Optimization

A Certifiably Globally Optimal Solution to Generalized Essential Matrix Estimation CVPR 2020 A Chance-Constrained Generative Framework for Sequence Optimization ICML 2020 A Characteristic Function Approach to Deep Implicit Generative Modeling CVPR 2020 A Closer Look at the Optimization Landscapes of Generative Adversarial Networks ICLR 2020 A Constraint-Based Approach to Learning and Explanation AAAI 2020 A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization AISTATS 2020 A convex optimization formulation for multivariate regression NeurIPS 2020 AdaGrad stepsizes: Sharp convergence over nonconvex landscapes JMLR 2020 Adam with Bandit Sampling for Deep Learning NeurIPS 2020 Adaptation Properties Allow Identification of Optimized Neural Codes NeurIPS 2020 Adapting to Smoothness: A More Universal Algorithm for Online Convex Optimization AAAI 2020 Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation ICLR 2020 Adaptive Experimental Design with Temporal Interference: A Maximum Likelihood Approach NeurIPS 2020 Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions AAAI 2020 Adaptive Gradient Descent without Descent ICML 2020 Adaptive Gradient Quantization for Data-Parallel SGD NeurIPS 2020 Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes NeurIPS 2020 Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment NeurIPS 2020 Adaptively Multi-Objective Adversarial Training for Dialogue Generation IJCAI 2020 Adaptive Neural Connections for Sparsity Learning WACV 2020 Adaptive Online Estimation of Piecewise Polynomial Trends NeurIPS 2020 Adaptive Online Kernel Sampling for Vertex Classification AISTATS 2020 Adaptive Rates for Total Variation Image Denoising JMLR 2020 Adaptive Smoothing for Path Integral Control JMLR 2020 A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints JMLR 2020