Stochastic Gradient Descent

Ask about this field

Recent momentum

emerging

0 papers in the last 28 days · 0.0% of indexed attention

Twelve weeks of publication activity for this topic as it is defined today.

Weekly history

Recent digests

What was published in this field, kept on the site without email delivery.

Period ending 2026-09-21

21 new papers

A weekly snapshot of new work published in Stochastic Gradient Descent.

Period ending 2026-09-14

17 new papers

A weekly snapshot of new work published in Stochastic Gradient Descent.

Period ending 2026-09-07

12 new papers

A weekly snapshot of new work published in Stochastic Gradient Descent.

Inside this field

Focused directions

541 papers

Latest in Stochastic Gradient Descent

Browse all topics →
CardsList

  1. Alternate loss functions and regression models that achieve robustness to outliers by modulating the learning rate

    Jun 20, 2026Mathew Mithra Noel, Arindam Banerjee, Yug D. Oswal +2OutliersLoss Function

  2. Gradient-Descent Steps to Success over Mean Accuracy: A Paradigm Shift for ML

    Jun 20, 2026Riccardo Poli, Ahmet YilmazHyperparameter OptimizationGradient Descent

  3. CoDMD: Copula-aware Distribution Matching Distillation for Fast Video Generation

    Jun 20, 2026Wenhu Zhang, Kun Cheng, Changyuan Wang +7Pre-Trained Video Diffusion ModelsLong Video Generation

  4. Finite-Sample Performance of Gradient Descent in Logistic Regression with Gaussian Design

    Jun 19, 2026Junren Chen, Arya MazumdarFinite-SampleGradient Descent

  5. Gradient-Based Learning of Parametric Engine Sound Representations for Real-Time Resynthesis and Tuning on Embedded Systems

    Jun 19, 2026Robin Doerfler, Matthieu Kuntz, Clemens ZimmerCombustion ControlHarmonic Tones

  6. Fisher-Geometric Sharpness and the Implicit Bias of SGD toward Flat Minima

    Jun 18, 2026Md Sakir Ahmed, Kumaresh Sarmah, Hemen DuttaSharpness-Aware MinimizationStochastic Gradient Descent

  7. Agentic Symbolic Search: Characterizing PDEs Beyond Hand-crafted Expressions, Meshes, and Neural Networks

    Jun 18, 2026Zongmin Yu, Liu YangPartial Differential Equation BenchmarksPartial Differential Equations

  8. On the Oracle Complexity of Interpolation-Based Gradient Descent

    Jun 18, 2026Dongmin Lee, William Lu, Anuran MakurGradient DescentEmpirical Risk Minimization

  9. Global Convergence of Gradient Descent for Score Matching in Gaussian Mixtures via Reverse Fisher Divergence

    Jun 18, 2026Alexander TyurinGaussian Mixture ModelsGradient Descent

  10. Weight Adaptation for Improving Parallel Performance of Adaptive Stochastic Natural Gradient

    Jun 18, 2026Yutaro Yamada, Kento Uchida, Shinichi ShirakawaEvolutionary SearchStochastic Gradient Descent

  11. Leverage Is Not Reach: A Control-Window Law for Single-Neuron Steering in Language Models

    Jun 18, 2026Hongliang LiuActivation SteeringModel Activations

  12. Federated Bilevel Performative Prediction

    Jun 18, 2026Liangxin Qian, Chang Liu, Xuanyu Cao +2Bilevel OptimizationDistributional Learning

  13. Compute Efficiency and Serial Runtime Tradeoffs for Stochastic Momentum Methods

    Jun 17, 2026Depen Morwani, Alexandru Meterez, Pranav Nair +1Stochastic Gradient DescentBatch Size

  14. Dual-Channel Grounded World Modeling (DCGWM): Structural Prevention of Objective Interference Collapse via Heterogeneous External Grounding with Inward-Only Gradient Flow

    Jun 17, 2026Akshay HazareJoint-Embedding Predictive ArchitecturesWasserstein Gradient Flows

  15. BrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection

    Jun 17, 2026Md Taimur Ahad, Bo Song, Yan LiBrain Tumor ClassificationMagnetic Resonance Imaging

  16. Correcting Sensor-Induced Distribution Drift with Wasserstein Adversarial Learning

    Jun 17, 2026Saraa Ali, Vladimir Bocharnikov, Fedor Ratnikov +3Distribution ShiftsWasserstein Gradient Flows

  17. Mixed-Precision Communication-Avoiding SGD for Generalized Linear Models on GPUs

    Jun 16, 2026Aditya Devarakonda, Irene Simó Muñoz, Giulia GuidiStochastic Gradient DescentMulti-Gpu Systems

