Distributional Learning

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

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Period ending 2026-09-21

7 new papers

A weekly snapshot of new work published in Distributional Learning.

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.

228 papers

Latest in Distributional Learning

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  1. A Distributional Optimisation Perspective on Combining Models in Deep Learning

    Sep 21, 2026Congye Wang, Yan Lin, Zheyang Shen +2Distributional LearningMixture Models

  2. Adversarially Robust PAC Learning with Optimal VC Rates

    Sep 21, 2026Steve Hanneke, Amirreza ShaeiriOptimal Sample ComplexityDistributional Learning

  3. Multivariate quantile regression via Kolmogorov-Arnold Networks

    Sep 20, 2026Andrew Polar, Michael PoluektovKolmogorov-Arnold NetworksQuantile Regression

  4. Adaptive Determinantal Client Scheduling in Federated Learning

    Sep 20, 2026Wen Xu, Ben Liang, Gary Boudreau +1Optimal SchedulingDistributional Learning

  5. Belted Engression: Sufficient Dimension Reduction for Generative Distributional Regression

    Sep 20, 2026Wenxi Tan, Bing Li, Lingzhou XueGenerative ModelsDistributional Learning

  6. Limits of Confidence in Diffusion

    Sep 17, 2026Russ Webb, Amitis Shidani, Alice Bizeul +1Conditional DistributionDistributional Learning

  7. Distributionally Robust Federated Learning with Multi-Source Data

    Sep 17, 2026Yingzhu Liu, Zhongkui Li, Pengcheng You +1Federated LearningDistributional Learning

  8. Efficiently Distributed Federated Learning

    Sep 17, 2026Gianluca Mittone, Robert Birke, Marco AldinucciFederated LearningHeterogeneous Edge Devices

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

    Sep 14, 2026Yilin Xu, Chun Hei Michael Shiu, Chih Wei Ling +1Federated LearningDifferential Privacy

  10. 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 +1Federated LearningByzantine Attacks

  11. Sylvas: Synergistic Learning Value based Device Scheduling in Federated Continual Learning

    Sep 14, 2026Yuxuan Sun, Yuxuan Bai, Tan Chen +2Efficient Continual LearningHeterogeneous Edge Devices

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

    Sep 14, 2026Doryan Lesaignoux, Enrique Mármol Campos, Gabriele Spini +2Federated LearningByzantine Attacks

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

    Sep 11, 2026Shaddin Dughmi, Alireza F. PourSemi-Supervised LearningOptimal Sample Complexity

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

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

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

    Sep 7, 2026Xiao Ma, Hong Shen, Hui Tian +2Federated LearningByzantine Attacks

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

    Sep 3, 2026Michael Khavkin, Kichang Lee, Jaeho Jin +2Federated LearningDifferential Privacy

  17. Learning the Geometry of Admissible Hypotheses through Inductive Bias in Training Distributions

    Aug 31, 2026James Crowley, Faez Ahmed, Anton van BeekPartial Differential EquationsUnified Inductive Bias

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

    Aug 25, 2026Hao Wu, Kin Whye Chew, Yizhan Han +2Distributional LearningSatellite

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

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

  20. Statistical Properties of Robust Learning under Distributional Shifts

    Aug 13, 2026Zhiyi Li, Xiaojie Mao, Yunbei Xu +1Distribution ShiftsDistributional Learning

  21. Federated Compositional Muon Optimizer for Matrix-Wise Models

    Aug 13, 2026Wang Yan, Feihu HuangMuon OptimizerMuon

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

    Aug 12, 2026Ellen Su, Andres Potapczynski, Shikai Qiu +2Distributional Learning

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

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

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

    Aug 10, 2026Jae Ho Chang, Arnab Auddy, Subhadeep PaulFederated LearningVariational Inference

  25. A Mean-Field Framework for Inference-Time Distributional Control of Diffusion Models

    Aug 9, 2026Samuel Howard, Nikolas NüskenInference-Time ControlDistributional Learning

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

    Aug 9, 2026Parham Sazdar, Mostafa Tavassolipour, Reshad HosseiniMultimodal Domain GeneralizationHeterogeneous Pruning Scores

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

    Aug 7, 2026M. Sajid, A. Quadir, A. Rahaman +2Random ForestEnsemble

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

    Aug 6, 2026Grigory MalinovskyFederated LearningDistributed Optimization

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

    Aug 5, 2026Rahil Aftab, Vineet Kumar Rakesh, Soumya Mazumdar +1Federated LearningFedavg

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

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

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

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

  32. Diverse and Plausible Algorithmic Recourse via Tractable Recourse Distributions

    Aug 5, 2026Anagha Sabu, Hrithik Suresh, Narayanan C. KrishnanCounterfactual ReasoningDistributional Learning

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

    Aug 4, 2026Chenhan Xiao, Xinyu He, Haoran Li +2Ac Optimal Power FlowScenario Generation

  34. GLOBE: Trajectory-Aligned Gradient Matching with Structured SparseOptimization for Coreset Selection

    Aug 3, 2026Hetian Liu, Jin Cui, Mengcheng Shi +4Geometry-Aware Uncertainty CoresetsDistributional Learning

  35. Statistical Mechanics of Learning on Product Wasserstein Manifolds

    Aug 2, 2026Srinivasa Rao P Vangmayi P ReddyWasserstein DistanceDistributional Learning

  36. 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

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

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

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

    Jul 30, 2026Kağan Akman, Naci Saldi, Serdar YükselGeneralization BoundsDistributional Learning

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

    Jul 30, 2026Xiang Yuan, Kaiqing Lei, Zhenyu Jin +3ReweightingModel Weights

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

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

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

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

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

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

  43. PIcsC: Partitioning-Induced Covariate Shift Correction

    Jul 28, 2026Behraj Khan, Behroz Mirza, Syed Ahmad Chan Bukhari +1Federated LearningPartitioning

  44. Generative Distributionally Robust Optimization

    Jul 27, 2026Ziwei Zhang, Jonathan Yu-Meng Li, Zhihao JinRobust OptimizationDistributional Learning

  45. Learning Distributions from Multiple Data Providers

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

  46. Adaptive Data Admission and Retention for Streaming Federated Learning

    Jul 27, 2026Zhuoyi Zhao, Ben LiangFederated LearningLearning-Augmented Algorithms

  47. Directional Influence Function: Estimating Training Data Influence in Constrained Learning

    Jul 25, 2026Xin Wang, R. Tyrrell Rockafellar, Xuegang +1Efficient Influence FunctionInfluence

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

    Jul 25, 2026Samar Samir Khalil, Noha S. Tawfik, Marco SpruitFederated LearningData Mining

  49. Spectral-Aware Analytic Class-Incremental Learning for Long-Tailed Distributions

    Jul 24, 2026Quyen Tran, Hai Nguyen, Quan Dao +4Domain Incremental LearningRegularization Techniques

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

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

  51. Projection Pursuit CPCANet for Domain Generalization

    Jul 24, 2026Yu-Hsi Chen, Abd-Krim SeghouaneMultimodal Domain GeneralizationDomain Generalization

  52. Distributional Determinantal Point Process for Repulsive Clustering of Distributions

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

  53. Online Variance Reduction for Domain Adaptation on Streaming Data

    Jul 22, 2026Andrea NapoliVariance ReductionDomain Adaptation

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

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

  55. Parallel gradient boosting for flexible estimation of conditional distributions

    Jul 15, 2026Rémy Chapelle, Nicolas Vayatis, Bruno Falissard +1Quantile RegressionGradient Boosted Tree

  56. Privacy-Aware Collaborative and Distributed Bayesian Optimization

    Jul 13, 2026Aditya Rane, Sathwik Yamana, Paritosh Ramanan +2Differential PrivacyBayesian Optimization

  57. Generalized Distribution-Free Semi-Supervised Learning with Risk Rewrite

    Jul 11, 2026Yushi Hirose, Hiroo Irobe, Takafumi KanamoriSemi-Supervised LearningDistributional Learning

  58. Learning Predictive Ambiguity Sets for Decision-Focused Distributionally Robust Optimization

    Jul 10, 2026Junjie GuoRobust OptimizationPredictive Uncertainty

  59. Statistical Efficiency and Inference of Quantile Distributional Reinforcement Learning

    Jul 9, 2026Zijie Cheng, Yang Peng, Zhihua ZhangQuantile RegressionDistributional Learning

  60. FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning

    Jul 9, 2026Lingyu Qiu, Daniela Annunziata, Stefano Izzo +2Federated LearningFedavg