Privacy Preserving Stochastic Channel-Based Federated Learning with Neural Network Pruning. - Rulin Shao; Hui Liu; Dianbo Liu (CoRR, 2019)

Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving. - Rulin Shao; Hongyu He; Hui Liu; Dianbo Liu (CoRR, 2019)

On the Adversarial Robustness of Visual Transformers. - Rulin Shao; Zhouxing Shi; Jinfeng Yi; Pin-Yu Chen; Cho-Jui Hsieh (CoRR, 2021)

How and When Adversarial Robustness Transfers in Knowledge Distillation? - Rulin Shao; Jinfeng Yi; Pin-Yu Chen; Cho-Jui Hsieh (CoRR, 2021)

Robust Text CAPTCHAs Using Adversarial Examples. - Rulin Shao; Zhouxing Shi; Jinfeng Yi; Pin-Yu Chen; Cho-Jui Hsieh (IEEE Big Data, 2022)

MPCFormer: fast, performant and private Transformer inference with MPC. - Dacheng Li; Rulin Shao; Hongyi Wang 001; Han Guo; Eric P. Xing; Hao Zhang 025 (CoRR, 2022)

On the Adversarial Robustness of Vision Transformers. - Rulin Shao; Zhouxing Shi; Jinfeng Yi; Pin-Yu Chen; Cho-Jui Hsieh (Trans. Mach. Learn. Res., 2022)

Cross-modal Attention Congruence Regularization for Vision-Language Relation Alignment. - Rohan Pandey; Rulin Shao; Paul Pu Liang; Ruslan Salakhutdinov; Louis-Philippe Morency (ACL (1), 2023)

MPCFORMER: Fast, Performant and Provate Transformer Inference with MPC. - Dacheng Li; Hongyi Wang 001; Rulin Shao; Han Guo; Eric P. Xing; Hao Zhang 025 (ICLR, 2023)

VisIT-Bench: A Dynamic Benchmark for Evaluating Instruction-Following Vision-and-Language Models. - Yonatan Bitton; Hritik Bansal; Jack Hessel; Rulin Shao; Wanrong Zhu; Anas Awadalla; Josh Gardner 001; Rohan Taori; Ludwig Schmidt (NeurIPS, 2023)

VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use. - Yonatan Bitton; Hritik Bansal; Jack Hessel; Rulin Shao; Wanrong Zhu; Anas Awadalla; Josh Gardner 001; Rohan Taori; Ludwig Schmidt (CoRR, 2023)

LightSeq: Sequence Level Parallelism for Distributed Training of Long Context Transformers. - Dacheng Li; Rulin Shao; Anze Xie; Eric P. Xing; Joseph E. Gonzalez; Ion Stoica; Xuezhe Ma; Hao Zhang 025 (CoRR, 2023)

Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning. - Zhiyang Xu; Chao Feng; Rulin Shao; Trevor Ashby; Ying Shen 006; Di Jin; Yu Cheng; Qifan Wang 001; Lifu Huang (ACL (Findings), 2024)

DataComp-LM: In search of the next generation of training sets for language models. - Jeffrey Li; Alex Fang; Georgios Smyrnis; Maor Ivgi; Matt Jordan; Samir Yitzhak Gadre; Hritik Bansal; Etash Kumar Guha; Sedrick Scott Keh; Kushal Arora; Saurabh Garg; Rui Xin; et al. (NeurIPS, 2024)

Scaling Retrieval-Based Language Models with a Trillion-Token Datastore. - Rulin Shao; Jacqueline He; Akari Asai; Weijia Shi; Tim Dettmers; Sewon Min; Luke Zettlemoyer; Pang Wei Koh (NeurIPS, 2024)

Language models scale reliably with over-training and on downstream tasks. - Samir Yitzhak Gadre; Georgios Smyrnis; Vaishaal Shankar; Suchin Gururangan; Mitchell Wortsman; Rulin Shao; Jean Mercat; Alex Fang; Jeffrey Li; Sedrick Keh; Rui Xin; Marianna Nezhurina; et al. (CoRR, 2024)

