7 papers
Hypothesis-Disciplined Multi-Agent Automated Formalization of Asymptotic Statistical Theory
Tingzhou Wei, Zeyu Zheng, Ethan X. Fang +1
Asymptotic statistical theory is a challenging domain for AI-assisted formalization: its central results mix convergence statements, asymptotic expansions, functional analysis, and…
Channel-Oriented Design for EEG-to-Music Reconstruction
Jiaxin Qing, Junwei Lu, Lexin Li
Brain-computer interfaces aim to decode naturalistic stimuli from neural signals, yet most progress to date has focused on vision and language. In this article, we study a more cha…
Contextual Online Uncertainty-Aware Preference Learning for Human Feedback
Nan Lu, Ethan Lee, Ethan X. Fang +1
Reinforcement Learning from Human Feedback (RLHF) has become a pivotal paradigm in artificial intelligence to align large models with human preferences. In this paper, we propose a…
MOTION: ML-Assisted On-Device Low-Latency Motion Recognition
Veeramani Pugazhenthi, Wei-Hsiang Chu, Junwei Lu +5
The use of tiny devices capable of low-latency gesture recognition is gaining momentum in everyday human-computer interaction and especially in medical monitoring fields. Embedded…
Uncertainty Quantification for Large Language Model Reward Learning under Heterogeneous Human Feedback
Pangpang Liu, Junwei Lu, Will Wei Sun
We study estimation and statistical inference for reward models used in aligning large language models (LLMs). A key component of LLM alignment is reinforcement learning from human…
Fisher Random Walk: Automatic Debiasing Contextual Preference Inference for Large Language Model Evaluation
Yichi Zhang, Alexander Belloni, Ethan X. Fang +2
Motivated by the need for rigorous and scalable evaluation of large language models, we study contextual preference inference for pairwise comparison functionals of context-depende…