Optimization

Estimating the Fundamental Matrix Without Point Correspondences With Application to Transmission Imaging ICCV 2019 Fast and Accurate Least-Mean-Squares Solvers NeurIPS 2019 Fast and Robust Shortest Paths on Manifolds Learned from Data AISTATS 2019 Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression NeurIPS 2019 GENO -- GENeric Optimization for Classical Machine Learning NeurIPS 2019 Global Convergence of Gradient Descent for Deep Linear Residual Networks NeurIPS 2019 GPSfM: Global Projective SFM Using Algebraic Constraints on Multi-View Fundamental Matrices CVPR 2019 HyperAdam: A Learnable Task-Adaptive Adam for Network Training AAAI 2019 Implicit Regularization of Accelerated Methods in Hilbert Spaces NeurIPS 2019 Improving Generalization and Stability of Generative Adversarial Networks ICLR 2019 Iterative Least Trimmed Squares for Mixed Linear Regression NeurIPS 2019 Kernel Learning for Data-Driven Spectral Analysis of Koopman Operators ACML 2019 KNG: The K-Norm Gradient Mechanism NeurIPS 2019 Learning dynamic polynomial proofs NeurIPS 2019 Learning to Optimize in Swarms NeurIPS 2019 Learning Transformation Synchronization CVPR 2019 Lifting Vectorial Variational Problems: A Natural Formulation Based on Geometric Measure Theory and Discrete Exterior Calculus CVPR 2019 Linearly Converging Quasi Branch and Bound Algorithms for Global Rigid Registration ICCV 2019 Low-Distortion Social Welfare Functions AAAI 2019 Lower Bounds for Parallel and Randomized Convex Optimization COLT 2019 Low-Rank Semidefinite Programming for the MAX2SAT Problem AAAI 2019 Low-Rank Tensor Completion With a New Tensor Nuclear Norm Induced by Invertible Linear Transforms CVPR 2019 Minimal Solvers for Mini-Loop Closures in 3D Multi-Scan Alignment CVPR 2019 Necessary and Sufficient Geometries for Gradient Methods NeurIPS 2019 Nonconvex Matrix Factorization from Rank-One Measurements AISTATS 2019