Learning Graphical Models
When Y our AIs Deceive You: Challenges of Partial Observability in Reinforcement Learning from Human Feedback Leon Lang University of Amsterdam Davis Foote
Modeling the human as Boltzmann-rational w.r.t. a belief over trajectories, we prove conditions under which RLHF is
Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage
Typically, deep RL algorithms learn a policy in an online trial-and-error fashion using millions to billions of data.
Efficient Model-Free Exploration in Low-Rank MDPs
What are the right computational primitives for exploration?
QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers
Queuing network control determines the allocation of scarce resources to manage congestion, a fundamental problem in manufacturing, communications, and healthcare.
PAC Learning Linear Thresholds from Label Proportions
The goal is to train a good instance classifier.
Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation
Please refer to the website for more details.
Markov Equivalence and Consistency in Differentiable Structure Learning Chang Deng Kevin Bello
These results are then generalized to general models and likelihoods, where the same claims hold.