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Stochastic Processes

2,667 papers

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Papers

Projected Stein Variational Gradient Descent NIPS 2020
Faster Wasserstein Distance Estimation with the Sinkhorn Divergence NIPS 2020
Online Robust Regression via SGD on the l1 loss NIPS 2020
Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model NIPS 2020
Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function NIPS 2020
Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes NIPS 2020
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm NIPS 2020
Rethinking Learnable Tree Filter for Generic Feature Transform NIPS 2020
On the Power of Louvain in the Stochastic Block Model NIPS 2020
Delay and Cooperation in Nonstochastic Linear Bandits NIPS 2020
Stochastic Optimization for Performative Prediction NIPS 2020
Decision-Making with Auto-Encoding Variational Bayes NIPS 2020
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks NIPS 2020
Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits NIPS 2020
Sub-sampling for Efficient Non-Parametric Bandit Exploration NIPS 2020
Bayesian Probabilistic Numerical Integration with Tree-Based Models NIPS 2020
Stochastic Normalizing Flows NIPS 2020
Random Reshuffling is Not Always Better NIPS 2020
Learning Kernel Tests Without Data Splitting NIPS 2020
Subgroup-based Rank-1 Lattice Quasi-Monte Carlo NIPS 2020
Minibatch vs Local SGD for Heterogeneous Distributed Learning NIPS 2020
Bootstrapping neural processes NIPS 2020
The Mean-Squared Error of Double Q-Learning NIPS 2020
Bandit Samplers for Training Graph Neural Networks NIPS 2020
Sampling from a k-DPP without looking at all items NIPS 2020