Stochastic Gradient Descent

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Period ending 2026-09-14

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  1. Riemannian--Lorentz Fusion of Vision Transformers and State-Space Models

    Sep 16, 2026Badri N. Patro, Vijay S. AgneeswaranSelf-Supervised Vision TransformersVision Transformer

  2. Double descent is the principle of least action

    Sep 16, 2026Congzhou M ShaDescentStochastic Gradient Descent

  3. Fast Learning Rates for Physics-Informed Kernel Methods

    Sep 16, 2026Luc Brogat-Motte, Joachim Bona-Pellissier, Giacomo Meanti +1Physics-Informed Machine LearningKernel Method

  4. Gradient Descent with Stochastic Subspaces via Persistence of Memory

    Sep 16, 2026Subhroshekhar Ghosh, Clement Z. Q. Ng, Pierre-Louis Poirion +1Stochastic Gradient DescentGradient Descent

  5. A GAN-Based Framework for Robust DDoS Attack Detection

    Sep 16, 2026Makram Chehayeb, Walid Fahs, Amina Rizk +2Denial-Of-Service AttacksWasserstein Gradient Flows

  6. Preservation of Log-Concavity and Convergence of Wasserstein-Fisher-Rao Gradient Flows

    Sep 16, 2026Francesca Romana Crucinio, Sahani PathirajaWasserstein Gradient FlowsNon-Log-Concave

  7. Matching Multi-Loop Complexities with a Single Loop: Optimal Optimization Stationarity and Best-Known Game Stationarity in Nonconvex--Concave Minimax Optimization

    Sep 16, 2026Minghao Zhang, Zi XuConvex OptimizationStationarity

  8. Derivative-Free Structured Updates for Muon

    Sep 15, 2026Pengcheng XieMuon OptimizerGradient-Based Optimization

  9. Optimization over covariance matrices with a parameterized metric

    Sep 15, 2026Yibang Li, Bamdev Mishra, Pratik Jawanpuria +1Covariance EstimationCondition Number

  10. Geometry of learning dynamics: Gradient descent versus natural gradient on the ridge of optimization

    Sep 15, 2026Akira TamamoriGradient DescentInformation-Geometric Optimization

  11. Online Gradient Computation for Warping Gaussian Process Transformations

    Sep 15, 2026Emilio Ruiz-Moreno, Konstantinos Slavakis, Baltasar Beferull-LozanoGaussian ProcessWasserstein Gradient Flows

  12. Federated stochastic bilevel optimization with fully first-order gradients

    Sep 14, 2026Yihan Zhang, Rohit Dhaipule, Chiu C Tan +2Bilevel OptimizationProximal Gradient Descent

  13. Compute-Optimal Pretrain--Fine-tune in Ridge Gradient Descent

    Sep 14, 2026Alex Buna, Fanghui Liu, Patrick RebeschiniSpectral PreconditioningModel Pretraining

  14. How I learned to stop worrying and love StopGrads: Stationarity, Convergence, and a case study on Flow Map Learning

    Sep 14, 2026Max W. Shen, Mark Goldstein, Zichu Wang +2Wasserstein Gradient FlowsFlow Map

  15. Solving Finite-sum Coupled Compositional Optimization via Multi-block-Single-probe Estimator

    Sep 14, 2026Wei Jiang, Sifan Yang, Yibo Wang +2Variance ReductionStochastic Optimization

  16. Benign Loss Landscapes Can Coexist with Worst-Case Hardness

    Sep 14, 2026Zach Furman, Stephan Wäldchen, Yangda Bei +1Loss LandscapeTensor Networks

  17. Quantile-based Loss Filtering for Outlier-Robust Stochastic Gradient Descent

    Sep 14, 2026Jamie Haddock, Anna Ma, Elizaveta RebrovaStochastic Gradient DescentQuantile Regression

  18. Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise and H\"{o}lder Smoothness

    Sep 14, 2026Misbah Uz Zaman, Anirbit MukherjeeStochastic Gradient DescentLipschitz Constant

  19. High-Probability Convergence of SGD via Batched Updates

    Sep 14, 2026Feng Zhu, Robert W. Heath Jr., Aritra MitraStochastic Gradient DescentFederated Learning

  20. Tight Sampling Complexity with stochastic gradient oracles in Fixed Dimensions

    Sep 14, 2026Weiming Ou, Xiao WangNon-Log-ConcaveOptimal Sample Complexity

  21. LiftGCN: Efficient Energy-Preserving Graph Learning via Joukowski Spectral Lifting for Finite Element Stress Prediction

