latent state

Representation Learning in Deep RL via Discrete Information Bottleneck AISTATS 2023 Switching Autoregressive Low-rank Tensor Models NeurIPS 2023 The Ordered Matrix Dirichlet for State-Space Models AISTATS 2023 Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach ICML 2022 ForeSI: Success-Aware Visual Navigation Agent WACV 2022 Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics ICLR 2022 Variational Mixtures of ODEs for Inferring Cellular Gene Expression Dynamics ICML 2022 When Is Partially Observable Reinforcement Learning Not Scary? COLT 2022 Graph Gamma Process Linear Dynamical Systems AISTATS 2021 Non-Stationary Off-Policy Optimization AISTATS 2021 Provable Rich Observation Reinforcement Learning with Combinatorial Latent States ICLR 2021 Reinforcement Learning in Reward-Mixing MDPs NeurIPS 2021 Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains NeurIPS 2021 Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning CoRL 2020 Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning ICML 2020 Latent Bandits Revisited NeurIPS 2020 Learning the Linear Quadratic Regulator from Nonlinear Observations NeurIPS 2020 Particle Filter Recurrent Neural Networks AAAI 2020 Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning NeurIPS 2020 Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatment NeurIPS 2020 Learning Dynamic Generator Model by Alternating Back-Propagation through Time AAAI 2019 Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models ICML 2019 Learning and Communicating the Latent States of Human-Machine Collaboration IJCAI 2018 Spectral Filtering for General Linear Dynamical Systems NeurIPS 2018 A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning NeurIPS 2017