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Convex Optimization

864 papers

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Papers

Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions NeurIPS 2016 CVXPY: A Python-Embedded Modeling Language for Convex Optimization JMLR 2016 Designing smoothing functions for improved worst-case competitive ratio in online optimization NeurIPS 2016 Epigraph projections for fast general convex programming ICML 2016 Estimation from Indirect Supervision with Linear Moments ICML 2016 Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling ICML 2016 Fast Algorithms for Linear and Kernel SVM+ CVPR 2016 Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier ICML 2016 Finite Sample Prediction and Recovery Bounds for Ordinal Embedding NeurIPS 2016 Iterative Hessian Sketch: Fast and Accurate Solution Approximation for Constrained Least-Squares JMLR 2016 Iterative Regularization for Learning with Convex Loss Functions JMLR 2016 Large-Scale Semantic 3D Reconstruction: An Adaptive Multi-Resolution Model for Multi-Class Volumetric Labeling CVPR 2016 MOCCA: Mirrored Convex/Concave Optimization for Nonconvex Composite Functions JMLR 2016 On Lower and Upper Bounds in Smooth and Strongly Convex Optimization JMLR 2016 On the Statistical Limits of Convex Relaxations ICML 2016 Optimistic Bandit Convex Optimization NeurIPS 2016 Primary Object Segmentation in Videos via Alternate Convex Optimization of Foreground and Background Distributions CVPR 2016 SDP Relaxation with Randomized Rounding for Energy Disaggregation NeurIPS 2016 Stochastic Three-Composite Convex Minimization NeurIPS 2016 Stochastic Variance Reduction Methods for Saddle-Point Problems NeurIPS 2016 Structure-Blind Signal Recovery NeurIPS 2016 Sub-sampled Newton Methods with Non-uniform Sampling NeurIPS 2016 Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization CVPR 2016 The non-convex Burer-Monteiro approach works on smooth semidefinite programs NeurIPS 2016 Tight Complexity Bounds for Optimizing Composite Objectives NeurIPS 2016