Distributional Learning

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11 papers in the last 28 days · 0.3% of indexed attention

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Period ending 2026-09-14

6 new papers

A weekly snapshot of new work published in Distributional Learning.

Period ending 2026-09-07

3 new papers

A weekly snapshot of new work published in Distributional Learning.

306 papers

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  1. Personalized Federated Learning through Global Knowledge Distillation and Local Head Adaptation

    Sep 15, 2026Polycarpo Souza Neto, José Mairton Barros da Silva Júnior, Charles Casimiro CavalcanteHeterogeneous Knowledge DistillationDistributional Learning

  2. Certified Uncertainty Propagation in One-Shot Federated Bayesian Models via Posterior Event Transport

    Sep 14, 2026Mahyar Mohammadi, Mohammad Hossein Badiei, Abolfazl Yaghmaei +1Bayesian Neural NetworksDistributional Learning

  3. Privacy-Aligned Personalized Federated Learning with Compact Adaptation and Variable-Length Gaussian Communication

    Sep 14, 2026Yilin Xu, Chun Hei Michael Shiu, Chih Wei Ling +1Differential PrivacyDp-Sgd

  4. Privacy-enhanced federated learning via asynchronous aggregation and local differential perturbation

    Sep 14, 2026Zhen Zhong, Shini Yang, Liesheng WeiDistributional Learning

  5. SWB-DM: A Calibrated Sliced-Wasserstein-Barycenter Aggregator with Delayed-Momentum Caching for Byzantine-Robust Federated Learning under Partial Participation

    Sep 14, 2026Saranraj S, Saranya M S, Alex David S +1Quantum Federated LearningDistributional Learning

  6. End-to-End Verifiable and Robust Federated Learning

    Sep 14, 2026Doryan Lesaignoux, Enrique Mármol Campos, Gabriele Spini +2Distributional LearningOutliers

  7. Relatively Smart II: Tractable or Semi-Supervised Instance-Optimal Learning

    Sep 11, 2026Shaddin Dughmi, Alireza F. PourSemi-Supervised LearningDistributional Learning

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

    Sep 10, 2026Jun-Yi Meng, Zheng-Chu Guo, Yuan MaoDistributional LearningGeneralization Bounds

  9. HybridFLow: SDN-Orchestrated Client Partitioning for Hybrid Federated Learning

    Sep 9, 2026Osama Abu Hamdan, Rabin Pandey, Hao Che +2FedavgPartitioning

  10. FedGenSC: Federated Generative Semantic Communication with Channel-Aware Adaptation

    Sep 8, 2026Rita Abou Fares, Razan Al Kakoun, Maher Nouiehed +1Distributional LearningDistortion

  11. Robust Decentralized Personalized Federated Learning via Prediction-Constrained Neighborhood Collaboration

    Sep 7, 2026Xiao Ma, Hong Shen, Hui Tian +2Robust Federated LearningByzantine-Robust Aggregation

  12. FedRAW: Preserving Rare-Label Influence in Asynchronous Federated Learning

    Sep 7, 2026Prashant Bajpai, Divya Saxena, Philippe Lalanda +1Federated Learning FrameworkDistributional Learning

  13. Pushing the (Decision) Boundaries: Dynamically Calibrating Differentially Private Noise to Explainability in Federated Learning

    Sep 3, 2026Michael Khavkin, Kichang Lee, Jaeho Jin +2Differential PrivacyExplainability

  14. Differentially private federated learning with Byzantine-robust aggregation: A cross-domain framework for secure model training in banking and healthcare systems

    Sep 2, 2026Srikumar NayakByzantine-Robust AggregationDifferential Privacy

  15. Optimizing Byzantine Node Placement in Decentralized Federated Learning

    Sep 1, 2026Edoardo Gabrielli, Gabriele TolomeiByzantine-Robust AggregationDistributional Learning

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

    Sep 1, 2026Chathurika S Abeykoon, Mathias Nthiani Muia, Mallory GoldsteinData AugmentationExtreme Class Imbalance

  17. SatDL: Jointly Optimizing Data Redistribution and Training for Satellite-Based Distributed Learning

    Aug 25, 2026Hao Wu, Kin Whye Chew, Yizhan Han +2Distributional LearningWireless Resource Allocation

  18. Wasserstein Filtering: A Sample Selection Method for Robust Distribution Learning

    Aug 13, 2026Yikai Xu, Zhao Chen, Jian HuangOutliersWasserstein Distance

  19. Online Inference for Quantile Temporal Difference Learning in Distributional Reinforcement Learning

    Aug 13, 2026Zijie Cheng, Yang Peng, Zhihua ZhangDistributional Reinforcement LearningTemporal Difference

