245 results on '"Benjamin Van Roy"'
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2. Posterior Sampling for Continuing Environments.
3. An Information-Theoretic Analysis of In-Context Learning.
4. Efficient Exploration for LLMs.
5. Nonstationary Bandit Learning via Predictive Sampling.
6. Scalable Neural Contextual Bandit for Recommender Systems.
7. Deep Exploration for Recommendation Systems.
8. Approximate Thompson Sampling via Epistemic Neural Networks.
9. Leveraging Demonstrations to Improve Online Learning: Quality Matters.
10. Choice between Partial Trajectories.
11. Aligning AI Agents via Information-Directed Sampling.
12. The Need for a Big World Simulator: A Scientific Challenge for Continual Learning.
13. Satisficing Exploration for Deep Reinforcement Learning.
14. Information-Theoretic Foundations for Neural Scaling Laws.
15. Information-Theoretic Foundations for Machine Learning.
16. Exploration Unbound.
17. Adaptive Crowdsourcing Via Self-Supervised Learning.
18. Reinforcement Learning, Bit by Bit.
19. Evaluating high-order predictive distributions in deep learning.
20. Epistemic Neural Networks.
21. A Definition of Continual Reinforcement Learning.
22. Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States.
23. Satisficing in Time-Sensitive Bandit Learning.
24. Bayesian Reinforcement Learning with Limited Cognitive Load.
25. Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models.
26. Continual Learning as Computationally Constrained Reinforcement Learning.
27. Maintaining Plasticity via Regenerative Regularization.
28. A Definition of Non-Stationary Bandits.
29. On the Convergence of Bounded Agents.
30. Non-Stationary Contextual Bandit Learning via Neural Predictive Ensemble Sampling.
31. RLHF and IIA: Perverse Incentives.
32. Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping.
33. The Value of Information When Deciding What to Learn.
34. Deciding What to Learn: A Rate-Distortion Approach.
35. An Information-Theoretic Framework for Deep Learning.
36. The Neural Testbed: Evaluating Joint Predictions.
37. An Analysis of Ensemble Sampling.
38. Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning.
39. An Information-Theoretic Analysis of Compute-Optimal Neural Scaling Laws.
40. Fine-Tuning Language Models via Epistemic Neural Networks.
41. Posterior Sampling for Continuing Environments.
42. Between Rate-Distortion Theory & Value Equivalence in Model-Based Reinforcement Learning.
43. On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning.
44. Gaussian Imagination in Bandit Learning.
45. Is Stochastic Gradient Descent Near Optimal?
46. Sample Complexity versus Depth: An Information Theoretic Analysis.
47. Inclusive Artificial Intelligence.
48. Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping.
49. Robustness of Epinets against Distributional Shifts.
50. Nonstationary Bandit Learning via Predictive Sampling.
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