← Optimization & Theory
Machine Learning › Optimization & Theory ›

Stochastic Methods

1077 directly classified papers

Papers per year

Papers

O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions ICML 2013
Aggregating Predictions via Sequential Mini-Trading ACML 2013
Estimation, Optimization, and Parallelism when Data is Sparse NIPS 2013
New Subsampling Algorithms for Fast Least Squares Regression NIPS 2013
Noise-Enhanced Associative Memories NIPS 2013
Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods NIPS 2012
Confidence-Weighted Linear Classification for Text Categorization JMLR 2012
Breaking the Curse of Kernelization: Budgeted Stochastic Gradient Descent for Large-Scale SVM Training JMLR 2012
A Stochastic Bandit Algorithm for Scratch Games ACML 2012
Entangled Monte Carlo NIPS 2012
Fast Resampling Weighted v-Statistics NIPS 2012
Adaptive MCMC with Bayesian Optimization AISTATS 2012
Evaluation of marginal likelihoods via the density of states AISTATS 2012
Adaptive Metropolis with Online Relabeling AISTATS 2012
Ancestor Sampling for Particle Gibbs NIPS 2012
Sparse Approximate Manifolds for Differential Geometric MCMC NIPS 2012
Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions NIPS 2012
Fully Bayesian inference for neural models with negative-binomial spiking NIPS 2012
MCMC for continuous-time discrete-state systems NIPS 2012
Optimal Regularized Dual Averaging Methods for Stochastic Optimization NIPS 2012
Online allocation and homogeneous partitioning for piecewise constant mean-approximation NIPS 2012
A Stochastic Gradient Method with an Exponential Convergence _Rate for Finite Training Sets NIPS 2012
Interpreting prediction markets: a stochastic approach NIPS 2012
Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions NIPS 2012
Efficient Sampling for Bipartite Matching Problems NIPS 2012