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Stochastic Processes

2667 directly classified papers

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Papers

PEP: Parameter Ensembling by Perturbation NIPS 2020
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks NIPS 2020
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems NIPS 2020
Quantitative Propagation of Chaos for SGD in Wide Neural Networks NIPS 2020
Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards NIPS 2020
SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology NIPS 2020
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients NIPS 2020
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty NIPS 2020
Depth Uncertainty in Neural Networks NIPS 2020
Zap Q-Learning With Nonlinear Function Approximation NIPS 2020
Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis NIPS 2020
Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes NIPS 2020
Semi-Cyclic Stochastic Gradient Descent ICML 2019
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions ICML 2019
Memory in Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity From Spatiotemporal Dynamics CVPR 2019
Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit COLT 2019
Fast and Accurate Stochastic Gradient Estimation NIPS 2019
Predicting Sick Patient Volume in a Pediatric Outpatient Setting using Time Series Analysis MLHC 2019
Neural Jump Stochastic Differential Equations NIPS 2019
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation NIPS 2019
Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement ICML 2019
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data NIPS 2019
Efficient Diversified Mini-Batch Selection using Variable High-layer Features ACML 2019
Finite-Time Error Bounds For Linear Stochastic Approximation andTD Learning COLT 2019
Exponentially convergent stochastic k-PCA without variance reduction NIPS 2019