Optimization

Automatic Differentiation of Sketched Regression AISTATS 2020 Automatic Discovery of Heterogeneous Machine Learning Pipelines: An Application to Natural Language Processing COLING 2020 Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-Based Approach CVPR 2020 Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond NeurIPS 2020 AutoML-Zero: Evolving Machine Learning Algorithms From Scratch ICML 2020 Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation NeurIPS 2020 AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference NeurIPS 2020 Auto-Tuning Structured Light by Optical Stochastic Gradient Descent CVPR 2020 Auxiliary Task Reweighting for Minimum-data Learning NeurIPS 2020 Avoiding Side Effects in Complex Environments NeurIPS 2020 A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models AISTATS 2020 Background Data Resampling for Outlier-Aware Classification CVPR 2020 Balancing Training for Multilingual Neural Machine Translation ACL 2020 Bandits for BMO Functions ICML 2020 Batch Reinforcement Learning with Hyperparameter Gradients ICML 2020 Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation ICML 2020 Bayesian Optimization using Pseudo-Points IJCAI 2020 Beliefs We Can Believe in: Replacing Assumptions with Data in Real-Time Search AAAI 2020 Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method NeurIPS 2020 Best-first Enumeration Based on Bounding Conflicts, and its Application to Large-scale Hybrid Estimation (Extended Abstract) IJCAI 2020 Better Full-Matrix Regret via Parameter-Free Online Learning NeurIPS 2020 Beyond Network Pruning: a Joint Search-and-Training Approach IJCAI 2020 Beyond Pairwise Comparisons in Social Choice: A Setwise Kemeny Aggregation Problem AAAI 2020 Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition Under Reshuffling AAAI 2020 Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning NeurIPS 2020