Candidate Label Distributions

Follow this topic Ask about this topic

Recent momentum

+75%

7 papers in the last 28 days · 0.1% of indexed attention

Twelve weeks of publication activity for this topic as it is defined today.

Weekly history

Recent digests

What was published in this topic, kept on the site without email delivery.

Period ending 2026-09-21

3 new papers

A weekly snapshot of new work published in Candidate Label Distributions.

Period ending 2026-09-14

2 new papers

A weekly snapshot of new work published in Candidate Label Distributions.

36 papers

Latest in Candidate Label Distributions

Open your feed →

Personalized Federated Learning through Global Knowledge Distillation and Local Head Adaptation
Sep 15, 2026Polycarpo Souza Neto, José Mairton Barros da Silva Júnior, Charles Casimiro CavalcanteFederated Learning Candidate Label Distributions
ParsHate: A Benchmark Dataset for Hate and Target Detection in Persian
Sep 14, 2026Zahra Bokaei, Walid Magdy, Bonnie WebberHate Speech Hate
Split Conformal Prediction with Label-Shift-Adjusted Bayesian Scores
Sep 14, 2026Hyeonsu Lee, Juyeon Kim, Erkhembayar Jadamba +2Nonconformity Scores Conformal Prediction
From matrix inversion to constraints: provably tighter confidence regions for importance weights in label shift
Sep 13, 2026Mushan Li, Kihyun Han, Yanyuan MaDomain Adaptation Candidate Label Distributions
Conformal Calibration Transfer
Sep 11, 2026Achref DoulaConformal Prediction Conformal Calibration
Decision Shifts, Lost Label Functionality, and an Inconclusive Grounding Audit in Correctness-Gated Multi-Teacher Distillation
Sep 9, 2026Xiaofei FengMulti-Teacher Distillation Candidate Label Distributions
FedRAW: Preserving Rare-Label Influence in Asynchronous Federated Learning
Sep 7, 2026Prashant Bajpai, Divya Saxena, Philippe Lalanda +1Federated Learning Candidate Label Distributions
Coordination on a Budget: Federated Active Learning with Few Labels
Aug 19, 2026Liam Mohr, Daphna WeinshallActive Learning Candidate Label Distributions
Exponential Convex Calibration Dimension for the Multi-Label Jaccard Measure
Aug 13, 2026Mingyuan ZhangMulti-Label Classification Candidate Label Distributions
Targeted Label-Flipping and Oversampling Attacks on Federated Conditional GANs
Aug 10, 2026Panav Shah, Avishek GhoshFederated Learning Candidate Label Distributions
Label Granularity Skew in Federated Learning with Hierarchical Image Classification
Aug 10, 2026Jaeheon Kim, Hokeun Kim, Bong Jun ChoiHeterogeneous Federated Learning Federated Learning
D3O: Dynamic Distribution Distillation for Ordinal Regression
Jul 26, 2026Chunlai Dong, Yaojun Hu, Yuyang Xu +2Candidate Label Distributions Bidirectional Distillation
The Label Complexity of Class-Conditional Coverage under Distribution Shift
Jul 20, 2026Weijia Han, Lisha QuCandidate Label Distributions Finite-Sample Coverage Guarantees
Learning Emotion from Motion: Kinetic Multi-Stream Skeleton Modeling with Metadata-Conditioned Weak Label Distributions
Jul 19, 2026Sosuke Suzuki, Yijin Wei, Koichiro Kamide +3Skeleton-Based Action Recognition Emotion Recognition
When does distribution shift break graph neural networks calibration?
Jul 12, 2026Abderaouf BahiGraph Neural Networks Distribution Shifts
WHERE to Generate Matters: Budget-Aware Synthetic Augmentation for Label Skewed Federated Learning
Jul 7, 2026Sangwoo Lee, Sunghwan Park, Jaewoo LeeFederated Learning Fedavg
Do Counterfactually Fair Image Classifiers Satisfy Group Fairness? -- A Theoretical and Empirical Study
Jul 7, 2026Sangwon Jung, Sumin Yu, Sanghyuk Chun +1Fairness-Aware Learning Counterfactual Learning
Geometry-Aware Bayesian Quantification via Compositional Data Analysis
Jul 6, 2026Alejandro Moreo, Pablo González, Juan José del CozCandidate Label Distributions Density Ratio Estimation
Group-invariant Coresets for Data-efficient Active Learning
Jul 1, 2026L. C. Ayres, J. C. M. Bermudez, S. J. M. de Almeida +1Geometry-Aware Uncertainty Coresets Active Learning
