Stochastic Processes

Kernel-Based Reinforcement Learning in Robust Markov Decision Processes ICML 2019 Kernel Trajectory Maps for Multi-Modal Probabilistic Motion Prediction CoRL 2019 Large Scale Evolving Graphs with Burst Detection IJCAI 2019 Large Scale Markov Decision Processes with Changing Rewards NeurIPS 2019 Learning deep kernels for exponential family densities ICML 2019 Learning Deep Stochastic Optimal Control Policies Using Forward-Backward SDEs RSS 2019 Learning Hawkes Processes Under Synchronization Noise ICML 2019 Learning Heterogeneous Spatial-Temporal Representation for Bike-Sharing Demand Prediction AAAI 2019 Learning Linear Dynamical Systems with Semi-Parametric Least Squares COLT 2019 Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model NeurIPS 2019 Learning Protein Structure with a Differentiable Simulator ICLR 2019 Learning to Explore Intrinsic Saliency for Stereoscopic Video CVPR 2019 LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models AISTATS 2019 Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond NeurIPS 2019 Log-concave sampling: Metropolis-Hastings algorithms are fast JMLR 2019 Maximum Entropy-Regularized Multi-Goal Reinforcement Learning ICML 2019 Maximum Expected Hitting Cost of a Markov Decision Process and Informativeness of Rewards NeurIPS 2019 Maximum Mean Discrepancy Gradient Flow NeurIPS 2019 Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit COLT 2019 Memory Bounded Open-Loop Planning in Large POMDPs Using Thompson Sampling AAAI 2019 Memory in Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity From Spatiotemporal Dynamics CVPR 2019 Meta-Descent for Online, Continual Prediction AAAI 2019 Meta-Learning For Stochastic Gradient MCMC ICLR 2019 Minimax Learning of Ergodic Markov Chains ALT 2019 Minimax Online Prediction of Varying Bernoulli Process under Variational Approximation ACML 2019