Optimization

Linear Dynamical Systems as a Core Computational Primitive NeurIPS 2020 Linearly Convergent Frank-Wolfe with Backtracking Line-Search AISTATS 2020 Linearly Converging Error Compensated SGD NeurIPS 2020 Linear Symmetric Quantization of Neural Networks for Low-precision Integer Hardware ICLR 2020 Linear Time Sinkhorn Divergences using Positive Features NeurIPS 2020 Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing NeurIPS 2020 Lipschitz-Certifiable Training with a Tight Outer Bound NeurIPS 2020 Lipschitz constant estimation of Neural Networks via sparse polynomial optimization ICLR 2020 Local Regularizer Improves Generalization AAAI 2020 Lookahead: A Far-sighted Alternative of Magnitude-based Pruning ICLR 2020 Look-ahead Meta Learning for Continual Learning NeurIPS 2020 Loss Control with Rank-one Covariance Estimate for Short-term Portfolio Optimization JMLR 2020 Loss Function Search for Face Recognition ICML 2020 Low Bias Low Variance Gradient Estimates for Boolean Stochastic Networks ICML 2020 Lower Bounds and Optimal Algorithms for Personalized Federated Learning NeurIPS 2020 Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems ICML 2020 Low-loss connection of weight vectors: distribution-based approaches ICML 2020 Low-Rank Bottleneck in Multi-head Attention Models ICML 2020 Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer CVPR 2020 Low-rank regularization and solution uniqueness in over-parameterized matrix sensing AISTATS 2020 Low-Variance Black-Box Gradient Estimates for the Plackett-Luce Distribution AAAI 2020 LSM: Learning Subspace Minimization for Low-Level Vision CVPR 2020 MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius ICLR 2020 MAGSAC++, a Fast, Reliable and Accurate Robust Estimator CVPR 2020 Manifold Identification for Ultimately Communication-Efficient Distributed Optimization ICML 2020