    Sep 14, 2026Chen Zeng, Qiao WangFinite Element MethodGraph Representation Learning

  22. Stochastic Gradient Descent over P2

    Sep 11, 2026Maria Oprea, Qin Li, Yunan YangStochastic Gradient DescentStochastic Differential Equations

  23. Almost Sure Convergence Analysis of Stochastic Gradient Methods with Clipping and Additive Noise

    Sep 10, 2026Amartya Mukherjee, Jun LiuStochastic Gradient DescentGradient Clipping

  24. AdamX: Cosine similarity meets gradient descent

    Sep 10, 2026Francisco Caldas, Ruben Belo, Cláudia SoaresAdamM+Adam

  25. Generalization Analysis of Distributed Kernel-based Robust Gradient Descent Algorithms

    Sep 10, 2026Jun-Yi Meng, Zheng-Chu Guo, Yuan MaoGeneralization BoundsKernel Hilbert Spaces

  26. Learning with Synthetic Data via SGD in High-Dimensional Linear Regression

    Sep 10, 2026Jichu li, Difan ZouSynthetic DataStochastic Gradient Descent

  27. Cascading Gradient Inversion via LT-Code Inspired Peeling in Federated Learning

    Sep 10, 2026Saeed Shariati, Mohsen Alambardar MeybodiFederated LearningStochastic Gradient Descent

  28. Settling: Equilibrium Inference for Non-Convex Validity Sets

    Sep 9, 2026Lyes Saad SaoudConformal InferenceEquilibrium

  29. Exact-Form Regret for Gradient Descent, Mirror Descent and Follow-the-Regularized-Leader

    Sep 8, 2026Ashkan Soleymani, Gabriele Farina, Patrick JailletMirror DescentLinear Regret

  30. Why shared attention vectors fail: a case for outcome-indexed tuning

    Sep 8, 2026Lenard DomeSoftmax AttentionGradient Descent

  31. Not All Variables Agree: Reliability-Aware Variable-Wise Gradient Surgery for Multivariate Time-Series Forecasting

    Sep 8, 2026Jinwoo Park, Hyeongwon Kang, Pilsung KangMultivariate Time Series ForecastingMultivariate Forecasting

  32. The Exact Time-Uniform Rate Frontier for Stochastic Gradient Descent on Smooth Convex Objectives

    Sep 8, 2026Ruijie Li, Kang Chen, Tianyu WangStochastic Gradient DescentStochastic

  33. Speed Limit for Information Acquisition in Stochastic Learning Dynamics

    Sep 8, 2026Shuta Kobayashi, Andreas DechantStochastic Gradient DescentInformation-Theoretic Limits

  34. Sparse Data Augmentation for Optimization with Provable Guarantees

    Sep 8, 2026Behrooz Tahmasebi, Melanie WeberConvex OptimizationAugmentation

  35. SGD in Multiclass Logistic Regression: Sequential Learning and Scaling Laws

    Sep 7, 2026Konstantinos Christopher Tsiolis, Denny Wu, Christos Thrampoulidis +1Multiclass ClassificationScaling Laws

  36. A Theoretical Analysis of Generalization Dynamics in Neural Networks under Gradient Descent with Weight Decay

    Sep 7, 2026Yuqing Wang, Ioannis G. Kevrekidis, Mikhail BelkinGeneralization BoundsTwo-Layer Neural Networks

  37. Robust Decentralized Federated Distillation via Multi-Modality Knowledge Collaboration

    Sep 7, 2026Xiao Ma, Hong Shen, Hui Tian +2Byzantine AttacksGradient Concentration

  38. High-Dimensional Learning Dynamics of Attention-Indexed Models

    Sep 3, 2026Yizhou Xu, Margarita Sagitova, Lenka Zdeborová +1Attention-Based ModelsStochastic Gradient Descent

  39. TruncGradGS: Improved 3D Gaussian Splatting via Truncated Gradient Updates

    Sep 3, 2026Theo Morales, Nhat-Quynh Le-Pham, Robin Atkins +13D Gaussian Splats3D Gaussian

  40. Learning Informative Prior with Infinite-Dimensional Continuous Normalizing Flow for Bayesian Inverse Problem

    Sep 3, 2026Yang Zhao, Junxiong Jia, Tao ZhouIll-Posed Inverse ProblemNormalizing Flows

