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Chenyi Li

5 accepted papers

2026

OptProver: Bridging Olympiad and Optimization through Continual Training in Formal Theorem Proving

ICML 2026poster

Recent advances in formal theorem proving have focused on Olympiad-level mathematics, leaving undergraduate domains largely unexplored. Optimization, fundamental to machine learning, operations research, and scientific computing, remains underserved by existing provers. Its reliance on domain-specif…

Cited by 0SourceScholar
2026

SITA: A Framework for Structure-to-Instance Theorem Autoformalization

AAAI 2026technical

While large language models (LLMs) have shown progress in mathematical reasoning, they still face challenges in formalizing theorems that arise from instantiating abstract structures in concrete settings. With the goal of auto-formalizing mathematical results at the research level, we develop a fram

Cited by 0SourcePDFScholar
2026

SetPO: Set-Level Policy Optimization for Diversity-Preserving LLM Reasoning

ICML 2026poster

Reinforcement learning with verifiable rewards has shown notable effectiveness in enhancing large language models (LLMs) reasoning performance, especially in mathematics tasks. However, such improvements often come with reduced outcome diversity, where the model concentrates probability mass on a na…

Cited by 0SourceScholar
2025

Self supervised learning for in vivo localization of microelectrode arrays using raw local field potential

NeurIPS 2025poster

Recent advances in large-scale neural recordings have enabled accurate decoding of behavior and cognitive states, yet decoding anatomical regions remains underexplored, despite being crucial for consistent targeting in multiday recordings and effective deep brain stimulation. Current approaches typi…

Cited by 0SourcecodeScholar
2022

Enhancing Speaking Styles in Conversational Text-to-Speech Synthesis with Graph-Based Multi-Modal Context Modeling

ICASSP 2022accepted

Comparing with traditional text-to-speech (TTS) systems, conversational TTS systems are required to synthesize speeches with proper speaking style confirming to the conversational context. However, state-of-the-art context modeling methods in conversational TTS only model the textual information in…

Cited by 0SourceScholar