Optimization

Accelerating Primal Solution Findings for Mixed Integer Programs Based on Solution Prediction AAAI 2020 A Fast Anderson-Chebyshev Acceleration for Nonlinear Optimization AISTATS 2020 A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization NeurIPS 2020 A General System of Differential Equations to Model First-Order Adaptive Algorithms JMLR 2020 A Graduated Filter Method for Large Scale Robust Estimation CVPR 2020 A Low Complexity Algorithm with O(√T) Regret and O(1) Constraint Violations for Online Convex Optimization with Long Term Constraints JMLR 2020 An Improved Latent Low Rank Representation for Automatic Subspace Clustering IJCAI 2020 Approximating shapes in images with low-complexity polygons CVPR 2020 A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration NeurIPS 2020 A Stochastic Derivative-Free Optimization Method with Importance Sampling: Theory and Learning to Control AAAI 2020 A Stochastic Derivative Free Optimization Method with Momentum ICLR 2020 A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach AISTATS 2020 Automatically Learning Compact Quality-aware Surrogates for Optimization Problems NeurIPS 2020 Auto-Tuning Structured Light by Optical Stochastic Gradient Descent CVPR 2020 Averaging Essential and Fundamental Matrices in Collinear Camera Settings CVPR 2020 Best Arm Identification in Spectral Bandits IJCAI 2020 Black-Box Methods for Restoring Monotonicity ICML 2020 Building Scalable and Flexible Cluster Managers Using Declarative Programming OSDI 2020 Bundle Adjustment on a Graph Processor CVPR 2020 Cakewalk Sampling AAAI 2020 Causal Bayesian Optimization AISTATS 2020 Cogradient Descent for Bilinear Optimization CVPR 2020 Complexity Guarantees for Polyak Steps with Momentum COLT 2020 Conformal Symplectic and Relativistic Optimization NeurIPS 2020 Convex optimization based on global lower second-order models NeurIPS 2020