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Stochastic Processes

2,667 papers

Papers per year

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Papers

Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC NIPS 2020
Geometric Exploration for Online Control NIPS 2020
Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study NIPS 2020
Demystifying Orthogonal Monte Carlo and Beyond NIPS 2020
Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes NIPS 2020
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes NIPS 2020
Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards NIPS 2020
PEP: Parameter Ensembling by Perturbation NIPS 2020
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond NIPS 2020
Walsh-Hadamard Variational Inference for Bayesian Deep Learning NIPS 2020
Model Selection in Contextual Stochastic Bandit Problems NIPS 2020
Depth Uncertainty in Neural Networks NIPS 2020
Gibbs Sampling with People NIPS 2020
High-Dimensional Sparse Linear Bandits NIPS 2020
Robust, Accurate Stochastic Optimization for Variational Inference NIPS 2020
Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity NIPS 2020
Improved Techniques for Training Score-Based Generative Models NIPS 2020
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty NIPS 2020
Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis NIPS 2020
Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate NIPS 2020
Why are Adaptive Methods Good for Attention Models? NIPS 2020
Collapsing Bandits and Their Application to Public Health Intervention NIPS 2020
The Discrete Gaussian for Differential Privacy NIPS 2020
Decentralized Langevin Dynamics for Bayesian Learning NIPS 2020
Batched Coarse Ranking in Multi-Armed Bandits NIPS 2020