| Wasserstein fair classification R Jiang, A Pacchiano, T Stepleton, H Jiang, S Chiappa Uncertainty in artificial intelligence, 862-872, 2020 | 202 | 2020 |
| Effective diversity in population based reinforcement learning J Parker-Holder, A Pacchiano, KM Choromanski, SJ Roberts Advances in Neural Information Processing Systems 33, 18050-18062, 2020 | 163 | 2020 |
| Es-maml: Simple hessian-free meta learning X Song, W Gao, Y Yang, K Choromanski, A Pacchiano, Y Tang arXiv preprint arXiv:1910.01215, 2019 | 136 | 2019 |
| Model selection in contextual stochastic bandit problems A Pacchiano, M Phan, Y Abbasi Yadkori, A Rao, J Zimmert, T Lattimore, ... Advances in Neural Information Processing Systems 33, 10328-10337, 2020 | 105 | 2020 |
| A general approach to fairness with optimal transport C Silvia, J Ray, S Tom, P Aldo, J Heinrich, A John Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3633-3640, 2020 | 76 | 2020 |
| Stochastic bandits with linear constraints A Pacchiano, M Ghavamzadeh, P Bartlett, H Jiang International conference on artificial intelligence and statistics, 2827-2835, 2021 | 75 | 2021 |
| Dueling rl: Reinforcement learning with trajectory preferences A Saha, A Pacchiano, J Lee International Conference on Artificial Intelligence and Statistics, 6263-6289, 2023 | 70* | 2023 |
| Ready policy one: World building through active learning P Ball, J Parker-Holder, A Pacchiano, K Choromanski, S Roberts International Conference on Machine Learning, 591-601, 2020 | 55 | 2020 |
| Tactical optimism and pessimism for deep reinforcement learning T Moskovitz, J Parker-Holder, A Pacchiano, M Arbel, M Jordan Advances in Neural Information Processing Systems 34, 12849-12863, 2021 | 53 | 2021 |
| From complexity to simplicity: Adaptive es-active subspaces for blackbox optimization KM Choromanski, A Pacchiano, J Parker-Holder, Y Tang, V Sindhwani Advances in Neural Information Processing Systems 32, 2019 | 53 | 2019 |
| Regret bound balancing and elimination for model selection in bandits and rl A Pacchiano, C Dann, C Gentile, P Bartlett arXiv preprint arXiv:2012.13045, 2020 | 49 | 2020 |
| On approximate Thompson sampling with Langevin algorithms E Mazumdar, A Pacchiano, Y Ma, M Jordan, P Bartlett International Conference on Machine Learning, 6797-6807, 2020 | 49* | 2020 |
| Learning to score behaviors for guided policy optimization A Pacchiano, J Parker-Holder, Y Tang, K Choromanski, A Choromanska, ... International Conference on Machine Learning, 7445-7454, 2020 | 46 | 2020 |
| Unpacking reward shaping: Understanding the benefits of reward engineering on sample complexity A Gupta, A Pacchiano, Y Zhai, S Kakade, S Levine Advances in Neural Information Processing Systems 35, 15281-15295, 2022 | 45 | 2022 |
| Leveraging offline data in online reinforcement learning A Wagenmaker, A Pacchiano International Conference on Machine Learning, 35300-35338, 2023 | 44 | 2023 |
| Provably robust blackbox optimization for reinforcement learning K Choromanski, A Pacchiano, J Parker-Holder, Y Tang, D Jain, Y Yang, ... Conference on robot learning, 683-696, 2020 | 42 | 2020 |
| Towards tractable optimism in model-based reinforcement learning A Pacchiano, P Ball, J Parker-Holder, K Choromanski, S Roberts Uncertainty in Artificial Intelligence, 1413-1423, 2021 | 40* | 2021 |
| Online model selection for reinforcement learning with function approximation J Lee, A Pacchiano, V Muthukumar, W Kong, E Brunskill International Conference on Artificial Intelligence and Statistics, 3340-3348, 2021 | 39 | 2021 |
| Dynamic balancing for model selection in bandits and rl A Cutkosky, C Dann, A Das, C Gentile, A Pacchiano, M Purohit International Conference on Machine Learning, 2276-2285, 2021 | 38 | 2021 |
| Supervised pretraining can learn in-context reinforcement learning J Lee, A Xie, A Pacchiano, Y Chandak, C Finn, O Nachum, E Brunskill Advances in Neural Information Processing Systems 36, 2024 | 34 | 2024 |
Krzysztof ChoromanskiGoogle DeepMind Robotics & Columbia UniversityVerified email at columbia.edu
