Stochastic Methods

Linear Thompson Sampling Revisited AISTATS 2017 Measuring Sample Quality with Kernels ICML 2017 Modelling the Working Week for Multi-Step Forecasting using Gaussian Process Regression IJCAI 2017 Near Optimal Sketching of Low-Rank Tensor Regression NeurIPS 2017 Nonlinear Acceleration of Stochastic Algorithms NeurIPS 2017 Online to Offline Conversions, Universality and Adaptive Minibatch Sizes NeurIPS 2017 Optimal Rates for Multi-pass Stochastic Gradient Methods JMLR 2017 Random Projection Filter Bank for Time Series Data NeurIPS 2017 Rapid Mixing Swendsen-Wang Sampler for Stochastic Partitioned Attractive Models AISTATS 2017 Restart and Random Walk in Local Search for Maximum Vertex Weight Cliques with Evaluations in Clustering Aggregation IJCAI 2017 Safe Adaptive Importance Sampling NeurIPS 2017 Simple strategies for recovering inner products from coarsely quantized random projections NeurIPS 2017 Sparse Accelerated Exponential Weights AISTATS 2017 Stochastic Constraint Programming with And-Or Branch-and-Bound IJCAI 2017 Stochastic Difference of Convex Algorithm and its Application to Training Deep Boltzmann Machines AISTATS 2017 Stochastic Gradient Monomial Gamma Sampler ICML 2017 Stochastic Modified Equations and Adaptive Stochastic Gradient Algorithms ICML 2017 Stochastic Submodular Maximization: The Case of Coverage Functions NeurIPS 2017 Storage Fit Learning with Unlabeled Data IJCAI 2017 Task-based End-to-end Model Learning in Stochastic Optimization NeurIPS 2017 XOR-Sampling for Network Design with Correlated Stochastic Events IJCAI 2017 Zap Q-Learning NeurIPS 2017 Adaptive Algorithms for Online Convex Optimization with Long-term Constraints ICML 2016 Adaptive Sampling for SGD by Exploiting Side Information ICML 2016 A Self-Correcting Variable-Metric Algorithm for Stochastic Optimization ICML 2016