← Optimization
Mathematics & Optimization › Optimization ›

Stochastic Methods

2788 directly classified papers

Papers per year

Papers

Guarantees of Stochastic Greedy Algorithms for Non-monotone Submodular Maximization with Cardinality Constraint AISTATS 2020
Best Arm Identification for Cascading Bandits in the Fixed Confidence Setting ICML 2020
Modeling Noisy Annotations for Crowd Counting NIPS 2020
A Framework for Sample Efficient Interval Estimation with Control Variates AISTATS 2020
Bisection-Based Pricing for Repeated Contextual Auctions against Strategic Buyer ICML 2020
Optimal Sequential Maximization: One Interview is Enough! ICML 2020
Fourier Sparse Leverage Scores and Approximate Kernel Learning NIPS 2020
Generalised Bayesian Filtering via Sequential Monte Carlo NIPS 2020
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization NIPS 2020
Coresets for Near-Convex Functions NIPS 2020
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence NIPS 2020
Adaptive Online Estimation of Piecewise Polynomial Trends NIPS 2020
Minibatch Stochastic Approximate Proximal Point Methods NIPS 2020
Amortized Nesterov’s Momentum: A Robust Momentum and Its Application to Deep Learning UAI 2020
Safe Optimal Control Using Stochastic Barrier Functions and Deep Forward-Backward SDEs CORL 2020
Batch Stationary Distribution Estimation ICML 2020
Naive Exploration is Optimal for Online LQR ICML 2020
Stochastic Hamiltonian Gradient Methods for Smooth Games ICML 2020
Improved Optimistic Algorithms for Logistic Bandits ICML 2020
Multinomial Logit Bandit with Low Switching Cost ICML 2020
Structure Adaptive Algorithms for Stochastic Bandits ICML 2020
Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently ICML 2020
Estimating the Error of Randomized Newton Methods: A Bootstrap Approach ICML 2020
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent ICML 2020
On Approximate Thompson Sampling with Langevin Algorithms ICML 2020