  18. Edge Flow: A Tractable and Predictive Continuous-Time Model for Gradient Descent at the Edge of Stability

    Jun 16, 2026Pierre MarionGradient DescentSharpness-Aware Minimization

  19. Conservation Laws for Modern Neural Architectures

    Jun 16, 2026Viet-Hoang Tran, Vinh Khanh Bui, Tan Lai Ngoc +3Gradient FlowOver-Parameterized Neural Networks

  20. A Link between Shock-wave Theory and Symmetry-reduced Stochastic Gradient Descent for Artificial Neural Networks

    Jun 16, 2026Taiki MiyagawaTwo-Layer Neural NetworksStochastic Gradient Descent

  21. Agent Utilities over Generalized Voronoi Regions and their Gradients

    Jun 16, 2026Andre N. Costa, Petter Ögren, Carlos H. C. RibeiroVoronoi RegionsExpected Utility

  22. Functional Gradient Descent with Adaptive Representations

    Jun 15, 2026Daniel Csillag, Rodrigo Schuller, Pedro Dall'Antonia +3Gradient DescentFlat Minima

  23. Diffusion Flow Matching: Dimension-Improved KL Bounds and Wasserstein Guarantees

    Jun 15, 2026Marta Gentiloni Silveri, Giovanni Conforti, Alain DurmusWasserstein Gradient FlowsGenerative Models

  24. A Gradient Perspective on RLVR Stability and Winner Advantage Policy Optimization

    Jun 15, 2026Prasanth YSS, Zhichen Ren, Rasa Hosseinzadeh +6Reinforcement Learning With Verifiable RewardGradient Concentration

  25. HadBalance: A Plug-and-Play Unified Global Geometric Prior Framework for Generalizable Biomedical Segmentation

    Jun 14, 2026Zhuangzhi Gao, Feixiang Zhou, He Zhao +11Medical Image SegmentationStatistical Shape Model

  26. Retrievable Gradients: Continual Post-Training Without Cumulative Weight Drift

    Jun 14, 2026Weihang Su, Jiacheng Kang, Jingyan Xu +7Large Language Model Post-TrainingGradient Concentration

  27. A Bifurcation Theory Framework for Gradient Descent on the Edge of Stability

    Jun 14, 2026Eric GanGradient DescentBifurcation

  28. Automatic Differentiation from Scratch: How PyTorch Computes Gradients in Physics-Informed Neural Networks

    Jun 13, 2026Abdeladhim TahimiConventional Physics-Informed Neural NetworksAutomatic Differentiation

  29. Can Neural Networks Achieve Optimal Computational-statistical Tradeoff? An Analysis on Single-Index Model

    Jun 13, 2026Siyu Chen, Beining Wu, Miao Lu +2Optimal Sample ComplexityTwo-Layer Neural Networks

  30. Beyond Scalar Distances: Semantic Attribute Gradients from Frozen MLLMs for Visual Embeddings

    Jun 13, 2026Shubhang Bhatnagar, Dheeraj Baiju, Narendra AhujaVisual EmbeddingsVision Encoders

  31. Directing Open-Ended Evolution in Artificial Life via Multi-Scale Path Divergence

    Jun 12, 2026Mikhail Akhtyrchenko, Mikhail I. Katsnelson, Andrey UstyuzhaninEvolutionBiological

  32. FastMix: Fast Data Mixture Optimization via Gradient Descent

    Jun 12, 2026Haoru Tan, Sitong Wu, Yanfeng Chen +5Model PretrainingMixture Models

  33. Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning

    Jun 12, 2026Dmitriy Bystrov, Daniil Medyakov, Dmitry Bylinkin +1Parameter-Efficient Fine-TuningLarge Language Model Fine-Tuning

  34. Muonp^p: Muon with Fractional Spectral Powers

    Jun 11, 2026Yihe Dong, Will SawinMuon OptimizerMuon

  35. Clipping Makes Distributed and Federated Asynchronous SGD Robust to Stragglers

    Jun 11, 2026Samuel Erickson, Mikael JohanssonStochastic Gradient DescentGradient Clipping

  36. Reinforcement Learning Disrupts Gradient-Based Adversarial Optimization

    Jun 10, 2026Xinhai Zou, Chang Zhao, Alireza Aghabagherloo +3Adversarial TransferabilityAdversarial Training

  37. Simplicity Suffices for Parameter Noise Injection in Stochastic Gradient Descent

    Jun 10, 2026Benjamin Leblanc, Louis-Jacob Lebel, Teddy Kana +1Stochastic Gradient DescentGaussian Noise

  38. Secure Aggregation with Top-K Sparsification in Decentralized Federated Learning

    Jun 9, 2026Hengxuan Tang, Jinbao Zhu, Xiaohu TangFederated LearningSparsity

  39. Improving Adversarial Transferability on Vision-Language Pre-training Models via Surrogate-Specific Bias Correction

    Jun 9, 2026Lijia Yu, Jiuxin Cao, Yuchen Qiang +3Vision-Language Model AdaptationAdversarial Transferability

