Stochastic Methods

Simulation and Calibration of a Fully Bayesian Marked Multidimensional Hawkes Process with Dissimilar Decays ACML 2016 Slice Sampling on Hamiltonian Trajectories ICML 2016 Starting Small - Learning with Adaptive Sample Sizes ICML 2016 Stochastic Discrete Clenshaw-Curtis Quadrature ICML 2016 Stochastic Optimization for Large-scale Optimal Transport NeurIPS 2016 Stochastic Optimization for Multiview Representation Learning using Partial Least Squares ICML 2016 Stochastic Quasi-Newton Langevin Monte Carlo ICML 2016 Stochastic Variance Reduction for Nonconvex Optimization ICML 2016 Stochastic Variance Reduction Methods for Saddle-Point Problems NeurIPS 2016 Towards Stability and Optimality in Stochastic Gradient Descent AISTATS 2016 Variance-Reduced and Projection-Free Stochastic Optimization ICML 2016 Variance Reduction for Faster Non-Convex Optimization ICML 2016 Variance Reduction in Stochastic Gradient Langevin Dynamics NeurIPS 2016 Without-Replacement Sampling for Stochastic Gradient Methods NeurIPS 2016 A Complete Recipe for Stochastic Gradient MCMC NeurIPS 2015 Adaptive Hashing for Fast Similarity Search ICCV 2015 A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate ICML 2015 Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization NeurIPS 2015 Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care NeurIPS 2015 A Universal Catalyst for First-Order Optimization NeurIPS 2015 Averaged Least-Mean-Squares: Bias-Variance Trade-offs and Optimal Sampling Distributions AISTATS 2015 Beyond Convexity: Stochastic Quasi-Convex Optimization NeurIPS 2015 Cheap Bandits ICML 2015 Convergence Analysis of Prediction Markets via Randomized Subspace Descent NeurIPS 2015 Convergence rates of sub-sampled Newton methods NeurIPS 2015