4 papers
A Mechanistic Study of Tabular Foundation Models
Marin Biloš, James T. Wilson, Anderson Schneider +1
Tabular foundation models with different architectures converge in accuracy across a range of classification and regression tasks. This raises questions a leaderboard cannot answer…
Quantile-Coupled Flow Matching for Distributional Reinforcement Learning
Michael Groom, Victor-Alexandru Darvariu, Lars Kunze +2
Unlike standard expected-return Reinforcement Learning (RL), Distributional RL (DRL) models the full return distribution, making it better-suited for uncertainty-aware and risk-sen…
AlphaLab: Autonomous Multi-Agent Research Across Optimization Domains with Frontier LLMs
Brendan R. Hogan, Xiwen Chen, James T. Wilson +5
We present AlphaLab, an autonomous research harness that leverages frontier LLM agentic capabilities to automate the full experimental cycle in quantitative, computation-intensive…
Stopping Bayesian Optimization with Probabilistic Regret Bounds
James T. Wilson
Bayesian optimization is a popular framework for efficiently tackling black-box search problems. As a rule, these algorithms operate by iteratively choosing what to evaluate next u…