Stochastic Methods

Interpreting prediction markets: a stochastic approach NeurIPS 2012 Large Scale Distributed Deep Networks NeurIPS 2012 MCMC for continuous-time discrete-state systems NeurIPS 2012 Online allocation and homogeneous partitioning for piecewise constant mean-approximation NeurIPS 2012 Optimal Regularized Dual Averaging Methods for Stochastic Optimization NeurIPS 2012 Risk-Aversion in Multi-armed Bandits NeurIPS 2012 Sparse Approximate Manifolds for Differential Geometric MCMC NeurIPS 2012 Spectral learning of linear dynamics from generalised-linear observations with application to neural population data NeurIPS 2012 Stochastic Gradient Descent with Only One Projection NeurIPS 2012 Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions NeurIPS 2012 Training sparse natural image models with a fast Gibbs sampler of an extended state space NeurIPS 2012 Trajectory-Based Short-Sighted Probabilistic Planning NeurIPS 2012 A Finite-Time Analysis of Multi-armed Bandits Problems with Kullback-Leibler Divergences COLT 2011 An Empirical Evaluation of Thompson Sampling NeurIPS 2011 Beating SGD: Learning SVMs in Sublinear Time NeurIPS 2011 Better Mini-Batch Algorithms via Accelerated Gradient Methods NeurIPS 2011 Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization COLT 2011 Distributed Delayed Stochastic Optimization NeurIPS 2011 Finite Time Analysis of Stratified Sampling for Monte Carlo NeurIPS 2011 Gaussian process modulated renewal processes NeurIPS 2011 Hashing Algorithms for Large-Scale Learning NeurIPS 2011 Hogwild!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent NeurIPS 2011 Inferring spike-timing-dependent plasticity from spike train data NeurIPS 2011 Monte Carlo Value Iteration with Macro-Actions NeurIPS 2011 Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning NeurIPS 2011