Hanlin Zhang - RankMe

41 papers A* 10A 1Journal 27Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Prescriptive Scaling Reveals the Evolution of Language Model Capabilities. CoRR Hanlin Zhang, Jikai Jin, Vasilis Syrgkanis, Sham M. Kakade
2026	J	`jnl`	Scaling Reward Modeling without Human Supervision. CoRR Jingxuan Fan, Yueying Li, Zhenting Qi, Dinghuai Zhang, Kianté Brantley, Sham M. Kakade, Hanlin Zhang
2025	J	`jnl`	Connections between Schedule-Free Optimizers, AdEMAMix, and Accelerated SGD Variants. CoRR Depen Morwani, Nikhil Vyas, Hanlin Zhang, Sham M. Kakade
2025	J	`jnl`	Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning. CoRR Jikai Jin, Vasilis Syrgkanis, Sham M. Kakade, Hanlin Zhang
2025	A*	`conf`	Eliminating Position Bias of Language Models: A Mechanistic Approach. ICLR Ziqi Wang, Hanlin Zhang, Xiner Li, Kuan-Hao Huang, Chi Han, Shuiwang Ji, Sham M. Kakade, Hao Peng, Heng Ji
2025	J	`jnl`	EvoLM: In Search of Lost Language Model Training Dynamics. CoRR Zhenting Qi, Fan Nie, Alexandre Alahi, James Zou, Himabindu Lakkaraju, Yilun Du, Eric P. Xing, Sham M. Kakade, Hanlin Zhang
2025	A*	`conf`	Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems. ICLR Zhenting Qi, Hanlin Zhang, Eric P. Xing, Sham M. Kakade, Himabindu Lakkaraju
2025	A*	`conf`	How Does Critical Batch Size Scale in Pre-training? ICLR Hanlin Zhang, Depen Morwani, Nikhil Vyas, Jingfeng Wu, Difan Zou, Udaya Ghai, Dean P. Foster, Sham M. Kakade
2025	A*	`conf`	Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models. ICLR Yuda Song, Hanlin Zhang, Carson Eisenach, Sham M. Kakade, Dean P. Foster, Udaya Ghai
2024	—	`conf`	A Study on the Calibration of In-context Learning. NAACL-HLT Hanlin Zhang, Yifan Zhang, Yaodong Yu, Dhruv Madeka, Dean P. Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade
2024	A*	`conf`	CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training. NeurIPS David Brandfonbrener, Hanlin Zhang, Andreas Kirsch, Jonathan Richard Schwarz, Sham M. Kakade
2024	J	`jnl`	CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training. CoRR David Brandfonbrener, Hanlin Zhang, Andreas Kirsch, Jonathan Richard Schwarz, Sham M. Kakade
2024	A*	`conf`	DataComp-LM: In search of the next generation of training sets for language models. NeurIPS 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
2024	J	`jnl`	DataComp-LM: In search of the next generation of training sets for language models. CoRR Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Yitzhak Gadre, Hritik Bansal, Etash Kumar Guha, Sedrick 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, Alexandros G. Dimakis, Yair Carmon, Achal Dave, Ludwig Schmidt, Vaishaal Shankar
2024	J	`jnl`	Eliminating Position Bias of Language Models: A Mechanistic Approach. CoRR Ziqi Wang, Hanlin Zhang, Xiner Li, Kuan-Hao Huang, Chi Han, Shuiwang Ji, Sham M. Kakade, Hao Peng, Heng Ji
2024	—	`conf`	Evaluating Step-by-Step Reasoning through Symbolic Verification. NAACL-HLT (Findings) Yifan Zhang, Hanlin Zhang, Li Li, Eric P. Xing
2024	J	`jnl`	Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems. CoRR Zhenting Qi, Hanlin Zhang, Eric P. Xing, Sham M. Kakade, Himabindu Lakkaraju
2024	J	`jnl`	How Does Critical Batch Size Scale in Pre-training? CoRR Hanlin Zhang, Depen Morwani, Nikhil Vyas, Jingfeng Wu, Difan Zou, Udaya Ghai, Dean P. Foster, Sham M. Kakade
2024	J	`jnl`	Iterative Graph Self-Distillation. IEEE Trans. Knowl. Data Eng. Hanlin Zhang, Shuai Lin, Weiyang Liu, Pan Zhou, Jian Tang, Xiaodan Liang, Eric P. Xing
2024	J	`jnl`	Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models. CoRR Yuda Song, Hanlin Zhang, Carson Eisenach, Sham M. Kakade, Dean P. Foster, Udaya Ghai
