Stochastic Processes

FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis ICML 2020 Free Energy Wells and Overlap Gap Property in Sparse PCA COLT 2020 From Importance Sampling to Doubly Robust Policy Gradient ICML 2020 Generalized Entropy Regularization or: There’s Nothing Special about Label Smoothing ACL 2020 Generalized Tsallis Entropy Reinforcement Learning and Its Application to Soft Mobile Robots RSS 2020 Generating Diverse Translation from Model Distribution with Dropout EMNLP 2020 Generating Narrative Text in a Switching Dynamical System CoNLL 2020 Geometric Exploration for Online Control NeurIPS 2020 Gibbs Sampling with People NeurIPS 2020 Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems NeurIPS 2020 Gradient Temporal-Difference Learning with Regularized Corrections ICML 2020 Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent JMLR 2020 Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond NeurIPS 2020 Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks ICLR 2020 Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks NeurIPS 2020 Hierarchical Multi-Scale Gaussian Transformer for Stock Movement Prediction IJCAI 2020 High-Dimensional Sparse Linear Bandits NeurIPS 2020 How Does Noise Help Robustness? Explanation and Exploration under the Neural SDE Framework CVPR 2020 Hypermodels for Exploration ICLR 2020 Identifiability and Consistent Estimation of Nonparametric Translation Hidden Markov Models with General State Space JMLR 2020 Implicit regularization for deep neural networks driven by an Ornstein-Uhlenbeck like process COLT 2020 Improved Techniques for Training Score-Based Generative Models NeurIPS 2020 Index Tracking with Cardinality Constraints: A Stochastic Neural Networks Approach AAAI 2020 Infinitely deep neural networks as diffusion processes AISTATS 2020 Infinity Learning: Learning Markov Chains from Aggregate Steady-State Observations AAAI 2020