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Optimization

14,207 papers

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Papers

Quadratic Optimization with Orthogonality Constraints: Explicit Lojasiewicz Exponent and Linear Convergence of Line-Search Methods ICML 2016
Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks ICML 2016
Train faster, generalize better: Stability of stochastic gradient descent ICML 2016
Variance-Reduced and Projection-Free Stochastic Optimization ICML 2016
Solving Ridge Regression using Sketched Preconditioned SVRG ICML 2016
Non-negative Matrix Factorization under Heavy Noise ICML 2016
Extreme F-measure Maximization using Sparse Probability Estimates ICML 2016
Importance Sampling Tree for Large-scale Empirical Expectation ICML 2016
Starting Small - Learning with Adaptive Sample Sizes ICML 2016
Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms ICML 2016
Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling ICML 2016
Black-box Optimization with a Politician ICML 2016
No-Regret Algorithms for Heavy-Tailed Linear Bandits ICML 2016
Matrix Eigen-decomposition via Doubly Stochastic Riemannian Optimization ICML 2016
Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching ICML 2016
Gaussian quadrature for matrix inverse forms with applications ICML 2016
Train and Test Tightness of LP Relaxations in Structured Prediction ICML 2016
Stochastic Optimization for Multiview Representation Learning using Partial Least Squares ICML 2016
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization ICML 2016
On Graduated Optimization for Stochastic Non-Convex Problems ICML 2016
Stochastic Block BFGS: Squeezing More Curvature out of Data ICML 2016
The Sum-Product Theorem: A Foundation for Learning Tractable Models ICML 2016
Pareto Frontier Learning with Expensive Correlated Objectives ICML 2016
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits ICML 2016
Fast DPP Sampling for Nystrom with Application to Kernel Methods ICML 2016