  41. Convergence Theory of Knowledge Distillation in Asynchronous P2P Gossip Learning Network

    Sep 1, 2026Lucas Qingyang Fang, Tiyao Liu, Jinhao Jing +4Knowledge DistillationStochastic Gradient Descent

  42. CAT-Flow: Curvature-Adaptive sTeps for Flow Matching

    Sep 1, 2026Qinchan Li, Pedro Cisneros-Velarde, Keru Fu +3Hierarchical Flow MatchingGenerative Models

  43. On the Reliability of Generative Augmentation: A Wasserstein-Based Theoretical and Empirical Study

    Sep 1, 2026Chathurika S Abeykoon, Mathias Nthiani Muia, Mallory GoldsteinSynthetic Data AugmentationAugmentation

  44. The Multiple Timescales of Gradient Descent on the Edge of Stability: A Perturbative Derivation of the Central Flow

    Sep 1, 2026Raphaël BerthierGradient FlowGradient Descent

  45. Subspace Levenberg Marquardt Algorithms in Training Neural Networks

    Sep 1, 2026M. Duc HoangNeural NetworkStochastic Gradient Descent

  46. Hard-ReLU Gradient Descent Selects an Event-Free Sensitivity Limit

    Aug 31, 2026Xiaoyang Li, Runni ZhouGradient FlowCurvature

  47. Reciprocity Separates Gradient Flow from Rotation in Conservative Physical Learning

    Aug 31, 2026Ruiwu Niu, Xiaowen Bi, Michaël Antonie van WykGradient FlowWasserstein Gradient Flows

  48. Generalization as a robust performance property of learning-enabled dynamical systems

    Aug 31, 2026Filippo FabianiLearning DynamicsGeneralization Bounds

  49. Convergence rates for the RMSprop optimizer with full control of the hyperparameters

    Aug 31, 2026Steffen Dereich, Arnulf JentzenAdamStochastic Smooth Convex Optimization

  50. Estimating Population-Risk Curves Along Nonconvex Gradient Flows from the Training Sample

    Aug 31, 2026Mingzhi SongFinite-SampleWasserstein Gradient Flows

  51. Aligning Multi-Trajectory Supervision with Policy Optimization for VLA Driving

    Aug 31, 2026Tian Zhang, Zhuo Huang, Hongrui Ye +3Flow-Matching Vision-Language-ActionGroup Relative Policy Optimization

  52. Reward-guided Fine-Tuning of One-Step Generative Models via Wasserstein Gradient Flow

    Aug 30, 2026Hoseong Hwang, Woorim Han, Joungin Chun +2Wasserstein Gradient FlowsReward Gradients

  53. Kähler landscapes for complex neural network descents and guarantees including a search and destroy of the Calabi-Yau manifold

    Aug 20, 2026Andrew GracykLoss LandscapeManifolds

  54. Algorithmic Information Dynamics of Learning: A Certified, Differentiable Complexity Controller for Grokking

    Aug 14, 2026Luan Ozelim, Abicumaran Uthamacumaran, Hector ZenilLearning DynamicsGrokking

  55. Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws

    Aug 13, 2026Liu Ziyin, Yizhou Xu, Tomaso Poggio +1Two-Layer Neural NetworksPower-Law Distribution

  56. TANGCO: Learning Topology-Aware Capacity Allocation for Overload-driven Cascading Failures

    Aug 13, 2026Orkun Irsoy, Leman Akoglu, Osman YaganNetworked SystemsCapacity

  57. Fine-Tuning Generative Models for Extreme Events via CVaR-Penalized Wasserstein Gradient Flows

    Aug 12, 2026Thejani Gamage, Hyemin Gu, Zhizhen Zhang +3Long-Tailed DistributionWasserstein Gradient Flows

  58. Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness

    Aug 11, 2026Siqiao Mu, Diego KlabjanLipschitz ConstantGradient Descent

  59. Learning Gaussian Structure: Intervention-Guided Density Control for Feed-Forward Driving Reconstruction

    Aug 11, 2026Hang Li, Jiahe Li, Meiying Gu +3Lidar3D Gaussian Splatting

  60. Efficient Hypergradient Descent for Inverse Reinforcement Learning

    Aug 11, 2026Nikita Sevriukov, Anna Barabanova, Uliana Gagarina +4Entropy Regularized Reinforcement LearningGradient Descent