Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards. - Anmol Kagrecha; Jayakrishnan Nair 001; Krishna P. Jagannathan (NeurIPS, 2019)

"Please Come Back Later": Benefiting from Deferrals in Service Systems. - Anmol Kagrecha; Jayakrishnan Nair 001 (COMSNETS, 2020)

Bandit algorithms: Letting go of logarithmic regret for statistical robustness. - Kumar Ashutosh; Jayakrishnan Nair 001; Anmol Kagrecha; Krishna P. Jagannathan (AISTATS, 2021)

Statistically Robust, Risk-Averse Best Arm Identification in Multi-Armed Bandits. - Anmol Kagrecha; Jayakrishnan Nair 001; Krishna P. Jagannathan (IEEE Trans. Inf. Theory, 2022)

Constrained regret minimization for multi-criterion multi-armed bandits. - Anmol Kagrecha; Jayakrishnan Nair 001; Krishna Jagannathan 001 (Mach. Learn., 2023)

Adaptive Crowdsourcing Via Self-Supervised Learning. - Anmol Kagrecha; Henrik Marklund; Benjamin Van Roy; Hong Jun Jeon; Richard Zeckhauser (CoRR, 2024)

SkillAggregation: Reference-free LLM-Dependent Aggregation. - Guangzhi Sun; Anmol Kagrecha; Potsawee Manakul; Philip C. Woodland; Mark J. F. Gales (ACL (1), 2025)

Granular feedback merits sophisticated aggregation. - Anmol Kagrecha; Henrik Marklund; Potsawee Manakul; Richard Zeckhauser; Benjamin Van Roy (CoRR, 2025)