Hanlin Zhang

According to our database, Hanlin Zhang authored at least 27 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

EvoLM: In Search of Lost Language Model Training Dynamics.

Zhenting Qi, Fan Nie, Alexandre Alahi, James Zou, Himabindu Lakkaraju, Yilun Du, Eric P. Xing, Sham M. Kakade, Hanlin Zhang

CoRR, June, 2025

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning.

Jikai Jin, Vasilis Syrgkanis, Sham M. Kakade, Hanlin Zhang

CoRR, June, 2025

Connections between Schedule-Free Optimizers, AdEMAMix, and Accelerated SGD Variants.

Depen Morwani, Nikhil Vyas, Hanlin Zhang, Sham M. Kakade

CoRR, February, 2025

How Does Critical Batch Size Scale in Pre-training?

Hanlin Zhang, Depen Morwani, Nikhil Vyas, Jingfeng Wu, Difan Zou, Udaya Ghai, Dean P. Foster, Sham M. Kakade

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Eliminating Position Bias of Language Models: A Mechanistic Approach.

Ziqi Wang, Hanlin Zhang, Xiner Li, Kuan-Hao Huang, Chi Han, Shuiwang Ji, Sham M. Kakade, Hao Peng, Heng Ji

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems.

Zhenting Qi, Hanlin Zhang, Eric P. Xing, Sham M. Kakade, Himabindu Lakkaraju

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models.

Yuda Song, Hanlin Zhang, Carson Eisenach, Sham M. Kakade, Dean P. Foster, Udaya Ghai

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Iterative Graph Self-Distillation.

Hanlin Zhang, Shuai Lin, Weiyang Liu, Pan Zhou, Jian Tang, Xiaodan Liang, Eric P. Xing

IEEE Trans. Knowl. Data Eng., March, 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, Niklas Muennighoff, Reinhard Heckel, Jean Mercat, Mayee F. Chen, Suchin Gururangan, Mitchell Wortsman, Alon Albalak, Yonatan Bitton, Marianna Nezhurina, Amro Abbas, Cheng-Yu Hsieh, Dhruba Ghosh, Josh Gardner, Maciej Kilian, Hanlin Zhang, Rulin Shao, Sarah M. Pratt, Sunny Sanyal, Gabriel Ilharco, Giannis Daras, Kalyani Marathe, Aaron Gokaslan, Jieyu Zhang, Khyathi Raghavi Chandu, Thao Nguyen, Igor Vasiljevic, Sham M. Kakade, Shuran Song, Sujay Sanghavi, Fartash Faghri, Sewoong Oh, Luke Zettlemoyer, Kyle Lo, Alaaeldin El-Nouby, Hadi Pouransari, Alexander Toshev, Stephanie Wang, Dirk Groeneveld, Luca Soldaini, Pang Wei Koh, Jenia Jitsev, Thomas Kollar, Alex Dimakis, Yair Carmon, Achal Dave, Ludwig Schmidt, Vaishaal Shankar

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training.

David Brandfonbrener, Hanlin Zhang, Andreas Kirsch, Jonathan Richard Schwarz, Sham M. Kakade

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

A Study on the Calibration of In-context Learning.

Hanlin Zhang, Yifan Zhang, Yaodong Yu, Dhruv Madeka, Dean P. Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade

Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024

Evaluating Step-by-Step Reasoning through Symbolic Verification.

Yifan Zhang, Hanlin Zhang, Li Li, Eric P. Xing

Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2024, 2024

Watermarks in the Sand: Impossibility of Strong Watermarking for Language Models.

Hanlin Zhang, Benjamin L. Edelman, Danilo Francati, Daniele Venturi, Giuseppe Ateniese, Boaz Barak

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation.

Yifan Zhang, Hanlin Zhang, Zachary Chase Lipton, Li Erran Li, Eric P. Xing

Trans. Mach. Learn. Res., 2023

Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models.

Hanlin Zhang, Benjamin L. Edelman, Danilo Francati, Daniele Venturi, Giuseppe Ateniese, Boaz Barak

IACR Cryptol. ePrint Arch., 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, Scott Emmons, Dan Hendrycks

CoRR, 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, Scott Emmons, Dan Hendrycks

Proceedings of the International Conference on Machine Learning, 2023

Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming.

Hanlin Zhang, Jiani Huang, Ziyang Li, Mayur Naik, Eric P. Xing

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

2022

The Impact of Symbolic Representations on In-context Learning for Few-shot Reasoning.

Hanlin Zhang, Yifan Zhang, Li Erran Li, Eric P. Xing

CoRR, 2022

A Closer Look at the Calibration of Differentially Private Learners.

Hanlin Zhang, Xuechen Li, Prithviraj Sen, Salim Roukos, Tatsunori Hashimoto

CoRR, 2022

Can Transformers be Strong Treatment Effect Estimators?

Yifan Zhang, Hanlin Zhang, Zachary C. Lipton, Li Erran Li, Eric P. Xing

CoRR, 2022

Stochastic Neural Networks with Infinite Width are Deterministic.

Liu Ziyin, Hanlin Zhang, Xiangming Meng, Yuting Lu, Eric P. Xing, Masahito Ueda

CoRR, 2022

Toward learning human-aligned cross-domain robust models by countering misaligned features.

Haohan Wang, Zeyi Huang, Hanlin Zhang, Yong Jae Lee, Eric P. Xing

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Towards Principled Disentanglement for Domain Generalization.

Hanlin Zhang, Yifan Zhang, Weiyang Liu, Adrian Weller, Bernhard Schölkopf, Eric P. Xing

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features.

Haohan Wang, Zeyi Huang, Hanlin Zhang, Eric Poe Xing

CoRR, 2021

Learning Domain Invariant Representations for Generalizable Person Re-Identification.

Yifan Zhang, Hanlin Zhang, Zhang Zhang, Da Li, Zhen Jia, Liang Wang, Tieniu Tan

CoRR, 2021

2020

Towards Interpretable Natural Language Understanding with Explanations as Latent Variables.

Wangchunshu Zhou, Jinyi Hu, Hanlin Zhang, Xiaodan Liang, Maosong Sun, Chenyan Xiong, Jian Tang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020