MemCloak: Practical Access Obfuscation for Untrusted Memory. - Weixin Liang; Kai Bu; Ke Li; Jinhong Li; Arya Tavakoli (ACSAC, 2018)

DeepStore: In-Storage Acceleration for Intelligent Queries. - Vikram Sharma Mailthody; Zaid Qureshi; Weixin Liang; Ziyan Feng; Simon Garcia De Gonzalo; Youjie Li; Hubertus Franke; Jinjun Xiong; Jian Huang 006; Wen-Mei Hwu (MICRO, 2019)

CU-Net: Component Unmixing Network for Textile Fiber Identification. - Zunlei Feng; Weixin Liang; Daocheng Tao; Li Sun; Anxiang Zeng; Mingli Song (Int. J. Comput. Vis., 2019)

MOSS: End-to-End Dialog System Framework with Modular Supervision. - Weixin Liang; Youzhi Tian; Chengcai Chen; Zhou Yu 005 (AAAI, 2020)

Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation. - Weixin Liang; James Zou 001; Zhou Yu (ACL, 2020)

ALICE: Active Learning with Contrastive Natural Language Explanations. - Weixin Liang; James Zou 001; Zhou Yu (EMNLP (1), 2020)

DAWSON: A Domain Adaptive Few Shot Generation Framework. - Weixin Liang; Zixuan Liu; Can Liu (CoRR, 2020)

LRTA: A Transparent Neural-Symbolic Reasoning Framework with Modular Supervision for Visual Question Answering. - Weixin Liang; Feiyang Niu; Aishwarya N. Reganti; Govind Thattai; Gökhan Tür (CoRR, 2020)

HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations. - Weixin Liang; Kaihui Liang; Zhou Yu 005 (ACL/IJCNLP (1), 2021)

Neural Group Testing to Accelerate Deep Learning. - Weixin Liang; James Zou 001 (ISIT, 2021)

GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering. - Weixin Liang; Yanhao Jiang; Zixuan Liu (CoRR, 2021)

Disparities in Dermatology AI: Assessments Using Diverse Clinical Images. - Roxana Daneshjou; Kailas Vodrahalli; Weixin Liang; Roberto A. Novoa; Melissa Jenkins; Veronica Rotemberg; Justin Ko; Susan M. Swetter; Elizabeth E. Bailey; Olivier Gevaert; Pritam Mukherjee; Michelle Phung; et al. (CoRR, 2021)

SEAL: Interactive Tool for Systematic Error Analysis and Labeling. - Nazneen Rajani; Weixin Liang; Lingjiao Chen; Margaret Mitchell; James Zou 001 (EMNLP (Demos), 2022)

MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts. - Weixin Liang; James Zou 001 (ICLR, 2022)

Improving Out-of-Distribution Robustness via Selective Augmentation. - Huaxiu Yao; Yu Wang 170; Sai Li 005; Linjun Zhang; Weixin Liang; James Zou 001; Chelsea Finn (ICML, 2022)

Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning. - Weixin Liang; Yuhui Zhang; Yongchan Kwon; Serena Yeung; James Y. Zou (NeurIPS, 2022)

Disparities in Dermatology AI Performance on a Diverse, Curated Clinical Image Set. - Roxana Daneshjou; Kailas Vodrahalli; Roberto A. Novoa; Melissa Jenkins; Weixin Liang; Veronica Rotemberg; Justin Ko; Susan M. Swetter; Elizabeth E. Bailey; Olivier Gevaert; Pritam Mukherjee; Michelle Phung; et al. (CoRR, 2022)

GSCLIP : A Framework for Explaining Distribution Shifts in Natural Language. - Zhiying Zhu; Weixin Liang; James Zou 001 (CoRR, 2022)

Data Budgeting for Machine Learning. - Xinyi Zhao; Weixin Liang; James Zou 001 (CoRR, 2022)

Advances, challenges and opportunities in creating data for trustworthy AI. - Weixin Liang; Girmaw Abebe Tadesse; Daniel E. Ho; Li Fei-Fei 001; Matei Zaharia; Ce Zhang 001; James Zou 001 (Nat. Mach. Intell., 2022)

Systematic analysis of 50 years of Stanford University technology transfer and commercialization. - Weixin Liang; Scott Elrod; Daniel A. McFarland; James Zou 001 (Patterns, 2022)

Author Correction: Advances, challenges and opportunities in creating data for trustworthy AI. - Weixin Liang; Girmaw Abebe Tadesse; Daniel E. Ho; Li Fei-Fei 001; Matei Zaharia; Ce Zhang 001; James Zou 001 (Nat. Mac. Intell., 2022)

Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations. - Weixin Liang; Yining Mao; Yongchan Kwon; Xinyu Yang 002; James Zou 001 (ICML, 2023)

OpenDataVal: a Unified Benchmark for Data Valuation. - Kevin Fu Jiang; Weixin Liang; James Y. Zou; Yongchan Kwon (NeurIPS, 2023)

