{"total":42,"rows":[{"id":"2104.11375","title":"Decentralized Federated Averaging","created":"2021-04-23","cited_by_count":114,"area":null},{"id":"2307.12306","title":"Tackling the Curse of Dimensionality with Physics-Informed Neural Networks","created":"2023-07-23","cited_by_count":79,"area":null},{"id":"2107.11433","title":"A general sample complexity analysis of vanilla policy gradient","created":"2021-07-23","cited_by_count":76,"area":null},{"id":"2205.12173","title":"Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums","created":"2022-05-24","cited_by_count":63,"area":null},{"id":"2305.16296","title":"A Guide Through the Zoo of Biased SGD","created":"2023-05-25","cited_by_count":40,"area":null},{"id":"2102.09385","title":"Convergence of stochastic gradient descent schemes for Lojasiewicz-landscapes","created":"2021-02-16","cited_by_count":28,"area":null},{"id":"2305.12475","title":"Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive Methods","created":"2023-05-21","cited_by_count":23,"area":null},{"id":"2110.01663","title":"Global Convergence and Stability of Stochastic Gradient Descent","created":"2021-10-04","cited_by_count":17,"area":null},{"id":"2105.04319","title":"A Bregman Learning Framework for Sparse Neural Networks","created":"2021-05-10","cited_by_count":15,"area":null},{"id":"2102.09924","title":"A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions","created":"2021-02-19","cited_by_count":14,"area":null},{"id":"2307.11782","title":"Convergence of Adam for Non-convex Objectives: Relaxed Hyperparameters and Non-ergodic Case","created":"2023-07-20","cited_by_count":14,"area":null},{"id":"2311.14632","title":"Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach","created":"2023-11-24","cited_by_count":14,"area":null},{"id":"2206.03907","title":"A Unified Convergence Theorem for Stochastic Optimization Methods","created":"2022-06-08","cited_by_count":12,"area":null},{"id":"2108.04620","title":"A proof of convergence for the gradient descent optimization method with random initializations in the training of neural networks with ReLU activation for piecewise linear target functions","created":"2021-08-10","cited_by_count":10,"area":null},{"id":"2503.02225","title":"Sharpness-Aware Minimization: General Analysis and Improved Rates","created":"2025-03-04","cited_by_count":10,"area":null},{"id":"2104.00277","title":"A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions","created":"2021-04-01","cited_by_count":9,"area":null},{"id":"2108.08106","title":"Existence, uniqueness, and convergence rates for gradient flows in the training of artificial neural networks with ReLU activation","created":"2021-08-18","cited_by_count":9,"area":null},{"id":"2112.07369","title":"Convergence proof for stochastic gradient descent in the training of deep neural networks with ReLU activation for constant target functions","created":"2021-12-13","cited_by_count":9,"area":null},{"id":"2402.05155","title":"Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks","created":"2024-02-07","cited_by_count":9,"area":null},{"id":"2110.11205","title":"Robustness through Data Augmentation Loss Consistency","created":"2021-10-21","cited_by_count":8,"area":null},{"id":"2410.15637","title":"Large Deviation Upper Bounds and Improved MSE Rates of Nonlinear SGD: Heavy-tailed Noise and Power of Symmetry","created":"2024-10-21","cited_by_count":7,"area":null},{"id":"2208.02083","title":"Gradient descent provably escapes saddle points in the training of shallow ReLU networks","created":"2022-08-03","cited_by_count":5,"area":null},{"id":"2301.12677","title":"Distributed Stochastic Optimization under a General Variance Condition","created":"2023-01-30","cited_by_count":4,"area":null},{"id":"2101.07259","title":"Guided parallelized stochastic gradient descent for delay compensation","created":"2021-01-17","cited_by_count":3,"area":null},{"id":"2312.00462","title":"Learning Unorthogonalized Matrices for Rotation Estimation","created":"2023-12-01","cited_by_count":3,"area":null},{"id":"2510.10690","title":"Second-order Optimization under Heavy-Tailed Noise: Hessian Clipping and Sample Complexity Limits","created":"2025-10-12","cited_by_count":3,"area":null},{"id":"2105.03033","title":"Towards Sharper Utility Bounds for Differentially Private Pairwise Learning","created":"2021-05-07","cited_by_count":2,"area":null},{"id":"2310.12969","title":"Demystifying the Myths and Legends of Nonconvex Convergence of SGD","created":"2023-10-19","cited_by_count":2,"area":null},{"id":"2403.00853","title":"Parallel Momentum Methods Under Biased Gradient Estimations","created":"2024-02-29","cited_by_count":2,"area":null},{"id":"2511.11512","title":"Collaborative Representation Learning for Alignment of Tactile, Language, and Vision Modalities","created":"2025-11-14","cited_by_count":2,"area":null},{"id":"2103.11860","title":"Spatio-Temporal Neural Network for Fitting and Forecasting COVID-19","created":"2021-03-22","cited_by_count":0,"area":null},{"id":"2104.13790","title":"FastAdaBelief: Improving Convergence Rate for Belief-based Adaptive Optimizers by Exploiting Strong Convexity","created":"2021-04-28","cited_by_count":0,"area":null},{"id":"2203.10973","title":"A Local Convergence Theory for the Stochastic Gradient Descent Method in Non-Convex Optimization With Non-isolated Local Minima","created":"2022-03-21","cited_by_count":0,"area":null},{"id":"2209.13803","title":"FedVeca: Federated Vectorized Averaging on Non-IID Data with Adaptive Bi-directional Global Objective","created":"2022-09-28","cited_by_count":0,"area":null},{"id":"2212.01848","title":"Convergence of ease-controlled Random Reshuffling gradient Algorithms under Lipschitz smoothness","created":"2022-12-04","cited_by_count":0,"area":null},{"id":"2403.16557","title":"Accelerating Federated Learning by Selecting Beneficial Herd of Local Gradients","created":"2024-03-25","cited_by_count":0,"area":null},{"id":"2409.06542","title":"Dynamic Decoupling of Placid Terminal Attractor-based Gradient Descent Algorithm","created":"2024-09-10","cited_by_count":0,"area":null},{"id":"2410.14667","title":"SGD Jittering: A Training Strategy for Robust and Accurate Model-Based Architectures","created":"2024-10-18","cited_by_count":0,"area":null},{"id":"2412.06070","title":"Stochastic Gradient Descent Revisited","created":"2024-12-08","cited_by_count":0,"area":null},{"id":"2510.02735","title":"Quantitative Convergence Analysis of Projected Stochastic Gradient Descent for Non-Convex Losses via the Goldstein Subdifferential","created":"2025-10-03","cited_by_count":0,"area":null},{"id":"2602.03357","title":"Achieving Linear Speedup for Composite Federated Learning","created":"2026-02-03","cited_by_count":0,"area":null},{"id":"2606.22150","title":"Parameterized Representations via Implicit Stochastic Modulation for High-Dimensional and High-Order Neural PDE Solvers","created":"2026-06-20","cited_by_count":0,"area":null}],"next_cursor":null}