3 citations · 11 across the 9 of their papers we have counts for
6 papers · 1 filter
Behavior Cloning is Not All You Need: The Optimality of On-Policy Distillation for Noisy Expert Feedback
Ved Sriraman, Peihan Liu, Daniel Hsu +1
Imitation Learning is a natural framework for learning in sequential decision-making systems and has emerged as the dominant paradigm through which we understand language model tra…
Privately Fine-Tuned LLMs Preserve Temporal Dynamics in Tabular Data
Lucas Rosenblatt, Peihan Liu, Ryan McKenna +1
Research on differentially private synthetic tabular data has largely focused on independent and identically distributed rows where each record corresponds to a unique individual.…
Convex Relaxation for Solving Large-Margin Classifiers in Hyperbolic Space
Sheng Yang, Peihan Liu, Cengiz Pehlevan
Hyperbolic spaces have increasingly been recognized for their outstanding performance in handling data with inherent hierarchical structures compared to their Euclidean counterpart…
Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training
Alyssa Huang, Peihan Liu, Ryumei Nakada +2
The surge in multimodal AI's success has sparked concerns over data privacy in vision-and-language tasks. While CLIP has revolutionized multimodal learning through joint training o…
Improving Adversarial Transferability with Scheduled Step Size and Dual Example
Zeliang Zhang, Peihan Liu, Xiaosen Wang +1
Deep neural networks are widely known to be vulnerable to adversarial examples, especially showing significantly poor performance on adversarial examples generated under the white-…
How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies
Edward Small, Wei Shao, Zeliang Zhang +4
With the introduction of machine learning in high-stakes decision making, ensuring algorithmic fairness has become an increasingly important problem to solve. In response to this,…