works on

From the 1 of 42 linked papers with an AI index.

collaborators

42 papers

cs.AI2026

Theory-Level Autoformalization: From Isolated Statements to Unified Formal Knowledge Bases

Marcus J. Min, Mike He, Zhaoyu Li +5

The paper proposes shifting autoformalization from isolated statements to theory-level, aiming to automatically translate whole bodies of mathematical knowledge—including axioms, d…

cs.CY2026

Effective Personalized AI Tutors via LLM-Guided Reinforcement Learning

Angel Tsai-Hsuan Chung, Botong Zhang, Ling-Chieh Kung +2

Generative AI (GenAI) is rapidly reshaping education by unlocking the potential for personalized tutoring. Yet, emerging platforms largely focus on GenAI chatbot tutors that reacti…

cs.LG2026

Improving Access to Essential Medicines via Decision-Aware Machine Learning

Angel Tsai-Hsuan Chung, Jatu Abdulai, Patrick Bayoh +4

A critical challenge in healthcare systems in low- and middle-income countries (LMICs) is the efficient and equitable allocation of scarce resources, particularly essential medicin…

stat.ML2026

A Hierarchy of Policy Learning Problems

Hamsa Bastani, Osbert Bastani, Shihan Chen

Policy learning has received substantial attention with the goal of learning policies from observational data for decision-making. A majority of work in this space has focused on d…

cs.LG2026

Theory-Scale Auto-Formalization of Logics for Computer Science

Yuming Feng, Frederick Pu, One An +5

Auto-formalization is critical for scalable formal verification, but existing progress largely focuses on isolated statements, while theory-scale auto-formalization, which coherent…

cs.LG2026

LLM Program Optimization via Retrieval Augmented Search

Sagnik Anupam, Alexander Shypula, Osbert Bastani

Recent work has demonstrated the potential of large language models (LLMs) for program optimization, a key challenge in programming languages. We propose a blackbox adaptation meth…