RoRA-VLM: Robust Retrieval-Augmented Vision Language Models. - Jingyuan Qi; Zhiyang Xu; Rulin Shao; Yang Chen; Di Jin; Yu Cheng; Qifan Wang 001; Lifu Huang (CoRR, 2024)

OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs. - Akari Asai; Jacqueline He; Rulin Shao; Weijia Shi; Amanpreet Singh; Joseph Chee Chang; Kyle Lo; Luca Soldaini; Sergey Feldman; Mike D'Arcy; David Wadden; Matt Latzke; et al. (CoRR, 2024)

Improving Factuality with Explicit Working Memory. - Mingda Chen; Yang Li; Karthik Padthe; Rulin Shao; Alicia Sun; Luke Zettlemoyer; Gargi Gosh; Wen-tau Yih (CoRR, 2024)

Improving Factuality with Explicit Working Memory. - Mingda Chen; Yang Li; Karthik Padthe; Rulin Shao; Alicia Yi Sun; Luke Zettlemoyer; Gargi Ghosh; Wen-tau Yih (ACL (1), 2025)

Language models scale reliably with over-training and on downstream tasks. - Samir Yitzhak Gadre; Georgios Smyrnis; Vaishaal Shankar; Suchin Gururangan; Mitchell Wortsman; Rulin Shao; Jean Mercat; Alex Fang; Jeffrey Li; Sedrick Keh; Rui Xin; Marianna Nezhurina; et al. (ICLR, 2025)

LongLeader: A Comprehensive Leaderboard for Large Language Models in Long-context Scenarios. - Pei Chen; Hongye Jin; Cheng-Che Lee; Rulin Shao; Jingfeng Yang 001; Mingyu Zhao; Zhaoyu Zhang; Qin Lu; Kaiwen Men; Ning Xie; Huasheng Li; Bing Yin; et al. (NAACL (Long Papers), 2025)

ICONS: Influence Consensus for Vision-Language Data Selection. - Xindi Wu; Mengzhou Xia; Rulin Shao; Zhiwei Deng; Pang Wei Koh; Olga Russakovsky (CoRR, 2025)

TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval. - Chien-Yu Lin; Keisuke Kamahori; Yiyu Liu; Xiaoxiang Shi; Madhav Mahesh Kashyap; Yile Gu; Rulin Shao; Zihao Ye 001; Kan Zhu; Stephanie Wang; Arvind Krishnamurthy; Rohan Kadekodi; et al. (CoRR, 2025)

ReasonIR: Training Retrievers for Reasoning Tasks. - Rulin Shao; Rui Qiao 006; Varsha Kishore; Niklas Muennighoff; Xi Victoria Lin; Daniela Rus; Bryan Kian Hsiang Low; Sewon Min; Wen-tau Yih; Pang Wei Koh; Luke Zettlemoyer (CoRR, 2025)

Spurious Rewards: Rethinking Training Signals in RLVR. - Rulin Shao; Shuyue Stella Li; Rui Xin; Scott Geng; Yiping Wang; Sewoong Oh; Simon Shaolei Du; Nathan Lambert 001; Sewon Min; Ranjay Krishna; Yulia Tsvetkov; Hannaneh Hajishirzi; et al. (CoRR, 2025)

Frustratingly Simple Retrieval Improves Challenging, Reasoning-Intensive Benchmarks. - Xinxi Lyu; Michael Duan; Rulin Shao; Pang Wei Koh; Sewon Min (CoRR, 2025)

Learning to Reason for Factuality. - Xilun Chen 002; Ilia Kulikov; Vincent-Pierre Berges; Barlas Oguz; Rulin Shao; Gargi Ghosh; Jason Weston; Wen-tau Yih (CoRR, 2025)