stochastic optimization

Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences ICML 2022 Why do We Need Large Batchsizes in Contrastive Learning? A Gradient-Bias Perspective NeurIPS 2022 ZARTS: On Zero-order Optimization for Neural Architecture Search NeurIPS 2022 A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning ICLR 2021 A Biased Graph Neural Network Sampler with Near-Optimal Regret NeurIPS 2021 Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously ICML 2021 ACMo: Angle-Calibrated Moment Methods for Stochastic Optimization AAAI 2021 ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning AAAI 2021 Adaptive Proximal Gradient Methods for Structured Neural Networks NeurIPS 2021 A Hybrid Variance-Reduced Method for Decentralized Stochastic Non-Convex Optimization ICML 2021 An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning NeurIPS 2021 An Improved Analysis and Rates for Variance Reduction under Without-replacement Sampling Orders NeurIPS 2021 An Improved Analysis of Gradient Tracking for Decentralized Machine Learning NeurIPS 2021 An Inertial Newton Algorithm for Deep Learning JMLR 2021 An Online Method for A Class of Distributionally Robust Optimization with Non-convex Objectives NeurIPS 2021 An Optimistic Acceleration of AMSGrad for Nonconvex Optimization ACML 2021 Approximate Newton Methods JMLR 2021 A Regression Approach to Learning-Augmented Online Algorithms NeurIPS 2021 Asynchronous Stochastic Optimization Robust to Arbitrary Delays NeurIPS 2021 A Unified Convergence Analysis for Shuffling-Type Gradient Methods JMLR 2021 Bandit Learning with Delayed Impact of Actions NeurIPS 2021 Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization ICML 2021 Bilevel Optimization: Convergence Analysis and Enhanced Design ICML 2021 Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs NeurIPS 2021 Closing the Gap: Tighter Analysis of Alternating Stochastic Gradient Methods for Bilevel Problems NeurIPS 2021