Learning Auctions with Robust Incentive Guarantees. - Jacob D. Abernethy; Rachel Cummings; Bhuvesh Kumar; Sam Taggart; Jamie Morgenstern (NeurIPS, 2019)

Observation-Free Attacks on Stochastic Bandits. - Yinglun Xu; Bhuvesh Kumar; Jacob D. Abernethy (NeurIPS, 2021)

ActiveHedge: Hedge meets Active Learning. - Bhuvesh Kumar; Jacob D. Abernethy; Venkatesh Saligrama (ICML, 2022)

Optimal Spend Rate Estimation and Pacing for Ad Campaigns with Budgets. - Bhuvesh Kumar; Jamie Morgenstern; Okke Schrijvers (CoRR, 2022)

On the Robustness of Epoch-Greedy in Multi-Agent Contextual Bandit Mechanisms. - Yinglun Xu; Bhuvesh Kumar; Jacob D. Abernethy (CoRR, 2023)

Accelerated Federated Optimization with Quantization. - Yeojoon Youn; Bhuvesh Kumar; Jacob D. Abernethy (IEEE Data Eng. Bull., 2023)

Sequential Decision Making with Strategic Agents and Limited Feedback. - Bhuvesh Kumar (2023)

Private Mechanism Design via Quantile Estimation. - Yuanyuan Yang; Tao Xiao; Bhuvesh Kumar; Jamie H. Morgenstern (ICLR, 2025)

Learning Universal User Representations Leveraging Cross-domain User Intent at Snapchat. - Clark Mingxuan Ju; Leonardo Neves; Bhuvesh Kumar; Liam Collins; Tong Zhao 003; Yuwei Qiu; Qing Dou; Yang Zhou; Sohail Nizam; Rengim Aykan Ozturk; Yvette Liu; Sen Yang; et al. (SIGIR, 2025)

Revisiting Self-attention for Cross-domain Sequential Recommendation. - Clark Mingxuan Ju; Leonardo Neves; Bhuvesh Kumar; Liam Collins; Tong Zhao 003; Yuwei Qiu; Qing Dou; Sohail Nizam; Sen Yang; Neil Shah (CoRR, 2025)

Generative Recommendation with Semantic IDs: A Practitioner's Handbook. - Clark Mingxuan Ju; Liam Collins; Leonardo Neves; Bhuvesh Kumar; Louis Yufeng Wang; Tong Zhao 003; Neil Shah (CoRR, 2025)