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Stochastic Processes

2,667 papers

Papers per year

Papers

Corralling Stochastic Bandit Algorithms AISTATS 2021
Stochastic Gradient Descent Meets Distribution Regression AISTATS 2021
One-Sketch-for-All: Non-linear Random Features from Compressed Linear Measurements AISTATS 2021
Differentially Private Monotone Submodular Maximization Under Matroid and Knapsack Constraints AISTATS 2021
Hierarchical Inducing Point Gaussian Process for Inter-domian Observations AISTATS 2021
Tensor Networks for Probabilistic Sequence Modeling AISTATS 2021
Contrastive learning of strong-mixing continuous-time stochastic processes AISTATS 2021
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces AISTATS 2021
Learning Temporal Point Processes with Intermittent Observations AISTATS 2021
Combinatorial Gaussian Process Bandits with Probabilistically Triggered Arms AISTATS 2021
An Adaptive-MCMC Scheme for Setting Trajectory Lengths in Hamiltonian Monte Carlo AISTATS 2021
On Multilevel Monte Carlo Unbiased Gradient Estimation for Deep Latent Variable Models AISTATS 2021
Hidden Cost of Randomized Smoothing AISTATS 2021
Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates AISTATS 2021
A Technical Note on Non-Stationary Parametric Bandits: Existing Mistakes and Preliminary Solutions ALT 2021
Efficient sampling from the Bingham distribution ALT 2021
Submodular combinatorial information measures with applications in machine learning ALT 2021
Uncertainty quantification using martingales for misspecified Gaussian processes ALT 2021
Regret Minimization in Heavy-Tailed Bandits COLT 2021
The Last-Iterate Convergence Rate of Optimistic Mirror Descent in Stochastic Variational Inequalities COLT 2021
Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm COLT 2021
Outlier-Robust Learning of Ising Models Under Dobrushin’s Condition COLT 2021
On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning COLT 2021
Non-asymptotic approximations of neural networks by Gaussian processes COLT 2021
Convergence rates and approximation results for SGD and its continuous-time counterpart COLT 2021