Stochastic Processes

Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function NeurIPS 2020 Stochastic Nested Variance Reduction for Nonconvex Optimization JMLR 2020 Stochastic Normalizing Flows NeurIPS 2020 Stochastic Optimization for Performative Prediction NeurIPS 2020 Stochastic Optimization for Regularized Wasserstein Estimators ICML 2020 Stochastic Regret Minimization in Extensive-Form Games ICML 2020 Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty NeurIPS 2020 Stolen Probability: A Structural Weakness of Neural Language Models ACL 2020 Structure-Aware Feature Fusion for Unsupervised Domain Adaptation AAAI 2020 Structured Conditional Continuous Normalizing Flows for Efficient Amortized Inference in Graphical Models AISTATS 2020 Subgroup-based Rank-1 Lattice Quasi-Monte Carlo NeurIPS 2020 Sub-sampling for Efficient Non-Parametric Bandit Exploration NeurIPS 2020 Sundial: Fault-tolerant Clock Synchronization for Datacenters OSDI 2020 Symplectic Recurrent Neural Networks ICLR 2020 System Identification with Time-Aware Neural Sequence Models AAAI 2020 The Boomerang Sampler ICML 2020 The Break-Even Point on Optimization Trajectories of Deep Neural Networks ICLR 2020 The Discrete Gaussian for Differential Privacy NeurIPS 2020 The Gambler's Problem and Beyond ICLR 2020 The Implicit and Explicit Regularization Effects of Dropout ICML 2020 The Implicit Regularization of Stochastic Gradient Flow for Least Squares ICML 2020 The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation ICML 2020 The Mean-Squared Error of Double Q-Learning NeurIPS 2020 The route to chaos in routing games: When is price of anarchy too optimistic? NeurIPS 2020 Thompson Sampling for Unsupervised Sequential Selection ACML 2020