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Optimization

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Papers

Less than a Single Pass: Stochastically Controlled Stochastic Gradient AISTATS 2017
Localized Lasso for High-Dimensional Regression AISTATS 2017
Frank-Wolfe Algorithms for Saddle Point Problems AISTATS 2017
A Framework for Optimal Matching for Causal Inference AISTATS 2017
Combinatorial Topic Models using Small-Variance Asymptotics AISTATS 2017
Stochastic Difference of Convex Algorithm and its Application to Training Deep Boltzmann Machines AISTATS 2017
Decentralized Collaborative Learning of Personalized Models over Networks AISTATS 2017
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets AISTATS 2017
Gradient Boosting on Stochastic Data Streams AISTATS 2017
A Stochastic Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization AISTATS 2017
Scalable Learning of Non-Decomposable Objectives AISTATS 2017
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe AISTATS 2017
Faster Coordinate Descent via Adaptive Importance Sampling AISTATS 2017
Efficient Algorithm for Sparse Tensor-variate Gaussian Graphical Models via Gradient Descent AISTATS 2017
A Unified Computational and Statistical Framework for Nonconvex Low-rank Matrix Estimation AISTATS 2017
Structured adaptive and random spinners for fast machine learning computations AISTATS 2017
Linear Convergence of Stochastic Frank Wolfe Variants AISTATS 2017
Fast rates with high probability in exp-concave statistical learning AISTATS 2017
Optimistic Planning for the Stochastic Knapsack Problem AISTATS 2017
Finite-sum Composition Optimization via Variance Reduced Gradient Descent AISTATS 2017
Thompson Sampling for Linear-Quadratic Control Problems AISTATS 2017
Spatial Decompositions for Large Scale SVMs AISTATS 2017
Learning from Conditional Distributions via Dual Embeddings AISTATS 2017
Convergence Rate of Stochastic k-means AISTATS 2017
Automated Inference with Adaptive Batches AISTATS 2017