2 papers
cs.GT2025
Geometry Meets Incentives: Sample-Efficient Incentivized Exploration with Linear Contexts
Benjamin Schiffer, Mark Sellke
In the incentivized exploration model, a principal aims to explore and learn over time by interacting with a sequence of self-interested agents. It has been recently understood tha…
stat.ML2025
Foundations of Safe Online Reinforcement Learning in the Linear Quadratic Regulator: -Regret
Benjamin Schiffer, Lucas Janson
Understanding how to efficiently learn while adhering to safety constraints is essential for using online reinforcement learning in practical applications. However, proving rigorou…