Optimization

KungFu: Making Training in Distributed Machine Learning Adaptive OSDI 2020 Learning in Gated Neural Networks AISTATS 2020 Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis AAAI 2020 Learning to Auto Weight: Entirely Data-Driven and Highly Efficient Weighting Framework AAAI 2020 Learning to Optimize Non-Rigid Tracking CVPR 2020 Learning to solve TV regularised problems with unrolled algorithms NeurIPS 2020 Linear Convergence of Randomized Primal-Dual Coordinate Method for Large-scale Linear Constrained Convex Programming ICML 2020 Lipschitz-Certifiable Training with a Tight Outer Bound NeurIPS 2020 Local Regularizer Improves Generalization AAAI 2020 Low Bias Low Variance Gradient Estimates for Boolean Stochastic Networks ICML 2020 Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer CVPR 2020 LSM: Learning Subspace Minimization for Low-Level Vision CVPR 2020 MAGSAC++, a Fast, Reliable and Accurate Robust Estimator CVPR 2020 Manifold Identification for Ultimately Communication-Efficient Distributed Optimization ICML 2020 MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation CVPR 2020 Minibatch vs Local SGD for Heterogeneous Distributed Learning NeurIPS 2020 Modular Block-diagonal Curvature Approximations for Feedforward Architectures AISTATS 2020 Neural Network Branching for Neural Network Verification ICLR 2020 Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks ICML 2020 On the Acceleration of Deep Learning Model Parallelism With Staleness CVPR 2020 On the Convergence of SARAH and Beyond AISTATS 2020 On the Global Convergence of Training Deep Linear ResNets ICLR 2020 On the training dynamics of deep networks with L_2 regularization NeurIPS 2020 On the Variance of the Adaptive Learning Rate and Beyond ICLR 2020 Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities NeurIPS 2020