Distributional Learning

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Period ending 2026-09-21

7 new papers

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Period ending 2026-09-14

6 new papers

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Period ending 2026-09-07

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228 papers

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PeTeR: Post-Training Robustification of Probabilistic Circuits
Jul 8, 2026Adrian Ciotinga, Yeming Dai, YooJung ChoiDistributional Learning Probabilistic Inference
Collaborative Synthetic Data Generation for Knowledge Transfer in Federated Learning
Jul 8, 2026Maximilian Andreas Hoefler, Karsten Mueller, Wojciech SamekFederated Learning Differential Privacy
VCDP: Variation-Conditioned Distributional Proxy Learning for Semi-Supervised Medical Image Segmentation
Jul 8, 2026Zimu Zhang, Yiheng Zhong, Zhuoru Zhang +4Medical Image Segmentation Semi-Supervised Learning
Prior-aware and Context-guided Group Sampling for Active Probabilistic Subsampling
Jul 8, 2026Beomgu Kang, Hyunseok SeoSubsampling Distributional Learning
Robust Federated Learning Under Real-World Client Churn
Jul 8, 2026Dhruv Garg, Neha Lakhani, Debopam Sanyal +3Federated Learning Heterogeneous Edge Devices
SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity
Jul 5, 2026Liyang Yuan, Yibo Yang, Dandan Guo +2Federated Learning Heterogeneity
FedFFT: Taming Client Drift in Federated SAM via Spectral Perturbation Filtering
Jul 5, 2026Liyang Yuan, Yibo Yang, Dandan GuoFederated Learning Frequency Domain
FedProIn: Mitigating Client Drift for Learnable Prototypes in Federated Medical Imaging
Jul 5, 2026Harsh Kumar, Tarun Kumar Garg, Vaanathi SundaresanFederated Learning Fedavg
FedSPM: Routing-Enabled Federated Learning under Dual Heterogeneity via Semiparametric Mixture
Jul 5, 2026Zijian Wang, Pengfei Li, Guangyu Yang +1Heterogeneous Federated Learning Federated Learning
FedAvg for HAR: Exploring the Tradeoff Between Personalized and Generalization Accuracy
Jul 3, 2026Andrea De Luna, Susanna Peretti, Chiara Contoli +1Federated Learning Fedavg
Can Model Merging Improve Aggregation in DiLoCo?
Jul 3, 2026Stefan Horoi, Benjamin Thérien, Guy Wolf +1Continual Model Merging Model Merging
Understanding the Robustness of Distributed Self-Supervised Learning Frameworks Against Non-IID Data
Jul 2, 2026Xuanyu Chen, Nan Yang, Shuai Wang +1Self-Supervised Distributional Learning
Privacy-Preserving and Verifiable Approximate Distributed Coded Computing
Jul 2, 2026Xavier Martínez-Luaña, Alba Gude-Santos, Manuel Fernández-Veiga +1Privacy-Preserving Framework Federated Learning
Beyond the Performance Illusion: Structure-Aware Stratified Partitioning and Curriculum Distributionally Robust Optimization for Spatially Correlated Domains
Jul 2, 2026Prathamesh Patil, Arpit Jain, Aswanth KrishnanPartitioning Distributional Learning
Learning the Supports for Categorical Critic in Reinforcement Learning
Jul 2, 2026Jen-Yen Chang, Takayuki Osa, Tatsuya HaradaSoft Actor-Critic Value Functions
Unveiling the Non-Monotonic Effect of Privacy on Generalization under Byzantine Robustness
Jul 1, 2026Thomas Boudou, Batiste Le Bars, Nirupam Gupta +1Differential Privacy Generalization Bounds
Human-Machine Collaboration on Generative Meta-Learning: Model and Algorithm
Jul 1, 2026Midhun Parakkal Unni, Samuel KaskiMeta-Learning Reinforcement Learning From Human Feedback
Entropy-Regularized Probabilistic Gates for Sparse Model Discovery in Scarce-Data Federated Learning
Jun 30, 2026Krishna Harsha Kovelakuntla Huthasana, Alireza Olama, Andreas LundellFederated Learning Entropy-Regularized Equilibrium Selection
Distributionally Robust Linear Regression With Block Lewis Weights
Jun 30, 2026Naren Sarayu Manoj, Kumar Kshitij PatelLeast Squares Proximal Gradient Descent
Discovering Collaboration from Novelty: Random Network Distillation for Clustered Federated Learning
Jun 29, 2026Davide Domini, Gianluca Aguzzi, Ivana Dusparic +2Federated Learning Distributional Learning
