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Aldo Pacchiano
Aldo Pacchiano
Broad Institute of MIT and Harvard
Verified email at broadinstitute.org - Homepage
Title
Cited by
Cited by
Year
Wasserstein fair classification
R Jiang, A Pacchiano, T Stepleton, H Jiang, S Chiappa
Uncertainty in artificial intelligence, 862-872, 2020
1892020
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
1542020
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
1322019
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
982020
Stochastic bandits with linear constraints
A Pacchiano, M Ghavamzadeh, P Bartlett, H Jiang
International conference on artificial intelligence and statistics, 2827-2835, 2021
722021
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
712020
Dueling rl: Reinforcement learning with trajectory preferences
A Saha, A Pacchiano, J Lee
International Conference on Artificial Intelligence and Statistics, 6263-6289, 2023
65*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
532020
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
512021
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
502019
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
48*2020
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
472020
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
442020
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
422020
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
Leveraging offline data in online reinforcement learning
A Wagenmaker, A Pacchiano
International Conference on Machine Learning, 35300-35338, 2023
372023
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
372021
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
372021
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
362022
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
332024
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