mlsanityData to 14 Oct 2026

Resilience: A Criterion for Learning in the Presence of Arbitrary Outliers

arXiv 1703.04940 ↗First posted 15 Mar 2017cs.LG cs.AI cs.CCcited by 43

Not on the map. Why?

Abstract (arXiv)

We introduce a criterion, resilience, which allows properties of a dataset (such as its mean or best low rank approximation) to be robustly computed, even in the presence of a large fraction of arbitrary additional data. Resilience is a weaker condition than most other properties considered so far in the literature, and yet enables robust estimation in a broader variety of settings. We provide new information-theoretic results on robust distribution learning, robust estimation of stochastic block models, and robust mean estimation under bounded kth moments. We also provide new algorithmic results on robust distribution learning, as well as robust mean estimation in ℓp-norms. Among our proof techniques is a method for pruning a high-dimensional distribution with bounded 1st moments to a stable "core" with bounded 2nd moments, which may be of independent interest.

Key facts. Cited by 43 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Mar 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 9; 2022: 13; 2023: 6; 2024: 9; 2025: 2; 2026: 4.

43 papers.

Sort:

  1. 2021Robust and Differentially Private Mean Estimationcited by 66
  2. 2022Byzantine Machine Learning Made Easy by Resilient Averaging of Momentumscited by 63
  3. 2021Efficient Mean Estimation with Pure Differential Privacy via a Sum-of-Squares Exponential Mechanismcited by 50
  4. 2022Robustness Implies Privacy in Statistical Estimationcited by 47
  5. 2022Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein Distancescited by 41
  6. 2021Private Robust Estimation by Stabilizing Convex Relaxationscited by 38
  7. 2022Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guaranteescited by 30
  8. 2023On the Privacy-Robustness-Utility Trilemma in Distributed Learningcited by 24
  9. 2022Streaming Algorithms for High-Dimensional Robust Statisticscited by 21
  10. 2021Robust Training in High Dimensions via Block Coordinate Geometric Median Descentcited by 20
  11. 2022Just Rotate it: Deploying Backdoor Attacks via Rotation Transformationcited by 20
  12. 2023Outlier-Robust Wasserstein DROcited by 16
31 more
  1. 2021Robust Regression Revisited: Acceleration and Improved Estimation Ratescited by 15
  2. 2021Outlier-Robust Learning of Ising Models Under Dobrushin's Conditioncited by 14
  3. 2022List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filteringcited by 13
  4. 2022List-Decodable Sparse Mean Estimationcited by 11
  5. 2024SoS Certifiability of Subgaussian Distributions and its Algorithmic Applicationscited by 10
  6. 2023Near Optimal Private and Robust Linear Regressioncited by 9
  7. 2021Robust Estimation for Random Graphscited by 8
  8. 2023Robust Estimation under the Wasserstein Distancecited by 8
  9. 2024A Subspace-Constrained Tyler's Estimator and its Applications to Structure from Motioncited by 8
  10. 2025Towards Trustworthy Federated Learning with Untrusted Participantscited by 8
  11. 2021Combinatorial Bandits under Strategic Manipulationscited by 6
  12. 2022Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Modelscited by 6
  13. 2024Robustness Auditing for Linear Regression: To Singularity and Beyondcited by 6
  14. 2024Robust Distribution Learning with Local and Global Adversarial Corruptionscited by 5
  15. 2022Robust estimation algorithms don't need to know the corruption levelcited by 4
  16. 2024Distribution Learnability and Robustnesscited by 4
  17. 2022Robust Estimation for Nonparametric Families via Generative Adversarial Networkscited by 3
  18. 2024SoS Certificates for Sparse Singular Values and Their Applications: Robust Statistics, Subspace Distortion, and Morecited by 3
  19. 2023Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oraclescited by 2
  20. 2024The Broader Landscape of Robustness in Algorithmic Statisticscited by 2
  21. 2021Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Timecited by 1
  22. 2022Communication-efficient distributed eigenspace estimation with arbitrary node failurescited by 1
  23. 2025On the Learnability of Distribution Classes with Adaptive Adversariescited by 1
  24. 2022Privately Estimating a Gaussian: Efficient, Robust and Optimalcited by 0
  25. 2023Sparse Mean Estimation in Adversarial Settings via Incremental Learningcited by 0
  26. 2024On the Detection of Anomalous or Out-Of-Distribution Data in Vision Models Using Statistical Techniquescited by 0
  27. 2024Iterative thresholding for non-linear learning in the strong varepsilon-contamination modelcited by 0
  28. 2026Robust Statistical Estimators with Bounded Empirical Sensitivitycited by 0
  29. 2026Algorithms with Polynomially-Improved Approximation Factors for the 2 rightarrow q Norm, and Applicationscited by 0
  30. 2026Wasserstein Filtering: A Sample Selection Method for Robust Distribution Learningcited by 0
  31. 2026On the SoS Certifiability of Log-Concave Distributionscited by 0