Condensing Large-Scale Datasets Directly with Minimal Information Loss
Jul 1, 2026Xinyi Shang, Peng Sun, Bei Shi +2Imagenet Candidate Label Distributions
Conformal Prediction with Macro-Coverage Guarantees
Jun 26, 2026Aabesh Bhattacharyya, Tiffany Ding, Rina Foygel BarberConformal Prediction Finite-Sample Coverage Guarantees
FedReLa: Imbalanced Federated Learning via Re-Labeling
Jun 24, 2026Guangzheng Hu, Patricia Menéndez, Feng Liu +3Federated Learning Class Imbalance
Dual Distribution Estimation for Zero-shot Noisy Test-Time Adaptation with VLMs
Jun 24, 2026Wenjie Zhu, Yabin Zhang, Liang Xu +3Out-Of-Distribution Detection Vision-Language Model Adaptation
Distributional Loss for Robust Classification
Jun 11, 2026Kathleen Anderson, Thomas MartinetzCandidate Label Distributions Loss Function
Conformal Bayes under Label Shift: Post-Hoc Calibration vs. In-Training Adaptation
Jun 10, 2026Seungjin ChoiConformal Inference Conformal Calibration
Same Target, Different Basins: Hard vs. Soft Labels for Annotator Distributions
May 20, 2026Mirerfan Gheibi, Gashin GhazizadehSoft-Label Candidate Label Distributions
SDM: A Powerful Tool for Evaluating Model Robustness
May 19, 2026Xinlei Liu, Tao Hu, Jichao Xie +3Adversarial Examples Stochastic Gradient Descent
Locality-aware Private Class Identification for Domain Adaptation with Extreme Label Shift
May 7, 2026Chuan-Xian Ren, Cheng-Jun Guo, Hong YanDomain Adaptation Candidate Label Distributions
Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity
May 6, 2026Junxiang Wu, Zhiqiang Kou, Hongwei Zeng +7Federated Learning Candidate Label Distributions
A General Representation-Based Approach to Multi-Source Domain Adaptation
Apr 26, 2026Ignavier Ng, Yan Li, Zijian Li +3Domain Adaptation Candidate Label Distributions
Soft Label Pruning and Quantization for Large-Scale Dataset Distillation
Apr 20, 2026Xiao Lingao, Yang HeChain-Of-Thought Distillation Candidate Label Distributions
Holistic Optimal Label Selection for Robust Prompt Learning under Partial Labels
Apr 8, 2026Yaqi Zhao, Haoliang Sun, Yating Wang +2Candidate Label Distributions Few-Shot Learning
Factorizable joint shift revisited
Jan 21, 2026Dirk TascheDistribution Shifts Candidate Label Distributions
FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcasting
Jul 24, 2025Zhongzheng Yuan, Lianshuai Guo, Xunkai Li +3Federated Graph Learning Graph Representation Learning
Approaching the Harm of Gradient Attacks While Only Flipping Labels
Feb 28, 2025Abdessamad El-Kabid, El-Mahdi El-MhamdiCandidate Label Distributions Adversarial Examples
Label Differential Privacy via Aggregation
Date pendingAnand Brahmbhatt, Rishi Saket, Shreyas Havaldar +3Differential Privacy Candidate Label Distributions

Sep 15, 2026 · cs.LGJ/K move · Enter open · S save

Personalized Federated Learning through Global Knowledge Distillation and Local Head Adaptation

Polycarpo Souza Neto, José Mairton Barros da Silva Júnior, Charles Casimiro Cavalcante

Universidade Federal do Cear´a, Fortaleza, CE, Brazil · Uppsala University, Uppsala, Sweden

Statistical heterogeneity limits federated learning when a single global classifier cannot represent client-specific label distributions. In this work, we propose Personalized Federated Knowledge Distillation with Head Adaptation (pFedKDH), which aggregates only the shared backbone, keeps persistent client-specific heads, and uses a recalibrated global head as a teacher during local training. Across MNIST, Fashion-MNIST, CIFAR10, and CIFAR100 under class-wise Dirichlet partitions, pFedKDH obtains the best accuracy in most settings, with accuracy gaps up to 37.67% over the weakest baseline and consistently low standard deviation across repetitions. Component-wise diagnostics and convergence results support the role of persistent heads and distillation-guided local optimization under label-skewed data.

Federated Learning Candidate Label Distributions Knowledge Distillation Parameter-Efficient Adaptation

Open paper Ask about it PDF