Optimization

What if Neural Networks had SVDs? NeurIPS 2020 What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? ICML 2020 When deep denoising meets iterative phase retrieval ICML 2020 Why are Adaptive Methods Good for Attention Models? NeurIPS 2020 Why Are Learned Indexes So Effective? ICML 2020 Why Attention? Analyze BiLSTM Deficiency and Its Remedies in the Case of NER AAAI 2020 Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity ICLR 2020 Why Having 10,000 Parameters in Your Camera Model Is Better Than Twelve CVPR 2020 Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rollout AISTATS 2020 Winnowing with Gradient Descent COLT 2020 Woodbury Transformations for Deep Generative Flows NeurIPS 2020 WoodFisher: Efficient Second-Order Approximation for Neural Network Compression NeurIPS 2020 Word-level Textual Adversarial Attacking as Combinatorial Optimization ACL 2020 You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings ICLR 2020 Zeroth Order Non-convex optimization with Dueling-Choice Bandits UAI 2020 A2BCD: Asynchronous Acceleration with Optimal Complexity ICLR 2019 A Bandit Approach to Maximum Inner Product Search AAAI 2019 A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs ICML 2019 ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery AISTATS 2019 A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication JMLR 2019 Abstraction based Output Range Analysis for Neural Networks NeurIPS 2019 Accelerated Alternating Projections for Robust Principal Component Analysis JMLR 2019 Accelerated Coordinate Descent with Arbitrary Sampling and Best Rates for Minibatches AISTATS 2019 Accelerated Incremental Gradient Descent using Momentum Acceleration with Scaling Factor IJCAI 2019 Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances ICML 2019