  20. Federated Compositional Muon Optimizer for Matrix-Wise Models

    Aug 13, 2026Wang Yan, Feihu HuangMuonDistributional Learning

  21. Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning

    Aug 12, 2026Mirko Konstantin, Stefan Zachow, Anirban MukhopadhyayRobust Federated LearningDistributional Learning

  22. Epiplexity Guided Data Selection and Generation for Out-of-Distribution Generalization

    Aug 12, 2026Ellen Su, Andres Potapczynski, Shikai Qiu +2Efficient Data SelectionDistributional Learning

  23. BPG: Balancing Plasticity and Generalization for Domain Incremental Learning

    Aug 11, 2026Qiang Wang, Songlin Dong, Shaokun Wang +5Clip-Based Class-Incremental LearningParameter-Efficient Adaptation

  24. A Joint-Distribution Route to Fair Representations with Continuous Sensitive Attributes

    Aug 11, 2026Yijin Ni, Xiaoming HuoFairness-Aware LearningDisparities

  25. In-Context Density Estimation for Tabular Data

    Aug 10, 2026Patryk Marszałek, Jacek Tabor, Marek ŚmiejaRandomized Global-Local Density EstimationUnsupervised Anomaly Detection

  26. FedA2L: Adaptive layer-wise learning rate adjustment in decentralized federated learning

    Aug 10, 2026Van Truong Vo, Khoa Nguyen, Taehong KimFedavgLearning Rates

  27. Personalized Federated Learning via Variance-Aware Nonparametric Empirical Bayes

    Aug 10, 2026Jae Ho Chang, Arnab Auddy, Subhadeep PaulDistributional LearningEmpirical Risk Minimization

  28. A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning

    Aug 9, 2026Jianhan Zhang, Jitao Wang, John D. Piette +3Distributional Reinforcement LearningReweighting

  29. Domain-Aware Pruning: Sparsity and Domain Generalization via Regularized Probabilistic Masking

    Aug 9, 2026Parham Sazdar, Mostafa Tavassolipour, Reshad HosseiniMultimodal Domain GeneralizationMagnitude-Based Pruning

  30. Out-of-Distribution Federated Distillation with Domain-Aware Proxy

    Aug 9, 2026Jiahao Xiao, Jiangming LiuOut-Of-DistributionDistributional Learning

  31. Sheaf-Based Federated Representation Learning

    Aug 8, 2026Gabriele D'Acunto, Enrico Grimaldi, Valeria Avino +4Federated Learning FrameworkRepresentation Learning

  32. Uncertainty-Aware Ensemble Deep Randomized Neural Networks for Classification

    Aug 7, 2026M. Sajid, A. Quadir, A. Rahaman +2EnsembleDistributional Learning

  33. Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning

    Aug 7, 2026Alireza Moayedikia, Alicia Troncoso LoraDistributional LearningEmpirical Risk Minimization

  34. Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization

    Aug 6, 2026Grigory MalinovskyDistributed OptimizationDistributional Learning

  35. DG-FedReuse: Proxy-Gradient-Gated Cached-Update Reuse with Matched Sparse Uplink Accounting

    Aug 5, 2026Rahil Aftab, Vineet Kumar Rakesh, Soumya Mazumdar +1FedavgFederated Averaging

  36. SSTQ:Privacy-Preserving Vector Quantization via Subsampled Stochastic TurboQuant

    Aug 5, 2026Adel Javanmard, David P. Woodruff, Vahab MirrokniCalibration-Free Low-Bit Vector QuantizationDifferential Privacy

  37. The Sample Complexity of Distributionally Robust PAC Learning under Cressie--Read Divergences

    Aug 5, 2026Elad Aigner-Horev, Daniel Rosenberg, Roi WeissOptimal Sample ComplexityDistributional Learning

  38. Operationally Feasible Synthetic Power-Grid Scenarios via Learning the AC-Operable Joint Distribution

    Aug 4, 2026Chenhan Xiao, Xinyu He, Haoran Li +2Ac Optimal Power FlowDistributional Learning

  39. Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning

    Aug 3, 2026Yuan-Heng Tsai, Li-Hsing Yen, Yan-Wei ChenDistributional LearningSelection Bias

  40. CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning

    Aug 3, 2026Zijian Jiang, Chaoli Sun, Handing Wang +1One-Shot Federated LearningRobust Federated Learning

