Optimization

On the Bias-Variance-Cost Tradeoff of Stochastic Optimization NeurIPS 2021 On the Convergence of Communication-Efficient Local SGD for Federated Learning AAAI 2021 On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms NeurIPS 2021 On the Convergence of Step Decay Step-Size for Stochastic Optimization NeurIPS 2021 On the Convergence of Stochastic Compositional Gradient Descent Ascent Method IJCAI 2021 On the difficulty of unbiased alpha divergence minimization ICML 2021 On the Effects of Transformer Size on In- and Out-of-Domain Calibration EMNLP 2021 On the Faster Alternating Least-Squares for CCA AISTATS 2021 On-the-fly Rectification for Robust Large-Vocabulary Topic Inference ICML 2021 On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning AISTATS 2021 On the Impossibility of Global Convergence in Multi-Loss Optimization ICLR 2021 On the Inherent Regularization Effects of Noise Injection During Training ICML 2021 On the Linear Convergence of Policy Gradient Methods for Finite MDPs AISTATS 2021 On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression AISTATS 2021 On the Optimal Efficiency of A* with Dominance Pruning AAAI 2021 On the Origin of Implicit Regularization in Stochastic Gradient Descent ICLR 2021 On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths ICML 2021 On the PTAS for Maximin Shares in an Indivisible Mixed Manna AAAI 2021 On the Rate of Convergence of Regularized Learning in Games: From Bandits and Uncertainty to Optimism and Beyond NeurIPS 2021 On the Role of Optimization in Double Descent: A Least Squares Study NeurIPS 2021 On the Second-order Convergence Properties of Random Search Methods NeurIPS 2021 On the Stability Properties and the Optimization Landscape of Training Problems with Squared Loss for Neural Networks and General Nonlinear Conic Approximation Schemes JMLR 2021 On the Suboptimality of Negative Momentum for Minimax Optimization AISTATS 2021 On the Value of Infinite Gradients in Variational Autoencoder Models NeurIPS 2021 On the Verification of Neural ODEs with Stochastic Guarantees AAAI 2021