Optimization

Continual Learning With Extended Kronecker-Factored Approximate Curvature CVPR 2020 Continuous-time Lower Bounds for Gradient-based Algorithms ICML 2020 Contracting Implicit Recurrent Neural Networks: Stable Models with Improved Trainability L4DC 2020 Controllable Orthogonalization in Training DNNs CVPR 2020 Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling AISTATS 2020 Convergence of a Stochastic Gradient Method with Momentum for Non-Smooth Non-Convex Optimization ICML 2020 Convergence of Gradient Methods on Bilinear Zero-Sum Games ICLR 2020 Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters NeurIPS 2020 Convergence Rates for the Stochastic Gradient Descent Method for Non-Convex Objective Functions JMLR 2020 Convergence Rates of a Momentum Algorithm with Bounded Adaptive Step Size for Nonconvex Optimization ACML 2020 Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections JMLR 2020 Converging to Team-Maxmin Equilibria in Zero-Sum Multiplayer Games ICML 2020 Convex Geometry of Two-Layer ReLU Networks: Implicit Autoencoding and Interpretable Models AISTATS 2020 Convex optimization based on global lower second-order models NeurIPS 2020 Convex Programming for Estimation in Nonlinear Recurrent Models JMLR 2020 Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space ICML 2020 CO-Optimal Transport NeurIPS 2020 Coresets for Data-efficient Training of Machine Learning Models ICML 2020 Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples UAI 2020 Coresets for Near-Convex Functions NeurIPS 2020 Coresets for Regressions with Panel Data NeurIPS 2020 Coresets for Robust Training of Deep Neural Networks against Noisy Labels NeurIPS 2020 Coresets via Bilevel Optimization for Continual Learning and Streaming NeurIPS 2020 Correlation Robust Influence Maximization NeurIPS 2020 Costly Zero Order Oracles COLT 2020