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Priyank Agrawal
Priyank Agrawal
Columbia University
Zweryfikowany adres z columbia.edu - Strona główna
Tytuł
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Cytowane przez
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Learning-augmented mechanism design: Leveraging predictions for facility location
P Agrawal, E Balkanski, V Gkatzelis, T Ou, X Tan
Proceedings of the 23rd ACM Conference on Economics and Computation, 497-528, 2022
492022
Improved worst-case regret bounds for randomized least-squares value iteration
P Agrawal, J Chen, N Jiang
Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 6566-6573, 2021
232021
A tractable online learning algorithm for the multinomial logit contextual bandit
P Agrawal, T Tulabandhula, V Avadhanula
European Journal of Operational Research 310 (2), 737-750, 2023
172023
Incentivising exploration and recommendations for contextual bandits with payments
P Agrawal, T Tulabandhula
Multi-Agent Systems and Agreement Technologies: 17th European Conference …, 2020
52020
Optimistic Q-learning for average reward and episodic reinforcement learning
P Agrawal, S Agrawal
arXiv preprint arXiv:2407.13743, 2024
32024
Learning by repetition: Stochastic multi-armed bandits under priming effect
P Agrawal, T Tulabandula
Conference on Uncertainty in Artificial Intelligence, 470-479, 2020
32020
Improved Sample Complexity for Global Convergence of Actor-Critic Algorithms
N Kumar, P Agrawal, G Ramponi, KY Levy, S Mannor
arXiv preprint arXiv:2410.08868, 2024
2024
Bandits with Temporal Stochastic Constraints
P Agrawal, T Tulabandhula
arXiv preprint arXiv:1811.09026, 2018
2018
Policy Gradient with Tree Search (PGTS) in Reinforcement Learning Evades Local Maxima
N Kumar, P Agrawal, KY Levy, S Mannor
The Second Tiny Papers Track at ICLR 2024, 0
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