Federated Learning with Energy-Based Structured Probabilistic Inference
Jun 29, 2026Dario Fenoglio, Daniil Kirilenko, Martin Gjoreski +1Federated Learning Distributional Learning
Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning
Jun 27, 2026Wenhao Yuan, Chenchen Lin, Jian Chen +3Federated Learning Domain Incremental Learning
FoggyTrust: Robust Federated Learning with Hierarchical Trust Networks
Jun 26, 2026Emmanuel Rassou, Tomas GonzalezFederated Learning Byzantine Attacks
Learning Subset-Shared Invariances for Domain Generalization with Mixture-of-Experts
Jun 24, 2026Tien-Hung Nguyen, Tien-Dat Tran, M. -Duong Nguyen +1Multimodal Domain Generalization Domain Generalization
TL++: Accuracy and Privacy Preserving Traversal Learning for Distributed Intelligent Systems
Jun 24, 2026Erdenebileg Batbaatar, Young YoonFederated Learning Privacy-Preserving Framework
Data Augmentation: A Fourier Analysis Perspective
Jun 23, 2026Behrooz Tahmasebi, Melanie Weber, Stefanie JegelkaAugmentation Distributional Learning
Factored Gossip DiLoCo: Reducing Blocking Communication in DiLoCo
Jun 22, 2026Chamin Hewa Koneputugodage, Thalaiyasingam Ajanthan, Sameera Ramasinghe +7Distributed Optimization Distributional Learning
Federated learning with heavy-tailed gradient noise and communication noise: a variance-reduction based algorithm
Jun 21, 2026Shengchao Zhao, Yongchao LiuFederated Learning Fedavg
Decision-Focused Learning: When and Why Traditional Prediction Models Fail
Jun 19, 2026Mo LiuPredictive Uncertainty Sequential Decision Making
Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation
Jun 19, 2026Conghui Li, Junhao Huang, Chern Hong Lim +2Distributional Learning Identity-Conditioned Latent Diffusion Models
Federated Bilevel Performative Prediction
Jun 18, 2026Liangxin Qian, Chang Liu, Xuanyu Cao +2Bilevel Optimization Distributional Learning
Wasserstein Policy Learning for Distributional Outcomes
Jun 17, 2026Yiyan Huang, Cheuk Hang Leung, Qi Wu +1Wasserstein Distance Causal Inferences
Online Distributional Prediction via Latent Cluster Geometry Under Drift and Corruption
Jun 17, 2026Navyansh Mahla, Prateek Chanda, Ganesh RamakrishnanDistributional Learning Posterior
Expanding SPHERE-JEPA: A Family of Statistical Regularizers for the Hypersphere
Jun 16, 2026Léo Nicollier, Enric Meinhardt-Llopis, Max Dunitz +3Regularization Techniques Maximum Mean Discrepancy
When Generator Replay Degrades: Projected Rehearsal Orchestration for Heterogeneous Federated Class-Incremental Learning
Jun 14, 2026Thinh T. H. Nguyen, Khoa D. Doan, Binh T. Nguyen +2Domain Incremental Learning Retraining
MUFFLe: Efficient Model Update Compression via Generalized Deduplication for Federated Learning
Jun 12, 2026Xiaobo Zhao, Daniel E. LucaniFederated Learning Wireless Resource Allocation
Recursively Trained Diffusion Models: Limiting Collapse Distribution and Spectral Characterization
Jun 11, 2026Naïl B. Khelifa, Richard E. Turner, Ramji VenkataramananDistributional Learning Collapse
A solvable model for unsupervised federated learning
Jun 11, 2026Giovanni Catania, Aurélien Decelle, Gianluca Manzan +2Federated Learning Distributional Learning
Accurate and Resource-Efficient Federated Continual Learning
Jun 9, 2026Jebacyril Arockiaraj, Dhruv Parikh, Jayashree Adivarahan +2Federated Learning Efficient Continual Learning
The Power of Test-Time Training for Approximate Sampling
Jun 9, 2026Noah Golowich, Ankur Moitra, Dhruv RohatgiOptimal Sample Complexity Inference-Time
OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework
Jun 7, 2026Chenhan Jin, Shengze Xu, Qingsong Wang +3Heterogeneous Pruning Scores Pruning
Improving Bayesian Optimization via Training-Aware Conditional Diffusion Models
Jun 7, 2026Yilin Zheng, Haowei Wang, Szu Hui Ng +1Bayesian Optimization Bayesian
Conditional Random Ordered Transport Spaces
Jun 6, 2026Lei Luo, Jian YangWasserstein Distance Optimal Transport
Partially Performative Prediction