Can large language models provide useful feedback on research papers? A large-scale empirical analysis. - Weixin Liang; Yuhui Zhang; Hancheng Cao; Binglu Wang; Daisy Ding; Xinyu Yang 002; Kailas Vodrahalli; Siyu He; Daniel Scott Smith; Yian Yin; Daniel A. McFarland; James Zou 001 (CoRR, 2023)

GPT detectors are biased against non-native English writers. - Weixin Liang; Mert Yüksekgönül; Yining Mao; Eric Wu; James Zou 001 (Patterns, 2023)

Navigating Dataset Documentations in AI: A Large-Scale Analysis of Dataset Cards on HuggingFace. - Xinyu Yang 002; Weixin Liang; James Zou 001 (ICLR, 2024)

Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews. - Weixin Liang; Zachary Izzo; Yaohui Zhang; Haley Lepp; Hancheng Cao; Xuandong Zhao; Lingjiao Chen; Haotian Ye; Sheng Liu; Zhi Huang; Daniel A. McFarland; James Y. Zou (ICML, 2024)

A Deep Reinforcement Learning Approach to Optimize Closing Down a Single-Arm Cluster Tool. - Weixin Liang; Qinghua Zhu 001; ZongRu Li; Jiantie Zhou; Yan Hou (SMC, 2024)

Navigating Dataset Documentations in AI: A Large-Scale Analysis of Dataset Cards on Hugging Face. - Xinyu Yang 002; Weixin Liang; James Zou 001 (CoRR, 2024)

What's documented in AI? Systematic Analysis of 32K AI Model Cards. - Weixin Liang; Nazneen Rajani; Xinyu Yang 002; Ezinwanne Ozoani; Eric Wu; Yiqun Chen 001; Daniel Scott Smith; James Zou 001 (CoRR, 2024)

Mapping the Increasing Use of LLMs in Scientific Papers. - Weixin Liang; Yaohui Zhang; Zhengxuan Wu; Haley Lepp; Wenlong Ji; Xuandong Zhao; Hancheng Cao; Sheng Liu; Siyu He; Zhi Huang; Diyi Yang; Christopher Potts; et al. (CoRR, 2024)

LMFusion: Adapting Pretrained Language Models for Multimodal Generation. - Weijia Shi; Xiaochuang Han; Chunting Zhou; Weixin Liang; Xi Victoria Lin; Luke Zettlemoyer; Lili Yu (CoRR, 2024)

Systematic analysis of 32,111 AI model cards characterizes documentation practice in AI. - Weixin Liang; Nazneen Rajani; Xinyu Yang 002; Ezinwanne Ozoani; Eric Wu; Yiqun Chen 001; Daniel Scott Smith; James Zou 001 (Nat. Mac. Intell., 2024)

Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models. - Weixin Liang; Lili Yu; Liang Luo; Srini Iyer 001; Ning Dong; Chunting Zhou; Gargi Ghosh; Mike Lewis; Wen-tau Yih; Luke Zettlemoyer; Xi Victoria Lin (Trans. Mach. Learn. Res., 2025)

Mixture-of-Mamba: Enhancing Multi-Modal State-Space Models with Modality-Aware Sparsity. - Weixin Liang; Junhong Shen; Genghan Zhang; Ning Dong; Luke Zettlemoyer; Lili Yu (CoRR, 2025)

Adaptive Self-improvement LLM Agentic System for ML Library Development. - Genghan Zhang; Weixin Liang; Olivia Hsu; Kunle Olukotun (CoRR, 2025)

The Widespread Adoption of Large Language Model-Assisted Writing Across Society. - Weixin Liang; Yaohui Zhang; Mihai Codreanu; Jiayu Wang; Hancheng Cao; James Zou (CoRR, 2025)

Weak-for-Strong: Training Weak Meta-Agent to Harness Strong Executors. - Fan Nie; Lan Feng; Haotian Ye; Weixin Liang; Pan Lu; Huaxiu Yao; Alexandre Alahi; James Zou 001 (CoRR, 2025)

ResearchCodeBench: Benchmarking LLMs on Implementing Novel Machine Learning Research Code. - Tianyu Hua; Harper Hua; Violet Xiang; Benjamin Klieger; Sang T. Truong; Weixin Liang; Fan-Yun Sun; Nick Haber (CoRR, 2025)

From Replication to Redesign: Exploring Pairwise Comparisons for LLM-Based Peer Review. - Yaohui Zhang; Haijing Zhang; Wenlong Ji; Tianyu Hua; Nick Haber; Hancheng Cao; Weixin Liang (CoRR, 2025)

Computational Approaches to Understanding Large Language Model Impact on Writing and Information Ecosystems. - Weixin Liang (CoRR, 2025)

Closing the Modality Gap for Mixed Modality Search. - Binxu Li; Yuhui Zhang; Xiaohan Wang; Weixin Liang; Ludwig Schmidt; Serena Yeung-Levy (CoRR, 2025)