Regularization

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Period ending 2026-09-21

16 new papers

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Period ending 2026-09-14

18 new papers

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Period ending 2026-09-07

11 new papers

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636 papers

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  1. From Zipf's Law to Neural Scaling through Heaps' Law and Hilberg's Hypothesis

    Dec 15, 2025Łukasz DębowskiPower-Law DistributionKullback-Leibler Divergence

  2. HiMoE-VLA: Hierarchical Mixture-of-Experts for Generalist Vision-Language-Action Policies

    Dec 5, 2025Zhiying Du, Bei Liu, Yaobo Liang +7Mixture-of-Experts ModelsIterative Co-Training

  3. Efficient Public Verification of Private ML via Regularization

    Dec 3, 2025Zoë Ruha Bell, Anvith Thudi, Olive Franzese-McLaughlin +2Differential PrivacyPrivacy-Utility Trade-Offs

  4. FeRA: Frequency-Energy Constrained Routing for Effective Diffusion Adaptation Fine-Tuning

    Nov 22, 2025Bo Yin, Xiaobin Hu, Xingyu Zhou +7Generative ModelsRegularization Techniques

  5. Pruning as Regularization: Sensitivity-Aware One-Shot Pruning in ASR

    Nov 11, 2025Julian Irigoyen, Arthur Söhler, Andreas Søeborg KirkedalPruningFeed-Forward Network

  6. Solver-Integrated Adversarial Attacking and Training of Neural Operators

    Oct 21, 2025Yifei SunNeural OperatorsAdversarial Transferability

  7. SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization

    Oct 6, 2025Théophane Vallaeys, Jakob Verbeek, Matthieu CordVariational AutoencoderRegularization Techniques

  8. Breaking the Weak Recovery Limit in Random Phase Retrieval with Learned Regularizers

    Sep 18, 2025Stanislas Ducotterd, Zhiyuan Hu, Michael Unser +1Sparse RecoveryIll-Posed Inverse Problem

  9. Understanding and Improving Shampoo and SOAP via Kullback-Leibler Minimization

    Sep 3, 2025Wu Lin, Scott C. Lowe, Felix Dangel +3Neural Network OptimizationKullback-Leibler Divergence

  10. FAIRVAR: Fair Federated Learning via Variance Regularization

    Aug 16, 2025Zahra Kharaghani, Ali Dadras, Tommy LöfstedtFairness-Aware LearningFederated Learning

  11. Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization

    Aug 3, 2025Xin Ding, Yun Chen, Yongwei Wang +4Dual-Domain Equivariant Generative Adversarial NetworkGenerative Adversarial Network

  12. GeLaCo: An Evolutionary Approach to Layer Compression

    Jul 14, 2025David Ponce, Thierry Etchegoyhen, Javier Del SerLarge Language Model CompressionContext Compression

  13. Stabilizing Black-Box Prompt Optimization with Textual Regularization and Signal Aggregation

    Jul 14, 2025MohammadReza Davari, Utkarsh Garg, Weixin Cai +1Prior Prompt Optimization TechniquesAutomatic Prompt Optimization

  14. Spherical Cauchy Variational Autoencoders: Heavy Angular Tails and Exact KL Evaluation

    Jun 26, 2025Lukas Sablica, Kurt HornikPosteriorKullback-Leibler Divergence

  15. Discrete Compositional Generation via General Soft Operators and Robust Reinforcement Learning

    Jun 20, 2025Marco Jiralerspong, Esther Derman, Danilo Vucetic +5Molecular GenerationKullback-Leibler Regularization

  16. The Effect of Stochasticity in Score-Based Diffusion Sampling: a KL Divergence Analysis

    Jun 13, 2025Bernardo P. Schaeffer, Ricardo M. S. Rosa, Glauco ValleScore-Based Diffusion ModelStochastic Differential Equations

  17. A Jump-Diffusion Framework for Irregular Time Series Generation

    May 29, 2025O. Pfohl, J. Chemseddine, P. Hagemann +3Synthetic Time SeriesStochastic Systems

  18. EquiReg: Equivariance Regularized Diffusion for Inverse Problems

    May 29, 2025Bahareh Tolooshams, Aditi Chandrashekar, Rayhan Zirvi +4Ill-Posed Inverse ProblemInverse Problem

