Bandits with Temporal Stochastic Constraints. - Priyank Agrawal; Theja Tulabandhula (CoRR, 2018)

Incentivising Exploration and Recommendations for Contextual Bandits with Payments. - Priyank Agrawal; Theja Tulabandhula (EUMAS/AT, 2020)

Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect. - Priyank Agrawal; Theja Tulabandhula (UAI, 2020)

Improved Optimistic Algorithm For The Multinomial Logit Contextual Bandit. - Priyank Agrawal; Vashist Avadhanula; Theja Tulabandhula (CoRR, 2020)

Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration. - Priyank Agrawal; Jinglin Chen; Nan Jiang 008 (AAAI, 2021)

A tractable online learning algorithm for the multinomial logit contextual bandit. - Priyank Agrawal; Theja Tulabandhula; Vashist Avadhanula (Eur. J. Oper. Res., 2023)

Policy Gradient with Tree Search (PGTS) in Reinforcement Learning Evades Local Maxima. - Navdeep Kumar; Priyank Agrawal; Kfir Yehuda Levy; Shie Mannor (Tiny Papers @ ICLR, 2024)

Optimistic Q-learning for average reward and episodic reinforcement learning. - Priyank Agrawal; Shipra Agrawal 001 (CoRR, 2024)

Improved Sample Complexity for Global Convergence of Actor-Critic Algorithms. - Navdeep Kumar; Priyank Agrawal; Giorgia Ramponi; Kfir Yehuda Levy; Shie Mannor (CoRR, 2024)

Learning-Augmented Mechanism Design: Leveraging Predictions for Facility Location. - Priyank Agrawal; Eric Balkanski; Vasilis Gkatzelis; Tingting Ou; Xizhi Tan (Math. Oper. Res., 2024)

Optimistic Q-learning for average reward and episodic reinforcement learning extended abstract. - Priyank Agrawal; Shipra Agrawal (COLT, 2025)

Q-learning with Posterior Sampling. - Priyank Agrawal; Shipra Agrawal 001; Azmat Azati (CoRR, 2025)