# Robust Learning from Untrusted Sources

arXiv: https://arxiv.org/abs/1901.10310 · HTML: https://mlsanity.com/paper/1901.10310/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2019.

## Built on by (most cited first, 27 of 27)

- [Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning](https://mlsanity.com/paper/2205.12679/) (2022)
- [An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning](https://mlsanity.com/paper/2302.07173/) (2023)
- [Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasets](https://mlsanity.com/paper/2207.05471/) (2022)
- [Aggregating From Multiple Target-Shifted Sources](https://mlsanity.com/paper/2105.04051/) (2021)
- [Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer](https://mlsanity.com/paper/2207.11971/) (2022)
- [Fairness-Aware PAC Learning from Corrupted Data](https://mlsanity.com/paper/2102.06004/) (2021)
- [Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels](https://mlsanity.com/paper/2207.14476/) (2022)
- [Auto-weighted Robust Federated Learning with Corrupted Data Sources](https://mlsanity.com/paper/2101.05880/) (2021)
- [Multitask Learning and Bandits via Robust Statistics](https://mlsanity.com/paper/2112.14233/) (2021)
- [Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning](https://mlsanity.com/paper/2204.07373/) (2022)
- [Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation](https://mlsanity.com/paper/2304.02064/) (2023)
- [Best-Effort Adaptation](https://mlsanity.com/paper/2305.05816/) (2023)
- [Distributed Personalized Empirical Risk Minimization](https://mlsanity.com/paper/2310.17761/) (2023)
- [Rank-Aware Negative Training for Semi-Supervised Text Classification](https://mlsanity.com/paper/2306.07621/) (2023)
- [AdaTrans: Feature-wise and Sample-wise Adaptive Transfer Learning for High-dimensional Regression](https://mlsanity.com/paper/2403.13565/) (2024)
- [DistFL: Distribution-aware Federated Learning for Mobile Scenarios](https://mlsanity.com/paper/2110.11619/) (2021)
- [Collaborative Learning with Different Labeling Functions](https://mlsanity.com/paper/2402.10445/) (2024)
- [Federated Adapter on Foundation Models: An Out-Of-Distribution Approach](https://mlsanity.com/paper/2505.01075/) (2025)
- [Gray Learning from Non-IID Data with Out-of-distribution Samples](https://mlsanity.com/paper/2206.09375/) (2022)
- [Mixture Weight Estimation and Model Prediction in Multi-source Multi-target Domain Adaptation](https://mlsanity.com/paper/2309.10736/) (2023)
- [Robust and Actively Secure Serverless Collaborative Learning](https://mlsanity.com/paper/2310.16678/) (2023)
- [Inference-Time Consensus for Mitigating Hidden Behaviors from LLM Fine-Tuning](https://mlsanity.com/paper/2607.23394/) (2026)
- [Probabilistic Inference for Learning from Untrusted Sources](https://mlsanity.com/paper/2101.06171/) (2021)
- [FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data](https://mlsanity.com/paper/2106.11732/) (2021)
- [R-divergence for Estimating Model-oriented Distribution Discrepancy](https://mlsanity.com/paper/2310.01109/) (2023)
- [SAFLEX: Self-Adaptive Augmentation via Feature Label Extrapolation](https://mlsanity.com/paper/2410.02512/) (2024)
- [Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models](https://mlsanity.com/paper/2507.17008/) (2025)

## 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.