| Peer effects and stability in matching markets E Bodine-Baron, C Lee, A Chong, B Hassibi, A Wierman International Symposium on Algorithmic Game Theory, 117-129, 2011 | 295 | 2011 |
| Estimating the total treatment effect in randomized experiments with unknown network structure CL Yu, EM Airoldi, C Borgs, JT Chayes Proceedings of the National Academy of Sciences 119 (44), e2208975119, 2022 | 67 | 2022 |
| Adaptive discretization for episodic reinforcement learning in metric spaces SR Sinclair, S Banerjee, CL Yu Proceedings of the ACM on Measurement and Analysis of Computing Systems 3 (3 …, 2019 | 67 | 2019 |
| Sequential fair allocation: Achieving the optimal envy-efficiency tradeoff curve SR Sinclair, S Banerjee, CL Yu ACM SIGMETRICS Performance Evaluation Review 50 (1), 95-96, 2022 | 66 | 2022 |
| Blind regression: Nonparametric regression for latent variable models via collaborative filtering D Song, CE Lee, Y Li, D Shah Advances in Neural Information Processing Systems 29, 2016 | 61 | 2016 |
| Staggered rollout designs enable causal inference under interference without network knowledge M Cortez, M Eichhorn, C Yu Advances in Neural Information Processing Systems 35, 7437-7449, 2022 | 42* | 2022 |
| Adaptive discretization for model-based reinforcement learning S Sinclair, T Wang, G Jain, S Banerjee, C Yu Advances in Neural Information Processing Systems 33, 3858-3871, 2020 | 39 | 2020 |
| Thy friend is my friend: Iterative collaborative filtering for sparse matrix estimation C Borgs, J Chayes, CE Lee, D Shah Advances in neural information processing systems 30, 2017 | 37 | 2017 |
| Adaptive discretization in online reinforcement learning SR Sinclair, S Banerjee, CL Yu Operations Research 71 (5), 1636-1652, 2023 | 35 | 2023 |
| Nearest neighbors for matrix estimation interpreted as blind regression for latent variable model Y Li, D Shah, D Song, CL Yu IEEE Transactions on Information Theory 66 (3), 1760-1784, 2019 | 35* | 2019 |
| Computing the stationary distribution locally CE Lee, A Ozdaglar, D Shah Advances in Neural Information Processing Systems 26, 2013 | 35* | 2013 |
| Asynchronous approximation of a single component of the solution to a linear system A Ozdaglar, D Shah, CL Yu IEEE Transactions on Network Science and Engineering 7 (3), 975-986, 2019 | 33* | 2019 |
| Reducing crowdsourcing to graphon estimation, statistically D Shah, C Lee International Conference on Artificial Intelligence and Statistics, 1741-1750, 2018 | 31* | 2018 |
| Exploiting neighborhood interference with low-order interactions under unit randomized design M Cortez-Rodriguez, M Eichhorn, CL Yu Journal of Causal Inference 11 (1), 20220051, 2023 | 29 | 2023 |
| Sequential fair allocation of limited resources under stochastic demands SR Sinclair, G Jain, S Banerjee, CL Yu arXiv preprint arXiv:2011.14382, 2020 | 28 | 2020 |
| Iterative collaborative filtering for sparse noisy tensor estimation D Shah, CL Yu 2019 IEEE International Symposium on Information Theory (ISIT), 41-45, 2019 | 25 | 2019 |
| Nonparametric contextual bandits in an unknown metric space N Wanigasekara, CL Yu Advances in Neural Information Processing Systems 32, 14684-14694, 2019 | 22* | 2019 |
| Overcoming the long horizon barrier for sample-efficient reinforcement learning with latent low-rank structure T Sam, Y Chen, CL Yu Proceedings of the ACM on Measurement and Analysis of Computing Systems 7 (2 …, 2023 | 19 | 2023 |
| Clustered switchback experiments: Near-optimal rates under spatiotemporal interference S Jia, N Kallus, CL Yu arXiv preprint arXiv:2312.15574, 2023 | 18 | 2023 |
| Iterative collaborative filtering for sparse matrix estimation C Borgs, JT Chayes, D Shah, CL Yu Operations Research 70 (6), 3143-3175, 2022 | 15 | 2022 |
Devavrat ShahMassachusetts Institute of TechnologyVerified email at mit.edu
