Optimization

Online Linear Optimization with Many Hints NeurIPS 2020 Online Neural Connectivity Estimation with Noisy Group Testing NeurIPS 2020 Online Non-Convex Optimization with Imperfect Feedback NeurIPS 2020 Online Parameter-Free Learning of Multiple Low Variance Tasks UAI 2020 Online Revenue Maximization for Server Pricing IJCAI 2020 On Minimax Optimality of GANs for Robust Mean Estimation AISTATS 2020 On Performance Estimation in Automatic Algorithm Configuration AAAI 2020 On Robustness in Qualitative Constraint Networks IJCAI 2020 On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics JMLR 2020 On the Acceleration of Deep Learning Model Parallelism With Staleness CVPR 2020 On the Complexity Analysis of the Primal Solutions for the Accelerated Randomized Dual Coordinate Ascent JMLR 2020 On the Convergence of FedAvg on Non-IID Data ICLR 2020 On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems COLT 2020 On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms AISTATS 2020 On the distance between two neural networks and the stability of learning NeurIPS 2020 On the Generalization Benefit of Noise in Stochastic Gradient Descent ICML 2020 On the Global Convergence of Training Deep Linear ResNets ICLR 2020 On the Global Optimality of Model-Agnostic Meta-Learning ICML 2020 On the Iteration Complexity of Hypergradient Computation ICML 2020 On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them NeurIPS 2020 On the Max-Min Fair Stochastic Allocation of Indivisible Goods AAAI 2020 On the Noisy Gradient Descent that Generalizes as SGD ICML 2020 On the Optimal Weighted ℓ_2 Regularization in Overparameterized Linear Regression NeurIPS 2020 On the Robustness and Training Dynamics of Raw Waveform Models Interspeech 2020 On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples NeurIPS 2020