convergence rate

A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data ICLR 2024 autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm AISTATS 2024 Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems AISTATS 2024 Can Gaussian Sketching Converge Faster on a Preconditioned Landscape? ICML 2024 Classification of Data Generated by Gaussian Mixture Models Using Deep ReLU Networks JMLR 2024 Communication-Efficient Federated Learning with Accelerated Client Gradient CVPR 2024 Compressed and distributed least-squares regression: convergence rates with applications to federated learning JMLR 2024 Consistent Multiclass Algorithms for Complex Metrics and Constraints JMLR 2024 Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point ICML 2024 Convergence Behavior of an Adversarial Weak Supervision Method UAI 2024 Convergence of Some Convex Message Passing Algorithms to a Fixed Point ICML 2024 Debiasing Synthetic Data Generated by Deep Generative Models NeurIPS 2024 Decentralized Multi-Level Compositional Optimization Algorithms with Level-Independent Convergence Rate AISTATS 2024 Deep Learning for Computing Convergence Rates of Markov Chains NeurIPS 2024 Directional Smoothness and Gradient Methods: Convergence and Adaptivity NeurIPS 2024 Distribution Learning via Neural Differential Equations: A Nonparametric Statistical Perspective JMLR 2024 Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers ICML 2024 Efficient Sign-Based Optimization: Accelerating Convergence via Variance Reduction NeurIPS 2024 Efficient Stochastic Approximation of Minimax Excess Risk Optimization ICML 2024 Enabling Uncertainty Estimation in Iterative Neural Networks ICML 2024 Extragradient Type Methods for Riemannian Variational Inequality Problems AISTATS 2024 Faster Convergence with MultiWay Preferences AISTATS 2024 Fast two-time-scale stochastic gradient method with applications in reinforcement learning COLT 2024 Federated Representation Learning in the Under-Parameterized Regime ICML 2024 FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent ICLR 2024