However, it remains largely open to learn energy-based latent variable models (EBLVMs), exceptsomespecialcases. Thispaperpresents abi-levelscorematching (BiSM) method to learn EBLVMs with general structures by reformulating SM as a bilevel optimization problem. The higher level introduces a variational posterior of the latent variables and optimizes a modified SM objective, and the lower level optimizes the variational posterior to fit the true posterior.

Theoretically, we prove that SFB can learn an asymptotically-optimal predictor without test-domain labels. Empirically, we demonstrate the effectiveness of SFB on real and synthetic data.

Ensemble sampling serves as apractical approximation to Thompson sampling when maintaining anexact posterior distribution overmodel parameters iscomputationally intractable. In this paper, we establish a regret bound that ensures desirable behavior when ensemble sampling isapplied tothe linear bandit problem.

Bob) update her actions in the odd rounds so that, regardless of Bob's