Publications

Publications

• Semantic IDs for Recommender Systems at Snapchat: Use Cases, Technical Challenges, and Design Choices
  C. M. Ju, T. Zhao, L. Neves, L. Collins, B. Kumar, J. Ren, L. Zhang, W. Zhuo, V. Zhang, X. Bai, J. Li, K. Iyer, Z. Fan, Y. Xu, Y. Chen, P. Yu, M. Malik, N. Shah
  SIGIR 2026
  [paper], [arXiv]

• Exploiting ID-Text Complementarity via Ensembling for Sequential Recommendation
  L. Collins, B. Kumar, C. M. Ju, T. Zhao, D. Loveland, L. Neves, N. Shah
  RecSys 2026
  [arXiv]

• CoSearch: Joint Training of Reasoning and Document Ranking via Reinforcement Learning for Agentic Search
  H. Zeng, L. Collins, B. Kumar, N. Shah, H. Zamani
  COLM 2026
  [arXiv]

• Training-Free LLM-Based Recommendation with Post-LLM Item Refinement Using Collaborative Signals
  K. Kim, S. Kim, G. Lee, S. Kang, S. Kim, L. Collins, B. Kumar, D. Loveland, K. Shin
  CIKM 2026

• Sequential Data Augmentation for Generative Recommendation
  G. Lee, B. Kumar, C. M. Ju, T. Zhao, K. Shin, N. Shah, L. Collins
  WSDM 2026
  [pdf]

• Learning Universal User Representations Leveraging Cross-Domain User Intent at Snapchat
  C. M. Ju, L. Neves, B. Kumar, L. Collins, T. Zhao, Y. Qiu, Q. Dou, et al.
  SIGIR 2025
  [pdf]

• Generative Recommendation with Semantic IDs: A Practitioner's Handbook
  C. M. Ju, L. Collins, L. Neves, B. Kumar, Y. Y. Wang, T. Zhao, N. Shah
  CIKM 2025 [Best Paper Award in Resource Papers]
  [pdf], [Library]

• Private Mechanism Design via Quantile Estimation
  Y. Yang, T. Xiao, B. Kumar, J. Morgenstern
  ICLR 2025
  [pdf], [poster]

• Revisiting self-attention for cross-domain sequential recommendation
  C. M. Ju, L. Neves, B. Kumar, L. Collins, T. Zhao, Y. Qiu, Q. Dou, S. Nizam, S. Yang, N. Shah
  KDD 2025
  [pdf]

• Accelerated Federated Optimization with Quantization
  Y. Youn, B. Kumar, J. Abernethy
  IEEE Data Engineering Bulletin 2023
  [pdf]

• ActiveHedge: Hedge meets Active Learning
  B. Kumar, J. Abernethy, V. Saligrama
  ICML 2022 (Spotlight)
  [pdf], [poster], [talk]

• Observation Free Attacks on Stochastic Bandits
  Y. Xu, B. Kumar, J. Abernethy
  NeurIPS 2021
  [pdf]

• Bridging Truthfulness and Corruption-robustness in Multi-Armed Bandit Mechanisms
  Y. Xu, B. Kumar, J. Abernethy, T. Lykouris
  Incentives in ML Workshop at ICML 2020
  [pdf], [talk]

• Learning Auctions with Robust Incentive Guarantees
  B. Kumar, J. Abernethy, R. Cummings, J. Morgenstern, S. Taggart
  NeurIPS 2019
  [pdf], [poster]

Preprint(s)

• LLM-Based Generative Retrieval for Snapchat Content Recommendation
  L. Collins, J. Ren, D. Loveland, B. Kumar, C. M. Ju, X. Guo, M. Li, A. Hou, Y. Cui, P. Yang, J. Wang, S. A. Shafi, N. Than, R. Lu, W. Zhuo, D. Li, L. Zhang, M. Zhang, J. Ye, V. Xue, C. Zhu, N. Shah
  [arXiv]

• Optimal spend rate estimation and pacing for ad campaigns with budgets
  B. Kumar, J. Morgenstern, O. Schrijvers
  [pdf], [talk]