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cs.LG2026
On the Equivalence of Random Network Distillation, Deep Ensembles, and Bayesian Inference
Moritz A. Zanger, Yijun Wu, Pascal R. Van der Vaart +2
Uncertainty quantification is central to safe and efficient deployments of deep learning models, yet many computationally practical methods lack lacking rigorous theoretical motiva…
cs.LG2026
VariBASed: Variational Bayes-Adaptive Sequential Monte-Carlo Planning for Deep Reinforcement Learning
Joery A. de Vries, Jinke He, Yaniv Oren +3
Optimally trading-off exploration and exploitation is the holy grail of reinforcement learning as it promises maximal data-efficiency for solving any task. Bayes-optimal agents ach…