Jun 5, 2026Jaewook Lee, Tijana ZrnicDistribution Shifts Distributional Learning
DPDL: Towards Differential Privacy Preservation in Decentralized Stochastic Learning on Non-IID Data
Jun 3, 2026Yunsheng Yuan, Xue Xiao, Lina Wang +1Differential Privacy Distributional Learning
Edge of Stability Selectively Shapes Learning Across the Data Distribution
Jun 2, 2026Shauna Kwag, Anakha Ganesh, Tomaso Poggio +1Distributional Learning Condition Number
FlashbackCL: Mitigating Temporal Forgetting in Federated Learning
Jun 2, 2026Mubarak A. Ojewale, Adriana E. Chis, Jorge M. Cortes-Mendoza +2Federated Learning Efficient Continual Learning
Rethinking the Idiomaticity Decomposability Hypothesis: Evidence from Distributional Learning
Jun 2, 2026Maggie Mi, Golzar Atefi, Atsuki Yamaguchi +3Multilingual Idioms Syntactic Structure
Closing the Alignment-Maturity Gap in Federated Prototype Learning
Jun 1, 2026Mario Casado-Diez, Alejandro Dopico-Castro, Verónica Bolón-Canedo +1Heterogeneous Federated Learning Federated Learning
QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning
Jun 1, 2026Nazmus Shakib Shadin, Xinyue Zhang, Jingyi Wang +1Federated Learning Distributional Learning
Local MixVR: Breaking the Communication-Sample Dependence in Distributed Learning
May 31, 2026Tehila Dahan, Bassel Hamoud, Roie Reshef +2Distributional Learning Mini-Batch Stochastic Gradient Descent
Distributed GNEP Algorithms without Multiplier Sharing and Applications to Multi-Robot Coordination and Contextual Bandit-Based Active Learning
May 30, 2026Shao-An YinMulti-Agent Reinforcement Learning Nash Equilibrium
DistMatch: Adaptive Binning via Distribution Matching for Robust Sequential Conformal Prediction
May 30, 2026Enver Menadjiev, Jihyeon Seong, Jisu Yeo +1Conformal Prediction Quantile Regression
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 SharmaFederated Learning Efficient Continual Learning
KISS: Keeping it Simple and Slotted when Learning to Communicate over Wireless
May 29, 2026Kamil Szczech, Maksymilian Wojnar, Krzysztof Rusek +2Wireless Networks Multi-Agent Reinforcement Learning
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 +2Robust Optimization Corrupted Observations
Test Time Training for Supervised Causal Learning
May 28, 2026Zizhen Deng, Jiaru Zhang, Rui Ding +5Causal Foundation Models Supervised Learning
Does Distributed Training Undermine Compute Governance?
May 28, 2026Robi RahmanDistributed Optimization Privacy-Preserving Framework
Supervised Distributional Reduction via Optimal Transport and Dependence Maximization
May 26, 2026Sai-Aakash Ramesh, Archit Sood, Andrew Corbett +1Dimensionality Reduction Distributional Learning

Jul 8, 2026 · cs.LGJ/K move · Enter open · S save

PeTeR: Post-Training Robustification of Probabilistic Circuits

Adrian Ciotinga, Yeming Dai, YooJung Choi

School of Computing and Augmented Intelligence, Arizona State University

Probabilistic circuits (PCs) can model complex joint distributions while supporting exact and efficient computation of many inference queries. However, standard likelihood-based PC learning is vulnerable to overfitting and fragile generalization when confronted with data noise, small sample sizes, or distribution shifts. This can be mitigated using distributionally-robust optimization which consider worst-case distributions within a Wasserstein ball of the empirical distribution, but current methods are limited to training a model from scratch in this framework. Instead, we propose PeTeR: a novel, data-free post-training framework designed to robustify pre-trained PCs against distribution shifts without retraining from scratch. Empirical evaluations across multiple density estimation benchmarks demonstrate that PeTeR effectively robustifies baseline models against both random and adversarial perturbations, achieving competitive or superior performance to data-dependent robust learning baselines.

Distributional Learning Probabilistic Inference LLM Post-Training Methods

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