2024	A*	`conf`	Watermarks in the Sand: Impossibility of Strong Watermarking for Language Models. ICML Hanlin Zhang, Benjamin L. Edelman, Danilo Francati, Daniele Venturi, Giuseppe Ateniese, Boaz Barak
2023	J	`jnl`	A Study on the Calibration of In-context Learning. CoRR Hanlin Zhang, Yifan Zhang, Yaodong Yu, Dhruv Madeka, Dean P. Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade
2023	J	`jnl`	Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark. CoRR Alexander Pan, Jun Shern Chan, Andy Zou, Nathaniel Li, Steven Basart, Thomas Woodside, Jonathan Ng, Hanlin Zhang, Scott Emmons, Dan Hendrycks
2023	A*	`conf`	Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the Machiavelli Benchmark. ICML Alexander Pan, Jun Shern Chan, Andy Zou, Nathaniel Li, Steven Basart, Thomas Woodside, Hanlin Zhang, Scott Emmons, Dan Hendrycks
2023	J	`jnl`	Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation. Trans. Mach. Learn. Res. Yifan Zhang, Hanlin Zhang, Zachary Chase Lipton, Li Erran Li, Eric P. Xing
2023	—	`conf`	Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming. ACL (Findings) Hanlin Zhang, Jiani Huang, Ziyang Li, Mayur Naik, Eric P. Xing
2023	J	`jnl`	Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming. CoRR Hanlin Zhang, Jiani Huang, Ziyang Li, Mayur Naik, Eric P. Xing
2023	J	`jnl`	Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models. CoRR Hanlin Zhang, Benjamin L. Edelman, Danilo Francati, Daniele Venturi, Giuseppe Ateniese, Boaz Barak
2023	J	`jnl`	Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models. IACR Cryptol. ePrint Arch. Hanlin Zhang, Benjamin L. Edelman, Danilo Francati, Daniele Venturi, Giuseppe Ateniese, Boaz Barak
2022	J	`jnl`	A Closer Look at the Calibration of Differentially Private Learners. CoRR Hanlin Zhang, Xuechen Li, Prithviraj Sen, Salim Roukos, Tatsunori Hashimoto
2022	J	`jnl`	Can Transformers be Strong Treatment Effect Estimators? CoRR Yifan Zhang, Hanlin Zhang, Zachary C. Lipton, Li Erran Li, Eric P. Xing
2022	J	`jnl`	Stochastic Neural Networks with Infinite Width are Deterministic. CoRR Liu Ziyin, Hanlin Zhang, Xiangming Meng, Yuting Lu, Eric P. Xing, Masahito Ueda
2022	J	`jnl`	The Impact of Symbolic Representations on In-context Learning for Few-shot Reasoning. CoRR Hanlin Zhang, Yifan Zhang, Li Erran Li, Eric P. Xing
2022	A	`conf`	Toward learning human-aligned cross-domain robust models by countering misaligned features. UAI Haohan Wang, Zeyi Huang, Hanlin Zhang, Yong Jae Lee, Eric P. Xing
2022	A*	`conf`	Towards Principled Disentanglement for Domain Generalization. CVPR Hanlin Zhang, Yifan Zhang, Weiyang Liu, Adrian Weller, Bernhard Schölkopf, Eric P. Xing
2021	J	`jnl`	Learning Domain Invariant Representations for Generalizable Person Re-Identification. CoRR Yifan Zhang, Hanlin Zhang, Zhang Zhang, Da Li, Zhen Jia, Liang Wang, Tieniu Tan
2021	J	`jnl`	Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features. CoRR Haohan Wang, Zeyi Huang, Hanlin Zhang, Eric Poe Xing
2021	J	`jnl`	Towards Principled Disentanglement for Domain Generalization. CoRR Hanlin Zhang, Yifan Zhang, Weiyang Liu, Adrian Weller, Bernhard Schölkopf, Eric P. Xing
2020	J	`jnl`	Iterative Graph Self-Distillation. CoRR Hanlin Zhang, Shuai Lin, Weiyang Liu, Pan Zhou, Jian Tang, Xiaodan Liang, Eric P. Xing
2020	A*	`conf`	Towards Interpretable Natural Language Understanding with Explanations as Latent Variables. NeurIPS Wangchunshu Zhou, Jinyi Hu, Hanlin Zhang, Xiaodan Liang, Maosong Sun, Chenyan Xiong, Jian Tang
2020	J	`jnl`	Towards Interpretable Natural Language Understanding with Explanations as Latent Variables. CoRR Wangchunshu Zhou, Jinyi Hu, Hanlin Zhang, Xiaodan Liang, Maosong Sun, Chenyan Xiong, Jian Tang