified email at cs.ucla.edu