  40. FOGO: Forgetting-aware Orthogonalization Optimizer

    Jun 9, 2026Toan Nguyen, Yang Liu, Trung Le +2ForgettingContinual Learning Methods

  41. Learning Dynamics Reveal a Hierarchy of Weight-Induced Layerwise Gram Metrics

    Jun 8, 2026Claudio NordioGradient FlowLearning Dynamics

  42. Adaptive directional gradients for parameterised quantum circuits

    Jun 8, 2026Brian Coyle, Snehal Raj, Virag Umathe +2Quantum Approximate Optimization AlgorithmGradient Concentration

  43. Improved Convergence Analysis of Topology Dependence in Decentralized SGD

    Jun 8, 2026Yuki Takezawa, Anastasia Koloskova, Sebastian U. StichConvergence AnalysisStochastic Gradient Descent

  44. Generalization in Nonlinear Least Squares via Learned Feature Geometry

    Jun 7, 2026Ayub Kharel, Ilja Kuzborskij, Patrick Rebeschini +1Generalization BoundsLeast Squares

  45. Compile Once, Differentiate Everywhere: A Differentiable Meta-Circular Interpreter

    Jun 7, 2026Lucas ShenemanGradient-Based OptimizationMock-Interface Synthesis

  46. Fourier fractal dimension to predict the generalization of deep neural networks

    Jun 6, 2026Joao B. Florindo, Davi Wanderley MisturiniStochastic Gradient DescentFourier

  47. Minibatch Selection for Language Models via Partition Matroid Constrained Gradient Matching

    Jun 6, 2026Prayas Agrawal, Prateek Chanda, Ishita Khatri +3Gradient Concentration

  48. Accelerated Decentralized Stochastic Gradient Descent for Strongly Convex Optimization

    Jun 5, 2026Ming Sun, Kun YuanStochastic Smooth Convex OptimizationStochastic Gradient Descent

  49. Second-Order Path Kernel Interpolation Formulas in Machine Learning

    Jun 5, 2026Jin Guo, Roy Y. He, Jean-Michel MorelStochastic Gradient DescentInterpolation

  50. Beyond Linear and Overcomplete Regimes: A Mean-Field Analysis of Bottleneck Autoencoders

    Jun 5, 2026Santanu Das, Ramyak Bilas, Pascal Esser +1Masked AutoencodersTwo-Layer Neural Networks

  51. Residual-Controlled Multiplier Learning for Stochastic Constrained Decision-Making

    Jun 5, 2026Kang Liu, Jianchen Hu, Ziyu Qu +3Feedback LinearizationMini-Batch Stochastic Gradient Descent

  52. Uniform Stability and Generalization Error of GD and SGD on Fixed-Point Parameters

    Jun 5, 2026Jonghyun Shin, Sejun ParkStochastic Gradient DescentGeneralization Bounds

  53. Generalization in Deep Neural Networks: Minimax Rates for Gradient Methods

    Jun 4, 2026Junyu Zhou, Puyu Wang, Yunwen Lei +2Over-Parameterized Neural NetworksNeural Tangent Kernel

  54. Optimal Rates for Generalization of Gradient Descent Methods with Deep Neural Networks

    Jun 4, 2026Junyu Zhou, Puyu Wang, Yunwen Lei +2Gradient DescentStochastic Gradient Descent

  55. Flatland: The Adventures of Gradient Descent with Large Step Sizes

    Jun 4, 2026Leonardo Galli, Curtis Fox, Wiebke Bartolomaeus +2Gradient DescentLarge Step Sizes

  56. How abundant are good interpolators?

    Jun 4, 2026August Y. Chen, Ahmed El AlaouiOver-Parameterized Neural NetworksEmpirical Risk Minimization

  57. Diffusion Models Observe Only Gradients: A Geometric Perspective on Score Matching Errors

    Jun 4, 2026Naïl B. Khelifa, Richard E. Turner, Ramji VenkataramananScore-Based Diffusion ModelKullback-Leibler Divergence

  58. Quantifying Uncertainty In Wide Two-Layer Neural Networks: On The Law Of The Limiting Fluctuation Process

    Jun 4, 2026Arnaud Descours, Arnaud Guillin, Geoffrey Lacour +3Two-Layer Neural NetworksCentral Limit Theorem

  59. DP-MacAdam: Differentially Private Mechanism with Adaptive Clipping and Adaptive Momentum

    Jun 3, 2026Naima Tasnim, Lalitha Sankar, Oliver KosutDifferential PrivacyGradient Clipping

  60. A prism hierarchy of learning regimes in large linear autoencoders

    Jun 3, 2026Eugene Golikov, Yaroslav Gusev, Dmitry YarotskyLow-Data RegimesMasked Autoencoders