{"total":34,"rows":[{"id":"2403.12350","title":"Friendly Sharpness-Aware Minimization","created":"2024-03-19","cited_by_count":53,"area":null},{"id":"2106.08208","title":"SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients","created":"2021-06-15","cited_by_count":40,"area":null},{"id":"2212.00974","title":"Faster Adaptive Federated Learning","created":"2022-12-02","cited_by_count":35,"area":null},{"id":"2103.17182","title":"Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization","created":"2021-03-31","cited_by_count":28,"area":null},{"id":"2206.04613","title":"Explicit Regularization in Overparametrized Models via Noise Injection","created":"2022-06-09","cited_by_count":26,"area":null},{"id":"2109.05109","title":"Toward Communication Efficient Adaptive Gradient Method","created":"2021-09-10","cited_by_count":25,"area":null},{"id":"2402.07114","title":"Towards Quantifying the Preconditioning Effect of Adam","created":"2024-02-11","cited_by_count":19,"area":null},{"id":"2103.01338","title":"Acceleration via Fractal Learning Rate Schedules","created":"2021-03-01","cited_by_count":13,"area":null},{"id":"2312.07335","title":"Momentum Particle Maximum Likelihood","created":"2023-12-12","cited_by_count":12,"area":null},{"id":"2408.11979","title":"Only Strict Saddles in the Energy Landscape of Predictive Coding Networks?","created":"2024-08-21","cited_by_count":10,"area":null},{"id":"2106.02985","title":"Escaping Saddle Points Faster with Stochastic Momentum","created":"2021-06-05","cited_by_count":8,"area":null},{"id":"2210.17478","title":"TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization","created":"2022-10-31","cited_by_count":8,"area":null},{"id":"2303.05101","title":"Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal Metrics","created":"2023-03-09","cited_by_count":8,"area":null},{"id":"2602.22681","title":"Accelerating LLM Pre-Training through Flat-Direction Dynamics Enhancement","created":"2026-02-26","cited_by_count":8,"area":null},{"id":"2407.00649","title":"Particle Semi-Implicit Variational Inference","created":"2024-06-30","cited_by_count":7,"area":null},{"id":"2302.00252","title":"QLABGrad: a Hyperparameter-Free and Convergence-Guaranteed Scheme for Deep Learning","created":"2023-02-01","cited_by_count":6,"area":null},{"id":"2506.00181","title":"On the Interaction of Batch Noise, Adaptivity, and Compression, under $(L_0,L_1)$-Smoothness: An SDE Approach","created":"2025-05-30","cited_by_count":4,"area":null},{"id":"2507.15290","title":"Feel-Good Thompson Sampling for Contextual Bandits: a Markov Chain Monte Carlo Showdown","created":"2025-07-21","cited_by_count":4,"area":null},{"id":"2106.03763","title":"Vanishing Curvature and the Power of Adaptive Methods in Randomly Initialized Deep Networks","created":"2021-06-07","cited_by_count":3,"area":null},{"id":"2304.03486","title":"Can we learn better with hard samples?","created":"2023-04-07","cited_by_count":3,"area":null},{"id":"2404.08624","title":"Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks","created":"2024-04-12","cited_by_count":3,"area":null},{"id":"2412.13715","title":"SSE-SAM: Balancing Head and Tail Classes Gradually through Stage-Wise SAM","created":"2024-12-18","cited_by_count":3,"area":null},{"id":"2302.06103","title":"FedDA: Faster Framework of Local Adaptive Gradient Methods via Restarted Dual Averaging","created":"2023-02-13","cited_by_count":2,"area":null},{"id":"2405.01606","title":"Enhancing the Trainability of Variational Quantum Circuits with Regularization Strategies","created":"2024-05-02","cited_by_count":2,"area":null},{"id":"2509.19930","title":"How deep is your network? Deep vs. shallow learning of transfer operators","created":"2025-09-24","cited_by_count":2,"area":null},{"id":"2204.10964","title":"Statistical inference of travelers' route choice preferences with system-level data","created":"2022-04-23","cited_by_count":1,"area":null},{"id":"2212.14150","title":"A Dynamics Theory of Implicit Regularization in Deep Low-Rank Matrix Factorization","created":"2022-12-29","cited_by_count":1,"area":null},{"id":"2402.13810","title":"The Expected Loss of Preconditioned Langevin Dynamics Reveals the Hessian Rank","created":"2024-02-21","cited_by_count":1,"area":null},{"id":"2604.10373","title":"Shuffling the Data, Stretching the Step-size: Sharper Bias in constant step-size SGD","created":"2026-04-11","cited_by_count":1,"area":null},{"id":"2605.09331","title":"Dimension-Free Saddle-Point Escape in Muon","created":"2026-05-10","cited_by_count":1,"area":null},{"id":"2112.14872","title":"Local Quadratic Convergence of Stochastic Gradient Descent with Adaptive Step Size","created":"2021-12-30","cited_by_count":0,"area":null},{"id":"2205.13753","title":"HOUDINI: Escaping from Moderately Constrained Saddles","created":"2022-05-27","cited_by_count":0,"area":null},{"id":"2309.11378","title":"Preconditioned Federated Learning","created":"2023-09-20","cited_by_count":0,"area":null},{"id":"2605.02317","title":"Anon: Extrapolating Adaptivity Beyond SGD and Adam","created":"2026-05-04","cited_by_count":0,"area":null}],"next_cursor":null}