Priyank Agrawal

According to our database, Priyank Agrawal authored at least 10 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Improved Sample Complexity for Global Convergence of Actor-Critic Algorithms.

Navdeep Kumar, Priyank Agrawal, Giorgia Ramponi, Kfir Yehuda Levy, Shie Mannor

CoRR, 2024

Optimistic Q-learning for average reward and episodic reinforcement learning.

Priyank Agrawal, Shipra Agrawal

CoRR, 2024

Policy Gradient with Tree Search (PGTS) in Reinforcement Learning Evades Local Maxima.

Navdeep Kumar, Priyank Agrawal, Kfir Yehuda Levy, Shie Mannor

Proceedings of the Second Tiny Papers Track at ICLR 2024, 2024

2023

A tractable online learning algorithm for the multinomial logit contextual bandit.

Priyank Agrawal, Theja Tulabandhula, Vashist Avadhanula

Eur. J. Oper. Res., October, 2023

2022

Learning-Augmented Mechanism Design: Leveraging Predictions for Facility Location.

Priyank Agrawal, Eric Balkanski, Vasilis Gkatzelis, Tingting Ou, Xizhi Tan

Proceedings of the EC '22: The 23rd ACM Conference on Economics and Computation, Boulder, CO, USA, July 11, 2022

2021

Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration.

Priyank Agrawal, Jinglin Chen, Nan Jiang

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Improved Optimistic Algorithm For The Multinomial Logit Contextual Bandit.

Priyank Agrawal, Vashist Avadhanula, Theja Tulabandhula

CoRR, 2020

Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect.

Priyank Agrawal, Theja Tulabandhula

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Incentivising Exploration and Recommendations for Contextual Bandits with Payments.

Priyank Agrawal, Theja Tulabandhula

Proceedings of the Multi-Agent Systems and Agreement Technologies, 2020

2018

Bandits with Temporal Stochastic Constraints.

Priyank Agrawal, Theja Tulabandhula

CoRR, 2018