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A General Method for Amortizing Variational Filtering
Joseph Marino, Milan Cvitkovic, Yisong Yue
Neural Information Processing Systems http://nips.cc/
Sample Complexity of Interventional Causal Representation Learning
Consider a data-generation process that transforms low-dimensional latent causally-related variables to high-dimensional observed variables. Causal representation learning (CRL) is the process of using the observed data to recover the latent causal variables and the causal structure among them.
Leveraging the Exact Likelihood of Deep Latent Variable Models
Pierre-Alexandre Mattei, Jes Frellsen
Neural Information Processing Systems http://nips.cc/
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling
Tengyang Xie, Yifei Ma, Yu-Xiang Wang
Solving OPE is often the starting point in many RL applications. To tackle the problem of OPE, the idea of importance sampling (IS) corrects the mismatch in the distributions under the behavior policy and target policy.
