| Multi-agent actor-critic for mixed cooperative-competitive environments R Lowe, YI Wu, A Tamar, J Harb, OAI Pieter Abbeel, I Mordatch Advances in neural information processing systems 30, 2017 | 3333 | 2017 |
| Value iteration networks A Tamar, Y Wu, G Thomas, S Levine, P Abbeel Advances in neural information processing systems 29, 2016 | 653 | 2016 |
| Emergent tool use from multi-agent autocurricula B Baker, I Kanitscheider, T Markov, Y Wu, G Powell, B McGrew, ... arXiv preprint arXiv:1909.07528, 2019 | 592 | 2019 |
| The surprising effectiveness of ppo in cooperative multi-agent games C Yu, A Velu, E Vinitsky, J Gao, Y Wang, A Bayen, Y Wu Advances in Neural Information Processing Systems 35, 24611-24624, 2022 | 392 | 2022 |
| Building generalizable agents with a realistic and rich 3d environment Y Wu, Y Wu, G Gkioxari, Y Tian arXiv preprint arXiv:1801.02209, 2018 | 320 | 2018 |
| Adversarial training for relation extraction Y Wu, D Bamman, S Russell Proceedings of the 2017 Conference on Empirical Methods in Natural Language …, 2017 | 218 | 2017 |
| Robust multi-agent reinforcement learning via minimax deep deterministic policy gradient S Li, Y Wu, X Cui, H Dong, F Fang, S Russell Proceedings of the AAAI conference on artificial intelligence 33 (01), 4213-4220, 2019 | 217 | 2019 |
| Multi-task reinforcement learning with soft modularization R Yang, H Xu, Y Wu, X Wang Advances in Neural Information Processing Systems 33, 4767-4777, 2020 | 104 | 2020 |
| Bayesian relational memory for semantic visual navigation Y Wu, Y Wu, A Tamar, S Russell, G Gkioxari, Y Tian Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 96* | 2019 |
| Influence-based multi-agent exploration T Wang, J Wang, Y Wu, C Zhang arXiv preprint arXiv:1910.05512, 2019 | 82 | 2019 |
| Deep reinforcement learning for green security games with real-time information Y Wang, ZR Shi, L Yu, Y Wu, R Singh, L Joppa, F Fang Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 1401-1408, 2019 | 71 | 2019 |
| Evolutionary population curriculum for scaling multi-agent reinforcement learning Q Long, Z Zhou, A Gupta, F Fang, Y Wu, X Wang arXiv preprint arXiv:2003.10423, 2020 | 68 | 2020 |
| Noveld: A simple yet effective exploration criterion T Zhang, H Xu, X Wang, Y Wu, K Keutzer, JE Gonzalez, Y Tian Advances in Neural Information Processing Systems 34, 25217-25230, 2021 | 58* | 2021 |
| Swift: Compiled inference for probabilistic programming languages Y Wu, L Li, S Russell, R Bodik arXiv preprint arXiv:1606.09242, 2016 | 36* | 2016 |
| Discovering diverse multi-agent strategic behavior via reward randomization Z Tang, C Yu, B Chen, H Xu, X Wang, F Fang, S Du, Y Wang, Y Wu arXiv preprint arXiv:2103.04564, 2021 | 34 | 2021 |
| Unsupervised extractive summarization by pre-training hierarchical transformers S Xu, X Zhang, Y Wu, F Wei, M Zhou arXiv preprint arXiv:2010.08242, 2020 | 34 | 2020 |
| Multi-agent collaboration via reward attribution decomposition T Zhang, H Xu, X Wang, Y Wu, K Keutzer, JE Gonzalez, Y Tian arXiv preprint arXiv:2010.08531, 2020 | 31 | 2020 |
| Meta-learning MCMC proposals T Wang, Y Wu, D Moore, SJ Russell Advances in neural information processing systems 31, 2018 | 31 | 2018 |
| Sequence level contrastive learning for text summarization S Xu, X Zhang, Y Wu, F Wei Proceedings of the AAAI Conference on Artificial Intelligence 36 (10), 11556 …, 2022 | 26 | 2022 |
| Discrete-continuous mixtures in probabilistic programming: Generalized semantics and inference algorithms Y Wu, S Srivastava, N Hay, S Du, S Russell International Conference on Machine Learning, 5343-5352, 2018 | 26 | 2018 |
Aviv TamarTechnionVerified email at technion.ac.il
