37 citations · 40 across the 5 of their papers we have counts for
4 papers · 1 filter
Isoperimetry is All We Need: Langevin Posterior Sampling for RL with Sublinear Regret
Emilio Jorge, Christos Dimitrakakis, Debabrota Basu
Common assumptions, like linear or RKHS models, and Gaussian or log-concave posteriors over the models, do not explain practical success of RL across a wider range of distributions…
Minimax-Bayes Reinforcement Learning
Thomas Kleine Buening, Christos Dimitrakakis, Hannes Eriksson +2
While the Bayesian decision-theoretic framework offers an elegant solution to the problem of decision making under uncertainty, one question is how to appropriately select the prio…
Inferential Induction: A Novel Framework for Bayesian Reinforcement Learning
Hannes Eriksson, Emilio Jorge, Christos Dimitrakakis +2
Bayesian reinforcement learning (BRL) offers a decision-theoretic solution for reinforcement learning. While "model-based" BRL algorithms have focused either on maintaining a poste…
Do Kernel and Neural Embeddings Help in Training and Generalization?
Arman Rahbar, Emilio Jorge, Devdatt Dubhashi +1
Recent results on optimization and generalization properties of neural networks showed that in a simple two-layer network, the alignment of the labels to the eigenvectors of the co…