convex optimization

Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks NeurIPS 2019 Efficient and Scalable Multi-Task Regression on Massive Number of Tasks AAAI 2019 Fairness risk measures ICML 2019 Fast and Robust Multi-Person 3D Pose Estimation From Multiple Views CVPR 2019 Fast Decomposable Submodular Function Minimization using Constrained Total Variation NeurIPS 2019 Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression NeurIPS 2019 Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems AISTATS 2019 First-Order Algorithms Converge Faster than $O(1/k)$ on Convex Problems ICML 2019 Generalization Bounds in the Predict-then-Optimize Framework NeurIPS 2019 Generalized Maximum Entropy Estimation JMLR 2019 Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses NeurIPS 2019 Hamiltonian descent for composite objectives NeurIPS 2019 Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso NeurIPS 2019 Improved Convergence for ℓ_1 and ℓ_∞ Regression via Iteratively Reweighted Least Squares ICML 2019 Lazifying Conditional Gradient Algorithms JMLR 2019 Learning (from) Deep Hierarchical Structure among Features AAAI 2019 Learning Logistic Circuits AAAI 2019 Learning Positive Functions with Pseudo Mirror Descent NeurIPS 2019 Lifted Hinge-Loss Markov Random Fields AAAI 2019 Lifting Vectorial Variational Problems: A Natural Formulation Based on Geometric Measure Theory and Discrete Exterior Calculus CVPR 2019 Linear Convergence of the Primal-Dual Gradient Method for Convex-Concave Saddle Point Problems without Strong Convexity AISTATS 2019 Local SGD Converges Fast and Communicates Little ICLR 2019 Logarithmic Regret for Online Gradient Descent Beyond Strong Convexity AISTATS 2019 Lower Bounds for Parallel and Randomized Convex Optimization COLT 2019 Low-Rank Tensor Completion With a New Tensor Nuclear Norm Induced by Invertible Linear Transforms CVPR 2019