Towards Interpretable Natural Language Understanding with Explanations as Latent Variables. - Wangchunshu Zhou; Jinyi Hu; Hanlin Zhang 002; Xiaodan Liang; Maosong Sun 001; Chenyan Xiong; Jian Tang 005 (NeurIPS, 2020)

Learning Domain Invariant Representations for Generalizable Person Re-Identification. - Yifan Zhang 004; Hanlin Zhang 002; Zhang Zhang 001; Da Li 003; Zhen Jia; Liang Wang 001; Tieniu Tan (CoRR, 2021)

Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features. - Haohan Wang; Zeyi Huang; Hanlin Zhang 002; Eric Poe Xing (CoRR, 2021)

Towards Principled Disentanglement for Domain Generalization. - Hanlin Zhang 002; Yifan Zhang 004; Weiyang Liu; Adrian Weller; Bernhard Schölkopf; Eric P. Xing (CVPR, 2022)

Toward learning human-aligned cross-domain robust models by countering misaligned features. - Haohan Wang; Zeyi Huang; Hanlin Zhang 002; Yong Jae Lee; Eric P. Xing (UAI, 2022)

Stochastic Neural Networks with Infinite Width are Deterministic. - Liu Ziyin 001; Hanlin Zhang 002; Xiangming Meng; Yuting Lu; Eric P. Xing; Masahito Ueda (CoRR, 2022)

Can Transformers be Strong Treatment Effect Estimators? - Yifan Zhang 004; Hanlin Zhang 002; Zachary C. Lipton; Li Erran Li; Eric P. Xing (CoRR, 2022)

A Closer Look at the Calibration of Differentially Private Learners. - Hanlin Zhang 002; Xuechen Li 005; Prithviraj Sen; Salim Roukos; Tatsunori Hashimoto (CoRR, 2022)

The Impact of Symbolic Representations on In-context Learning for Few-shot Reasoning. - Hanlin Zhang 002; Yifan Zhang 004; Li Erran Li; Eric P. Xing (CoRR, 2022)

Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming. - Hanlin Zhang 002; Jiani Huang; Ziyang Li; Mayur Naik; Eric P. Xing (ACL (Findings), 2023)

Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the Machiavelli Benchmark. - Alexander Pan; Jun Shern Chan; Andy Zou; Nathaniel Li; Steven Basart; Thomas Woodside; Hanlin Zhang 002; Scott Emmons; Dan Hendrycks (ICML, 2023)

Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark. - Alexander Pan; Jun Shern Chan; Andy Zou; Nathaniel Li; Steven Basart; Thomas Woodside; Jonathan Ng; Hanlin Zhang 002; Scott Emmons; Dan Hendrycks (CoRR, 2023)

Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models. - Hanlin Zhang 002; Benjamin L. Edelman; Danilo Francati; Daniele Venturi 001; Giuseppe Ateniese; Boaz Barak (IACR Cryptol. ePrint Arch., 2023)

Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation. - Yifan Zhang 004; Hanlin Zhang 002; Zachary Chase Lipton; Li Erran Li; Eric P. Xing (Trans. Mach. Learn. Res., 2023)

Watermarks in the Sand: Impossibility of Strong Watermarking for Language Models. - Hanlin Zhang 002; Benjamin L. Edelman; Danilo Francati; Daniele Venturi 001; Giuseppe Ateniese; Boaz Barak (ICML, 2024)

Evaluating Step-by-Step Reasoning through Symbolic Verification. - Yifan Zhang 004; Hanlin Zhang 002; Li Li 002; Eric P. Xing (NAACL-HLT (Findings), 2024)

A Study on the Calibration of In-context Learning. - Hanlin Zhang 002; Yifan Zhang 004; Yaodong Yu; Dhruv Madeka; Dean P. Foster; Eric P. Xing; Himabindu Lakkaraju; Sham M. Kakade (NAACL-HLT, 2024)

CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training. - David Brandfonbrener; Hanlin Zhang 002; Andreas Kirsch 002; Jonathan Richard Schwarz; Sham M. Kakade (NeurIPS, 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)

Iterative Graph Self-Distillation. - Hanlin Zhang 002; Shuai Lin; Weiyang Liu; Pan Zhou 002; Jian Tang 005; Xiaodan Liang; Eric P. Xing (IEEE Trans. Knowl. Data Eng., 2024)

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models. - Yuda Song 001; Hanlin Zhang 002; Carson Eisenach; Sham M. Kakade; Dean P. Foster; Udaya Ghai (ICLR, 2025)

Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems. - Zhenting Qi; Hanlin Zhang 002; Eric P. Xing; Sham M. Kakade; Himabindu Lakkaraju (ICLR, 2025)

Eliminating Position Bias of Language Models: A Mechanistic Approach. - Ziqi Wang 003; Hanlin Zhang 002; Xiner Li; Kuan-Hao Huang; Chi Han; Shuiwang Ji; Sham M. Kakade; Hao Peng 009; Heng Ji 001 (ICLR, 2025)

How Does Critical Batch Size Scale in Pre-training? - Hanlin Zhang 002; Depen Morwani; Nikhil Vyas 001; Jingfeng Wu; Difan Zou; Udaya Ghai; Dean P. Foster; Sham M. Kakade (ICLR, 2025)

Connections between Schedule-Free Optimizers, AdEMAMix, and Accelerated SGD Variants. - Depen Morwani; Nikhil Vyas 001; Hanlin Zhang 002; Sham M. Kakade (CoRR, 2025)

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning. - Jikai Jin; Vasilis Syrgkanis; Sham M. Kakade; Hanlin Zhang 002 (CoRR, 2025)

EvoLM: In Search of Lost Language Model Training Dynamics. - Zhenting Qi; Fan Nie; Alexandre Alahi; James Zou 001; Himabindu Lakkaraju; Yilun Du; Eric P. Xing; Sham M. Kakade; Hanlin Zhang 002 (CoRR, 2025)