  19. When Can Large Reasoning Models Save Thinking? Mechanistic Analysis of Behavioral Divergence in Reasoning

    May 21, 2025Rongzhi Zhu, Yi Liu, Jiancheng Wang +6Large Reasoning ModelsDivergence

  20. Optimal and Provable Calibration in High-Dimensional Binary Classification: Angular Calibration and Platt Scaling

    Feb 21, 2025Yufan Li, Pragya SurMulticalibration and CalibrationBinary Classification

  21. Generalization Bounds for Markov Algorithms through Entropy Flow Computations

    Feb 11, 2025Benjamin Dupuis, Maxime Haddouche, George Deligiannidis +1Generalization BoundsEntropy-Regularized Equilibrium Selection

  22. Machine Unlearning via Information Theoretic Regularization

    Feb 8, 2025Shizhou Xu, Thomas StrohmerSeveral Approximate Unlearning StrategiesMachine Unlearning

  23. On the Relationship Between CoCoA and ADMM for Distributed Empirical Risk Minimization

    Feb 1, 2025Runxiong Wu, Andi WangEmpirical Risk MinimizationPrimal-Dual Methods

  24. Fixed-Gaussian Spectral Algorithms: Minimax Optimal Rates for Misspecified Learning and Transfer

    Jan 18, 2025Haotian Lin, Matthew ReimherrKernel Ridge RegressionTransfer Learning

  25. Inclusive KL Gradient Flows: Otto-Wasserstein, Fisher-Rao-Gaussian, and Local-Estimator Dynamics

    Oct 31, 2024Jia-Jie ZhuWasserstein Gradient FlowsVariational Inference

  26. Robustness and Structure Preservation in Flow-Based Generative Models via Wasserstein Path-Space Divergences

    Oct 2, 2024Ziyu Chen, Markos A. Katsoulakis, Benjamin J. ZhangWasserstein DistanceGenerative Flow Networks

  27. On Regularization via Early Stopping for Least Squares Regression

    Jun 6, 2024Rishi Sonthalia, Jackie Lok, Elizaveta RebrovaKernel Ridge RegressionEarly Stopping

  28. Branch Scaling Manifests as Implicit Architectural Regularization for Improving Generalization in Overparameterized ResNets

    Mar 7, 2024Zixiong Yu, Guhan Chen, Jianfa Lai +2Over-Parameterized Neural NetworksResidual Connections

  29. SAR image segmentation algorithms based on I-divergence-TV model

    Dec 9, 2023Guangming LiuSynthetic Aperture RadarBregman Divergences

  30. Time-Varying Graph Learning with Constraints on Graph Temporal Variation

    Jan 10, 2020Haruki Yokota, Koki Yamada, Yuichi Tanaka +1SpatiotemporalTemporal Dynamics

  31. Multi-Source Wasserstein Distributionally Robust Graph Learning

    Date pendingChuansen Peng, Yifan Xia, Jinshan Zhong +1Graph Representation LearningWasserstein Distance

  32. REAL-Q: E2E LLM Quantization via Dynamic Gradient Descent

    Date pendingQian Zhang, Yaoming Li, Zhewen Tan +9Large Language Model QuantizationWeight-Only Quantization

  33. Accuracy is Not Enough: A Divergence-Based Approach to Evaluate Fidelity Loss in Quantized LLMs

    Date pendingShahzeb Qamar, Lorenz Sparrenberg, Christian Bauckhage +5Large Language Model QuantizationWeight-Only Quantization

  34. Confidence-Calibrating Regularization for Robust Brain MRI Segmentation Under Domain Shift

    Date pendingBehraj Khan, Tahir Qasim Syed, Syed Ahmad Chan BukhariMagnetic Resonance ImagingSegment Anything Model

  35. Deep learning methods for inverse problems using connections between proximal operators and Hamilton-Jacobi equations

    Date pendingOluwatosin Akande, Gabriel P. Langlois, Akwum OnwuntaIll-Posed Inverse ProblemInverse Problem

  36. Generalization Guarantees on Data-Driven Tuning of Gradient Descent with Langevin Updates

    Date pendingSaumya Goyal, Rohith Rongali, Ritabrata Ray +1Gradient DescentHyperparameter Optimization