mlsanityData to 7 Oct 2026

Robust Learning from Untrusted Sources

arXiv 1901.10310 ↗First posted 29 Jan 2019cs.LG stat.MLcited by 27

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Abstract (arXiv)

Modern machine learning methods often require more data for training than a single expert can provide. Therefore, it has become a standard procedure to collect data from external sources, e.g. via crowdsourcing. Unfortunately, the quality of these sources is not always guaranteed. As additional complications, the data might be stored in a distributed way, or might even have to remain private. In this work, we address the question of how to learn robustly in such scenarios. Studying the problem through the lens of statistical learning theory, we derive a procedure that allows for learning from all available sources, yet automatically suppresses irrelevant or corrupted data. We show by extensive experiments that our method provides significant improvements over alternative approaches from robust statistics and distributed optimization.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jan 2019.

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Citing papers per year: 2021: 7; 2022: 6; 2023: 8; 2024: 3; 2025: 2; 2026: 1.

27 papers.

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  1. 2022Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learningcited by 49
  2. 2023An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learningcited by 32
  3. 2022Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasetscited by 28
  4. 2021Aggregating From Multiple Target-Shifted Sourcescited by 24
  5. 2022Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformercited by 21
  6. 2021Fairness-Aware PAC Learning from Corrupted Datacited by 18
  7. 2022Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labelscited by 16
  8. 2021Auto-weighted Robust Federated Learning with Corrupted Data Sourcescited by 12
  9. 2021Multitask Learning and Bandits via Robust Statisticscited by 11
  10. 2023Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptationcited by 7
  11. 2023Best-Effort Adaptationcited by 7
  12. 2022Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learningcited by 6
15 more
  1. 2023Distributed Personalized Empirical Risk Minimizationcited by 6
  2. 2023Rank-Aware Negative Training for Semi-Supervised Text Classificationcited by 3
  3. 2021DistFL: Distribution-aware Federated Learning for Mobile Scenarioscited by 2
  4. 2024Collaborative Learning with Different Labeling Functionscited by 2
  5. 2024AdaTrans: Feature-wise and Sample-wise Adaptive Transfer Learning for High-dimensional Regressioncited by 2
  6. 2025Federated Adapter on Foundation Models: An Out-Of-Distribution Approachcited by 2
  7. 2022Gray Learning from Non-IID Data with Out-of-distribution Samplescited by 1
  8. 2023Mixture Weight Estimation and Model Prediction in Multi-source Multi-target Domain Adaptationcited by 1
  9. 2023Robust and Actively Secure Serverless Collaborative Learningcited by 1
  10. 2021Probabilistic Inference for Learning from Untrusted Sourcescited by 0
  11. 2021FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Datacited by 0
  12. 2023R-divergence for Estimating Model-oriented Distribution Discrepancycited by 0
  13. 2024SAFLEX: Self-Adaptive Augmentation via Feature Label Extrapolationcited by 0
  14. 2025Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Modelscited by 0
  15. 2026Inference-Time Consensus for Mitigating Hidden Behaviors from LLM Fine-Tuningcited by 0