{"total":48,"rows":[{"id":"2210.14199","title":"Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models","created":"2022-10-25","cited_by_count":84,"area":null},{"id":"2209.05045","title":"Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization","created":"2022-09-12","cited_by_count":43,"area":null},{"id":"2104.06763","title":"Oracle Complexity in Nonsmooth Nonconvex Optimization","created":"2021-04-14","cited_by_count":39,"area":null},{"id":"2102.06489","title":"Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness","created":"2021-02-12","cited_by_count":37,"area":null},{"id":"2203.16462","title":"Convergence of gradient descent for deep neural networks","created":"2022-03-30","cited_by_count":24,"area":null},{"id":"2501.12666","title":"Explicit Eigenvalue Regularization Improves Sharpness-Aware Minimization","created":"2025-01-22","cited_by_count":21,"area":null},{"id":"2101.02696","title":"Accelerated, Optimal, and Parallel: Some Results on Model-Based Stochastic Optimization","created":"2021-01-07","cited_by_count":14,"area":null},{"id":"2110.01827","title":"When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint","created":"2021-10-05","cited_by_count":12,"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":"2106.03034","title":"Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization","created":"2021-06-06","cited_by_count":10,"area":null},{"id":"2310.03234","title":"Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization","created":"2023-10-05","cited_by_count":10,"area":null},{"id":"2108.02072","title":"Stochastic Subgradient Descent Escapes Active Strict Saddles on Weakly Convex Functions","created":"2021-08-04","cited_by_count":9,"area":null},{"id":"2202.04296","title":"A Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization","created":"2022-02-09","cited_by_count":8,"area":null},{"id":"2305.05828","title":"A Normal Map-Based Proximal Stochastic Gradient Method: Convergence and Identification Properties","created":"2023-05-10","cited_by_count":5,"area":null},{"id":"2305.17498","title":"A Model-Based Method for Minimizing CVaR and Beyond","created":"2023-05-27","cited_by_count":5,"area":null},{"id":"2310.08858","title":"Adam-family Methods with Decoupled Weight Decay in Deep Learning","created":"2023-10-13","cited_by_count":5,"area":null},{"id":"2604.16620","title":"Lower Bounds and Proximally Anchored SGD for Non-Convex Minimization Under Unbounded Variance","created":"2026-04-17","cited_by_count":5,"area":null},{"id":"2101.00935","title":"First-Order Methods for Convex Optimization","created":"2021-01-04","cited_by_count":4,"area":null},{"id":"2110.05852","title":"On the Self-Penalization Phenomenon in Feature Selection","created":"2021-10-12","cited_by_count":4,"area":null},{"id":"2301.03125","title":"Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation","created":"2023-01-09","cited_by_count":4,"area":null},{"id":"2312.01047","title":"A New Random Reshuffling Method for Nonsmooth Nonconvex Finite-sum Optimization","created":"2023-12-02","cited_by_count":4,"area":null},{"id":"2405.10815","title":"A Functional Model Method for Nonconvex Nonsmooth Conditional Stochastic Optimization","created":"2024-05-17","cited_by_count":4,"area":null},{"id":"2103.03570","title":"Second-order step-size tuning of SGD for non-convex optimization","created":"2021-03-05","cited_by_count":3,"area":null},{"id":"2304.09221","title":"Convergence of stochastic gradient descent under a local Lojasiewicz condition for deep neural networks","created":"2023-04-18","cited_by_count":3,"area":null},{"id":"2307.10053","title":"Stochastic Subgradient Methods with Guaranteed Global Stability in Nonsmooth Nonconvex Optimization","created":"2023-07-19","cited_by_count":3,"area":null},{"id":"2405.10289","title":"On the Uniform Convergence of Subdifferentials in Stochastic Optimization and Learning","created":"2024-05-16","cited_by_count":3,"area":null},{"id":"2507.15264","title":"On exploration of an interior mirror descent flow for stochastic nonconvex constrained problem","created":"2025-07-21","cited_by_count":3,"area":null},{"id":"2509.18025","title":"Deep Learning as the Disciplined Construction of Tame Objects","created":"2025-09-22","cited_by_count":3,"area":null},{"id":"2205.01457","title":"Efficient algorithms for implementing incremental proximal-point methods","created":"2022-05-03","cited_by_count":2,"area":null},{"id":"2404.09438","title":"Developing Lagrangian-based Methods for Nonsmooth Nonconvex Optimization","created":"2024-04-15","cited_by_count":2,"area":null},{"id":"2412.10663","title":"Memory-Efficient 4-bit Preconditioned Stochastic Optimization","created":"2024-12-14","cited_by_count":2,"area":null},{"id":"2609.11655","title":"Musec: MomentUm SpEctral Clipping for Stable Muon-type Training","created":"2026-09-10","cited_by_count":2,"area":null},{"id":"2105.13271","title":"OpReg-Boost: Learning to Accelerate Online Algorithms with Operator Regression","created":"2021-05-27","cited_by_count":1,"area":null},{"id":"2206.14981","title":"Randomized Coordinate Subgradient Method for Nonsmooth Composite Optimization","created":"2022-06-30","cited_by_count":1,"area":null},{"id":"2304.12522","title":"A New Inexact Proximal Linear Algorithm with Adaptive Stopping Criteria for Robust Phase Retrieval","created":"2023-04-25","cited_by_count":1,"area":null},{"id":"2403.11565","title":"Convergence of Decentralized Stochastic Subgradient-based Methods for Nonsmooth Nonconvex functions","created":"2024-03-18","cited_by_count":1,"area":null},{"id":"2502.05668","title":"The late-stage training dynamics of (stochastic) subgradient descent on homogeneous neural networks","created":"2025-02-08","cited_by_count":1,"area":null},{"id":"2106.10759","title":"Robust Regression via Model Based Methods","created":"2021-06-20","cited_by_count":0,"area":null},{"id":"2201.05250","title":"Consistent Approximations in Composite Optimization","created":"2022-01-13","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":"2308.08886","title":"Dual Gauss-Newton Directions for Deep Learning","created":"2023-08-17","cited_by_count":0,"area":null},{"id":"2409.10323","title":"On the Hardness of Meaningful Local Guarantees in Nonsmooth Nonconvex Optimization","created":"2024-09-16","cited_by_count":0,"area":null},{"id":"2505.07298","title":"Learning-Based Surrogate Method for Stochastic Optimization under Decision-Dependent Uncertainty with Adaptive Random Designs","created":"2025-05-12","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":"2510.18218","title":"DualHash: A Stochastic Primal-Dual Algorithm with Theoretical Guarantee for Deep Hashing","created":"2025-10-21","cited_by_count":0,"area":null},{"id":"2511.21115","title":"Nonconvex Penalized LAD Estimation in Partial Linear Models with DNNs: Asymptotic Analysis and Proximal Algorithms","created":"2025-11-26","cited_by_count":0,"area":null},{"id":"2607.01755","title":"Decentralized Stochastic Subgradient-type Methods with Communication Compression for Nonsmooth Nonconvex Optimization","created":"2026-07-02","cited_by_count":0,"area":null},{"id":"2608.05460","title":"A proximal subgradient method for nonconvex stochastic optimization under the Kurdyka-{\\L}ojasiewicz condition","created":"2026-08-05","cited_by_count":0,"area":null}],"next_cursor":null}