1 citations · 1 across the 10 of their papers we have counts for
11 papers
Planning Against Learning in Rank-1 Games
William Overman
Learning algorithms are often used to make decisions in repeated multi-agent environments. When another player understands how a learner adapts from past experience, that player ca…
Calibrating Conservatism for Scalable Oversight
William Overman, Mohsen Bayati
Agentic AI systems capable of autonomous planning and extended environmental interaction pose a fundamental control problem: how can humans maintain meaningful oversight of systems…
Annealed Softmax Greedy in Many-Armed Bayesian Bandits
William Overman, Mohsen Bayati
Reinforcement learning with verifiable rewards and group-based policy optimization methods update a stochastic policy by sampling multiple completions per prompt and increasing the…
Causal Effects with Unobserved Unit Types in Interacting Human-AI Systems
William Overman, Sadegh Shirani, Mohsen Bayati
We study experiments on interacting populations of humans and AI agents, where both unit types and the interaction network remain unobserved. Although causal effects propagate thro…
The Oversight Game: Learning to Cooperatively Balance an AI Agent's Safety and Autonomy
William Overman, Mohsen Bayati
As increasingly capable agents are deployed, a central safety challenge is how to retain meaningful human control without modifying the underlying system. We study a minimal contro…
On Aligning Prediction Models with Clinical Experiential Learning: A Prostate Cancer Case Study
Jacqueline J. Vallon, William Overman, Wanqiao Xu +11
Over the past decade, the use of machine learning (ML) models in healthcare applications has rapidly increased. Despite high performance, modern ML models do not always capture pat…
Conformal Arbitrage: Risk-Controlled Balancing of Competing Objectives in Language Models
William Overman, Mohsen Bayati
Modern language model deployments must often balance competing objectives, for example, helpfulness versus harmlessness, cost versus accuracy, and reward versus safety. We introduc…
Can We Validate Counterfactual Estimations in the Presence of General Network Interference?
Sadegh Shirani, Yuwei Luo, William Overman +2
Randomized experiments have become a cornerstone of evidence-based decision-making in contexts ranging from online platforms to public health. However, in experimental settings wit…
Higher-Order Causal Message Passing for Experimentation with Complex Interference
Mohsen Bayati, Yuwei Luo, William Overman +2
Accurate estimation of treatment effects is essential for decision-making across various scientific fields. This task, however, becomes challenging in areas like social sciences an…
Improved Regret Bound for Safe Reinforcement Learning via Tighter Cost Pessimism and Reward Optimism
Kihyun Yu, Duksang Lee, William Overman +1
This paper studies the safe reinforcement learning problem formulated as an episodic finite-horizon tabular constrained Markov decision process with an unknown transition kernel an…
Aligning Model Properties via Conformal Risk Control
William Overman, Jacqueline Jil Vallon, Mohsen Bayati
AI model alignment is crucial due to inadvertent biases in training data and the underspecified machine learning pipeline, where models with excellent test metrics may not meet end…