Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards A Kagrecha, J Nair, K Jagannathan Advances in Neural Information Processing Systems 32, 11272-11281, 2019 | 50 | 2019 |
Bandit algorithms: Letting go of logarithmic regret for statistical robustness K Ashutosh, J Nair, A Kagrecha, K Jagannathan International Conference on Artificial Intelligence and Statistics, 622-630, 2021 | 19 | 2021 |
Constrained regret minimization for multi-criterion multi-armed bandits A Kagrecha, J Nair, K Jagannathan Machine Learning 112 (2), 431-458, 2023 | 12 | 2023 |
Statistically robust, risk-averse best arm identification in multi-armed bandits A Kagrecha, J Nair, K Jagannathan IEEE Transactions on Information Theory 68 (8), 5248-5267, 2022 | 5 | 2022 |
Adaptive Crowdsourcing Via Self-Supervised Learning A Kagrecha, H Marklund, B Van Roy, HJ Jeon, R Zeckhauser arXiv preprint arXiv:2401.13239, 2024 | | 2024 |
“Please come back later”: Benefiting from deferrals in service systems A Kagrecha, J Nair 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS …, 2020 | | 2020 |