3 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: Generalized Baselines
Benjamin Schiffer, Lucas Janson
Many practical applications of online reinforcement learning require the satisfaction of safety constraints while learning about the unknown environment. In this work, we establish…
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…