  41. Statistical Mechanics of Learning on Product Wasserstein Manifolds

    Aug 2, 2026Srinivasa Rao P Vangmayi P ReddyQuantum LearningWasserstein Distance

  42. Beyond Token-Level Cross-Entropy: Fréchet Distributional Post-Training for Autoregressive Image Generation

    Aug 1, 2026Jinhua Zhang, Yisong Lin, Wei Long +1Autoregressive Image GenerationFréchet Inception Distance

  43. Differentially Private Nonparametric Modal Learning with Applications to Regression and Clustering

    Jul 31, 2026Arkajyoti Bhattacharjee, Arnab AuddyDifferential PrivacyRandomized Global-Local Density Estimation

  44. Domain-Division based Progressive Learning for Source-Free Domain Adaptation

    Jul 31, 2026Pan Liu, Jing Li, Meng Zhao +3Unsupervised Domain AdaptationDomain Adaptation

  45. FairDiffuseVQVAE: Sampling-Time Fairness in Tabular Diffusion via Conditional Refinement of Vector-Quantized Latents

    Jul 31, 2026Nitish Nagesh, Mahdi Bagheri, Amir M. RahmaniFairness-Aware LearningFairness

  46. Generalization Bounds on Optimal Control for Transformer Training and Wasserstein Distributional Robustness

    Jul 30, 2026Kağan Akman, Naci Saldi, Serdar YükselGeneralization BoundsWasserstein Distance

  47. Harnessing the Potential of Optimizing Data Mixtures via Bayesian Domain Reweighting

    Jul 30, 2026Xiang Yuan, Kaiqing Lei, Zhenyu Jin +3ReweightingDistributional Learning

  48. First-order Constrained Trilevel Optimization Over Distributed Networks for Robust Coreset Selection

    Jul 30, 2026Yang Jiao, Kaixuan Jiao, Kai Yang +4Geometry-Aware Uncertainty CoresetsConstrained Optimization

  49. FedTopo: Relation-Level Topology Sharing for Model-Heterogeneous Federated Learning

    Jul 29, 2026Zhaoyang Ma, Zhihao Wu, Xin Gao +3Federated Learning FrameworkTopological Learning

  50. Chaos Is a LADDER: Domain Generalization Beyond Invariance via Reweighting

    Jul 29, 2026Yuhang Jiang, Fengchuan Zhang, Sanguo Zhang +1Multimodal Domain GeneralizationReweighting

  51. Generative Distributionally Robust Optimization

    Jul 27, 2026Ziwei Zhang, Jonathan Yu-Meng Li, Zhihao JinDistributional LearningGenerative Models

  52. Learning Distributions from Multiple Data Providers

    Jul 27, 2026Jon Kleinberg, Amin Saberi, Xizhi Tan +1Distributional LearningSample Complexity

  53. Distributed Convolutional Rank Regression over Decentralized Networks

    Jul 26, 2026Chunjing Li, Tiange Zhao, Xiaohui YuanLow-Rank FactorizationKernel Ridge Regression

  54. FedSLIM: Privacy-Preserving Federated MDL-Based Descriptive Pattern Mining Across Data Silos

    Jul 25, 2026Samar Samir Khalil, Noha S. Tawfik, Marco SpruitData MiningPrivacy-Preserving Framework

  55. Multi-Agent Privacy Game in Federated Learning: A Unified Mean-Field View

    Jul 25, 2026Kun Zhao, Xu ChenPrivacy Risks and ProtectionDistributional Learning

  56. Efficient Learning of Truncated Boolean Product Distributions: Influence to the Rescue

    Jul 24, 2026Rohan Chauhan, Ioannis PanageasOptimal Sample ComplexityDistributional Learning

  57. Projection Pursuit CPCANet for Domain Generalization

    Jul 24, 2026Yu-Hsi Chen, Abd-Krim SeghouaneMultimodal Domain GeneralizationDistributional Learning

  58. Distributional Determinantal Point Process for Repulsive Clustering of Distributions

    Jul 23, 2026Khai Nguyen, Yang Ni, Elizabeth Juarez-Colunga +1Determinantal Point ProcessMixture Models

  59. Fundamental limits of distributed multiclass classification from simple binary decisions

    Jul 21, 2026Ioannis Papageorgiou, Srinivas Nomula, Ayalvadi Ganesh +2Multiclass ClassificationDistributional Learning

  60. Distributional Matching for Vector Quantization: A Unified Theoretical and Empirical Framework

    Jul 17, 2026Xianghong Fang, Litao Guo, Hengchao Chen +8Calibration-Free Low-Bit Vector QuantizationVisual Representation Learning