13 citations · 13 across the 4 of their papers we have counts for
5 papers
Who Gets the Kidney? Human-AI Alignment, Indecision, and Moral Values
John P. Dickerson, Hadi Hosseini, Samarth Khanna +1
The rapid integration of Large Language Models (LLMs) in high-stakes decision-making -- such as allocating scarce resources like donor organs -- raises critical questions about the…
Syllabus: Portable Curricula for Reinforcement Learning Agents
Ryan Sullivan, Ryan Pégoud, Ameen Ur Rehman +5
Curriculum learning has been a quiet, yet crucial component of many high-profile successes of reinforcement learning. Despite this, it is still a niche topic that is not directly s…
Fair Polylog-Approximate Low-Cost Hierarchical Clustering
Marina Knittel, Max Springer, John Dickerson +1
Research in fair machine learning, and particularly clustering, has been crucial in recent years given the many ethical controversies that modern intelligent systems have posed. Ah…
Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks
Ryan Sullivan, Akarsh Kumar, Shengyi Huang +2
Most reinforcement learning methods rely heavily on dense, well-normalized environment rewards. DreamerV3 recently introduced a model-based method with a number of tricks that miti…
Ignorance is Almost Bliss: Near-Optimal Stochastic Matching With Few Queries
Avrim Blum, John P. Dickerson, Nika Haghtalab +3
The stochastic matching problem deals with finding a maximum matching in a graph whose edges are unknown but can be accessed via queries. This is a special case of stochastic -s…