Stochastic Methods

Accelerating Stochastic Gradient Descent using Predictive Variance Reduction NeurIPS 2013 Aggregating Predictions via Sequential Mini-Trading ACML 2013 An Approximate, Efficient LP Solver for LP Rounding NeurIPS 2013 Annealing between distributions by averaging moments NeurIPS 2013 Approximate Inference in Collective Graphical Models ICML 2013 A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning ICML 2013 Blind Calibration in Compressed Sensing using Message Passing Algorithms NeurIPS 2013 BudgetedSVM: A Toolbox for Scalable SVM Approximations JMLR 2013 Distributed and Adaptive Darting Monte Carlo through Regenerations AISTATS 2013 Dual Averaging and Proximal Gradient Descent for Online Alternating Direction Multiplier Method ICML 2013 Efficiently Sampling Probabilistic Programs via Program Analysis AISTATS 2013 Embed and Project: Discrete Sampling with Universal Hashing NeurIPS 2013 Estimation, Optimization, and Parallelism when Data is Sparse NeurIPS 2013 Fast Determinantal Point Process Sampling with Application to Clustering NeurIPS 2013 Large-Scale Learning with Less RAM via Randomization ICML 2013 Learning CRFs for Image Parsing with Adaptive Subgradient Descent ICCV 2013 Linear Convergence with Condition Number Independent Access of Full Gradients NeurIPS 2013 Loss-Proportional Subsampling for Subsequent ERM ICML 2013 Near-Optimal Entrywise Sampling for Data Matrices NeurIPS 2013 New Subsampling Algorithms for Fast Least Squares Regression NeurIPS 2013 Noise-Enhanced Associative Memories NeurIPS 2013 No more pesky learning rates ICML 2013 Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) NeurIPS 2013 O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions ICML 2013 Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models ICML 2013