| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2480 | 2023 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 971 | 2024 |
| Doubly robust off-policy evaluation with shrinkage Y Su, M Dimakopoulou, A Krishnamurthy, M Dudík International Conference on Machine Learning, 2020, 2019 | 109 | 2019 |
| Offline rl for natural language generation with implicit language q learning C Snell, I Kostrikov, Y Su, M Yang, S Levine arXiv preprint arXiv:2206.11871, 2022 | 88 | 2022 |
| Cab: Continuous adaptive blending for policy evaluation and learning Y Su, L Wang, M Santacatterina, T Joachims International Conference on Machine Learning, 6005-6014, 2019 | 84 | 2019 |
| Off-policy bandits with deficient support N Sachdeva, Y Su, T Joachims Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 79 | 2020 |
| Online adaptation to label distribution shift R Wu, C Guo, Y Su, KQ Weinberger Advances in Neural Information Processing Systems 34, 11340-11351, 2021 | 58 | 2021 |
| Adaptive Estimator Selection for Off-Policy Evaluation Y Su, P Srinath, A Krishnamurthy International Conference on Machine Learning, 2020, 2020 | 45 | 2020 |
| Training language models to self-correct via reinforcement learning A Kumar, V Zhuang, R Agarwal, Y Su, JD Co-Reyes, A Singh, K Baumli, ... arXiv preprint arXiv:2409.12917, 2024 | 43 | 2024 |
| Optimizing Rankings for Recommendation in Matching Markets Y Su, M Bayoumi, T Joachims Proceedings of the ACM Web Conference 2022, 328-338, 2022 | 31 | 2022 |
| Context-Aware Language Modeling for Goal-Oriented Dialogue Systems C Snell, S Yang, J Fu, Y Su, S Levine NAACL, 2022, 2022 | 26 | 2022 |
| Recommendations as treatments T Joachims, B London, Y Su, A Swaminathan, L Wang AI Magazine 42 (3), 19-30, 2021 | 21 | 2021 |
| Data-driven offline decision-making via invariant representation learning H Qi, Y Su, A Kumar, S Levine Advances in Neural Information Processing Systems 35, 13226-13237, 2022 | 17 | 2022 |
| Data-driven model-based optimization via invariant representation learning H Qi, Y Su, A Kumar, S Levine Proc. Adv. Neur. Inf. Proc. Syst (NeurIPS), 2022 | 6 | 2022 |
| Training language models to self-correct via reinforcement learning, 2024 A Kumar, V Zhuang, R Agarwal, Y Su, JD Co-Reyes, A Singh, K Baumli, ... URL https://arxiv. org/abs/2409.12917, 0 | 6 | |
| Unified off-policy learning to rank: a reinforcement learning perspective Z Zhang, Y Su, H Yuan, Y Wu, R Balasubramanian, Q Wu, H Wang, ... Advances in Neural Information Processing Systems 36, 2024 | 4 | 2024 |
| Long-Term Value of Exploration: Measurements, Findings and Algorithms Y Su, X Wang, EY Le, L Liu, Y Li, H Lu, B Lipshitz, S Badam, L Heldt, S Bi, ... Proceedings of the 17th ACM International Conference on Web Search and Data …, 2024 | 3 | 2024 |
| Learning from logged bandit feedback of multiple loggers Y Su, A Agarwal, T Joachims ICML Workshop on Machine Learning for Causal Inference, Counterfactual …, 2018 | 3 | 2018 |
| Value of exploration: Measurements, findings and algorithms Y Su, X Wang, EY Le, L Liu, Y Li, H Lu, B Lipshitz, S Badam, L Heldt, S Bi, ... CoRR, 2023 | 2 | 2023 |
| International Conference on Machine Learning Y Su, L Wang, M Santacatterina, T Joachims | 2 | 2019 |
Thorsten JoachimsProfessor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu