Distributional Learning

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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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Learning a Sampling-Free Variational DNN Plugin from Tiny Training Sets to Refine OOD Segmentation With Uncertainty Estimation
Jun 14, 2026Jimut B. Pal, Suyash P. AwateMedical Image Segmentation Out-Of-Distribution Detection Performance
Spokes: Optimizing for Diverse Pretraining Data Selection
Jun 13, 2026Clarence Lee, Yejin Choi, Luke Zettlemoyer +2Efficient Data Selection Output and Model Diversity
MUFFLe: Efficient Model Update Compression via Generalized Deduplication for Federated Learning
Jun 12, 2026Xiaobo Zhao, Daniel E. LucaniFederated Fine-Tuning Wireless Resource Allocation
Recursively Trained Diffusion Models: Limiting Collapse Distribution and Spectral Characterization
Jun 11, 2026Naïl B. Khelifa, Richard E. Turner, Ramji VenkataramananDiffusion Models Distributional Learning
A solvable model for unsupervised federated learning
Jun 11, 2026Giovanni Catania, Aurélien Decelle, Gianluca Manzan +2Distributional Learning Latent Factor Representations
Fed-FBD: Federated Functional Block Diversification for Isolation, Privacy, and Surgical Unlearning
Jun 10, 2026Weijie Chen, Alan B. McMillanPrivacy-Preserving Framework Adversarial Transferability
Accurate and Resource-Efficient Federated Continual Learning
Jun 9, 2026Jebacyril Arockiaraj, Dhruv Parikh, Jayashree Adivarahan +2Efficient Continual Learning Fedavg
Range Penalization: Theoretical Insights with Applications in Federated Learning
Jun 9, 2026Yiyuan She, Zhaojun Hu, Yifan SunRegularization Methods Quadratic Slack Penalty
Avoiding Exponential Blow-Up in Distributive Lattice Submodular Minimization
Jun 8, 2026Ishant ShanuSubmodular Maximization Lattice
Improving Bayesian Optimization via Training-Aware Conditional Diffusion Models
Jun 7, 2026Yilin Zheng, Haowei Wang, Szu Hui Ng +1Bayesian Optimization Pocket-Conditioned Diffusion
Conditional Random Ordered Transport Spaces
Jun 6, 2026Lei Luo, Jian YangWasserstein Distance Optimal Transport
CausShield: Sample Reconstruction-Resilient Vertical FL via Causal Representation Learning
Jun 6, 2026Yongqi Jiang, Yansong Gao, Siguang Chen +1Robust Federated Learning Privacy-Preserving Framework
Amortizing Federated Adaptation: Hypernetwork Driven LoRA for Personalized Foundation Models
Jun 4, 2026Sunny Gupta, Shambhavi Shanker, Amit SethiFederated Fine-Tuning Adaptive-Rank Low-Rank Adaptation Baselines
Toward Multi-Domain and Long-Tailed Quantization via Feature Alignment and Scaling
Jun 3, 2026Ting-An Chen, Chin-Yuan Yeh, De-Nian YangDistributional Learning Domain Shift
DPDL: Towards Differential Privacy Preservation in Decentralized Stochastic Learning on Non-IID Data
Jun 3, 2026Yunsheng Yuan, Xue Xiao, Lina Wang +1Differential Privacy Dp-Sgd
When Do Fewer Coordinates Suffice in DP-SGD?
Jun 3, 2026Huiqi Zhang, Fang XieDp-Sgd Differential Privacy
FlashbackCL: Mitigating Temporal Forgetting in Federated Learning
Jun 2, 2026Mubarak A. Ojewale, Adriana E. Chis, Jorge M. Cortes-Mendoza +2Efficient Continual Learning Forgetting
Rethinking the Idiomaticity Decomposability Hypothesis: Evidence from Distributional Learning
Jun 2, 2026Maggie Mi, Golzar Atefi, Atsuki Yamaguchi +3Multilingual Idioms Syntactic Structure
IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning
Jun 1, 2026Farhin Farhad Riya, Olivera Kotevska, Jinyuan Stella SunFederated Learning Framework Differential Privacy
Closing the Alignment-Maturity Gap in Federated Prototype Learning
Jun 1, 2026Mario Casado-Diez, Alejandro Dopico-Castro, Verónica Bolón-Canedo +1Learnable Prototypes Distributional Learning
Convex Distance Operator Transport: A Convex and Geometry-Preserving Formulation
Jun 1, 2026Junhyoung Chung, Euijong Song, Won Hwa Kim +1Optimal Transport Convex Sets
Fair Finetuning Mitigates Distribution Inference Attacks
Jun 1, 2026Rakshit NaiduFairness-Aware Learning Fairness Audits
QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning
Jun 1, 2026Nazmus Shakib Shadin, Xinyue Zhang, Jingyi Wang +1Split Learning Distributional Learning
Quantum Algorithm for Distributed Reduction of Entanglements (QADR): A Trainable and Simulation-Efficient QML Framework
May 31, 2026Syed Farhan Ahmad, Gregory T. ByrdVariational Quantum Circuits Quantum Neural Networks
Local MixVR: Breaking the Communication-Sample Dependence in Distributed Learning
May 31, 2026Tehila Dahan, Bassel Hamoud, Roie Reshef +2Distributional Learning Optimal Sample Complexity
HASA: Subnet Allocation for Compute-Constrained Model-Heterogeneous Federated Learning
May 30, 2026Amir Hossein Shahdadian, Ahmed M. Abdelmoniem, Mahdi Taheri +2Federated Learning Framework Edge Computing
Canonicalized Stable-List Replay for Private Federated Continual Learning over Language-Model Embeddings
May 29, 2026Ibne Farabi Shihab, Abu Sa-Adat Mohamed Moon-Im Al Ahsan, Anuj SharmaEfficient Continual Learning Differential Privacy
PE-means: Improved Differentially Private $k$ -means Clustering through Private EvolutionPE-means: Improved Differentially Private k-means Clustering through Private Evolution
May 29, 2026Thomas Humphries, Zinan Lin, Sergey YekhaninDifferential Privacy K$-Means
Multivariate Distributional Reinforcement Learning Using Sliced Divergences
May 29, 2026Baptiste Debes, Tinne TuytelaarsDistributional Reinforcement Learning Non-Exponential Discounting
Density-Guided Robust Counterfactual Explanations on Tabular Data under Model Multiplicity
May 29, 2026Jun Tan, Qing Guo, Zicheng Xu +3Counterfactual Explanation Randomized Global-Local Density Estimation
On Distributional Reinforcement Learning in Chaotic Dynamical Systems
May 28, 2026James Rudd-Jones, Mirco Musolesi, María Pérez-OrtizDistributional Reinforcement Learning Chaotic Dynamical Systems
Diffusion Models Are Statistically Optimal for Learning Low-Dimensional Multi-Modal Distributions
May 28, 2026Jingda Wu, Changxiao CaiLow-Dimensional Structure Score-Based Diffusion Model
Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption
May 28, 2026Yankai Chen, Hanrong Zhang, Bowei He +2Input Corruptions Distributional Learning
Does Distributed Training Undermine Compute Governance?
May 28, 2026Robi RahmanArtificial Intelligence Governance Distributed Optimization
Rethinking FID Through the Geometry of the Reference Dataset
May 28, 2026Yunghee Lee, Byeonghyun PakFréchet Inception Distance Evaluation Metrics
Conservative neural posterior estimation via distributionally robust training
May 27, 2026William Laplante, Yuga Hikida, Charita Dellaporta +2Posterior Bayesian Inference Approaches
Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee
May 27, 2026Shiyuan Zuo, Jiashuo Li, Rongfei Fan +2Robust Federated Learning Dimensionality Reduction
Supervised Distributional Reduction via Optimal Transport and Dependence Maximization
May 26, 2026Sai-Aakash Ramesh, Archit Sood, Andrew Corbett +1Dimensionality Reduction Distributional Learning
SPHERE-JEPA: Spherical Prediction with Homogeneous Embeddings
May 26, 2026Léo Nicollier, Max Dunitz, Marc Pic +3Hyperspherical Embedding Geometry Hypersphere
Transformers Can Learn Posterior Predictive Distributions In-Context
May 26, 2026Gyeonghun Kang, Changwoo J. Lee, Xiang ChengPosterior Tabular Prior-Data Fitted Network
Separate Aggregation of Split Network for Personalized Federated Learning
May 26, 2026Yunseok Kang, Jaeyoung SongHeterogeneity Distributional Learning
Geometry Adaptive Counterfactual Distribution Learning with Diffusion-Guided Smoothing
May 25, 2026Kwangho KimConformal Inference Distributional Learning
Certified Robustness from Approximate Gaussian Mixture Structures in Pretrained Latent Spaces
May 25, 2026Konstantinos Emmanouilidis, Tianjiao Ding, Nghia Nguyen +2Adversarial Transferability Distributional Learning
Multi-Objective Learning for Diffusion Models: A Statistical Theory under Semi-Supervised Learning
May 24, 2026Ziheng Cheng, Yixiao Huang, Hanlin Zhu +5Diffusion Models Distributional Learning
Abduction-Deduction Entanglement: Domain Generalization via Representation Transplants
May 24, 2026Kasra Jalaldoust, Elias BareinboumDistributional Learning Entanglement
On the Push-Based Asynchronous Federated Learning: A Bias-Correction Aggregation Approach
May 24, 2026Jiahui Bai, Hai Dong, A. K. QinFederated Learning Framework Asynchronous Execution
Estimating Mixture Distributions via Stochastic Mirror Descent
May 24, 2026Mohammadreza Ahmadypour, Tara Javidi, Farinaz KoushanfarMixture Models Estimators
ChainLearn: A Blockchain-Based Capacity-Aware Framework for Federated Ensemble Learning
May 23, 2026Karan Sharma, Aditya Tripathi, Rahul Mishra +1Ensemble Distributional Learning
Distributed Quantum Learning over Near-term Devices: Convergence Analysis and Security Design
May 21, 2026Atit Pokharel, Shaba Shaon, Thomas Morris +1Quantum Algorithms Quantum Learning
FIRMA: FIbonacci Ring Model Aggregation for Privacy-preserving Federated Learning
May 21, 2026Rachid HedjamRobust Federated Learning Distributional Learning
Detecting Atypical Clients in Federated Learning via Representation-Level Divergence
May 21, 2026Cristian Pérez-Corral, Jose I. Mestre, Alberto Fernández-Hernández +2Distributional Learning Bregman Divergences
Symbolic Density Estimation for Discrete Distributions
May 20, 2026Ziwen Liu, Meng LiRandomized Global-Local Density Estimation Conditional Distribution
CRAFT: Conflict-Resolved Aggregation for Federated Training
May 20, 2026Ziqi Wang, Qiang Liu, Nils ThuereyDistributional Learning Adversarial Transferability
FedCoE: Bridging Generalization and Personalization via Federated Coordinated Dual-level MoEs
May 20, 2026Penglin Dai, Fulian Li, Xincao Xu +3Fedavg Multiple Model Experts
A Typed Tensor Language for Federated Learning
May 20, 2026Theofilos Mailis, Kalliopi-Christina Despotidou, Konstantinos Filippopolitis +6Tensor Tensor Programs
Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning
May 20, 2026Adda Akram Bendoukha, Heber Hwang Arcolezi, Nesrine Kaaniche +1Fairness-Aware Learning Differential Privacy
LOSCAR-SGD: Local SGD with Communication-Computation Overlap and Delay-Corrected Sparse Model Averaging
May 20, 2026Yassine Maziane, Ammar Mahran, Artavazd Maranjyan +1Distributed Optimization Dp-Sgd
Sample Complexity of Transfer Learning: An Optimal Transport Approach
May 19, 2026Haoyang Cao, Xin Guo, Wenpin Tang +1Transfer Learning Distributional Learning
Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning
May 18, 2026Arnab Auddy, Xiangni Peng, Subhadeep PaulDifferential Privacy Dp-Sgd
Federated Martingale Posterior Samping
May 18, 2026Boning Zhang, Matteo Zecchin, Mingzhao Guo +2Bayesian Neural Networks Posterior

Jun 14, 2026 · cs.CVJ/K move · Enter open · S save

Learning a Sampling-Free Variational DNN Plugin from Tiny Training Sets to Refine OOD Segmentation With Uncertainty Estimation

Jimut B. Pal, Suyash P. Awate

Centre for Machine Intelligence and Data Science (C-MInDS), Indian Institute of Technology (IIT) Bombay, Mumbai · Computer Science and Engineering (CSE) Department, Indian Institute of Technology (IIT) Bombay, Mumbai

Deep neural networks (DNNs) frequently fail to generalize to out-of-distribution (OOD) medical images because of variations in scanners and acquisition protocols. Retraining DNN models to address these distribution shifts is often impractical due to the high cost of acquiring and annotating new medical datasets. To address this, we introduce VarDeepPCA, a novel lightweight variational DNN framework designed to restore/refine degraded segmentation maps by leveraging intrinsic geometric priors. Unlike existing approaches that require target-domain data or extensive pre-training, our VarDeepPCA explicitly learns a distribution of valid anatomical geometries using only small in-distribution (ID) datasets. Theoretically, our novel variational learning framework leverages a reinterpretation of the softmax mapping to implicitly perform exact distribution modeling, thereby enabling computationally efficient, sampling-free learning and inference. This also enables VarDeepPCA to provide uncertainty estimates associated with its restored segmentation maps. We empirically validate our framework across 4 distinct clinical applications, using 14 publicly available datasets, involving segmentation of the myocardium, neuroretinal rim, prostate, and fetal head. Comparisons against 15 existing methods demonstrate that VarDeepPCA consistently restores segmentation maps produced by the existing methods on OOD data to (i) significantly improve anatomical plausibility of geometries and clinical utility of the segmentations, and (ii) significantly reduce errors, without needing any more training data than that used by existing methods.

Medical Image Segmentation Out-Of-Distribution Detection Performance Out-Of-Distribution Variational Inference Distributional Learning Geometric Priors Object Geometry

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