Optimization

Learning to Learn by Zeroth-Order Oracle ICLR 2020 Learning to Link ICLR 2020 Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees AAAI 2020 Learning to Optimize on SPD Manifolds CVPR 2020 Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees ICLR 2020 Learning to Rank Learning Curves ICML 2020 Learning to Reason: Leveraging Neural Networks for Approximate DNF Counting AAAI 2020 Learning to Score Behaviors for Guided Policy Optimization ICML 2020 Learning to Select Base Classes for Few-Shot Classification CVPR 2020 Learning to Slide Unknown Objects with Differentiable Physics Simulations RSS 2020 Learning to Structure an Image With Few Colors CVPR 2020 Learning to Super Resolve Intensity Images From Events CVPR 2020 Learning What to Defer for Maximum Independent Sets ICML 2020 Learning with Differentiable Pertubed Optimizers NeurIPS 2020 Learning with Fenchel-Young losses JMLR 2020 Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions NeurIPS 2020 Learning With Subquadratic Regularization : A Primal-Dual Approach IJCAI 2020 Leverage Score Sampling for Faster Accelerated Regression and ERM ALT 2020 Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning NeurIPS 2020 Leveraging Discourse Rewards for Document-Level Neural Machine Translation COLING 2020 Leveraging Predictions in Smoothed Online Convex Optimization via Gradient-based Algorithms NeurIPS 2020 LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud Based Deep Networks CVPR 2020 Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation EMNLP 2020 Linear Convergence of Adaptive Stochastic Gradient Descent AISTATS 2020 Linear Convergence of Randomized Primal-Dual Coordinate Method for Large-scale Linear Constrained Convex Programming ICML 2020