mlsanityData to 14 Oct 2026

Concentration Inequalities for Statistical Inference

arXiv 2011.02258 ↗First posted 4 Nov 2020math.ST cs.LG math.PRcited by 24

Not on the map. Why?

Abstract (arXiv)

This paper gives a review of concentration inequalities which are widely employed in non-asymptotical analyses of mathematical statistics in a wide range of settings, from distribution-free to distribution-dependent, from sub-Gaussian to sub-exponential, sub-Gamma, and sub-Weibull random variables, and from the mean to the maximum concentration. This review provides results in these settings with some fresh new results. Given the increasing popularity of high-dimensional data and inference, results in the context of high-dimensional linear and Poisson regressions are also provided. We aim to illustrate the concentration inequalities with known constants and to improve existing bounds with sharper constants.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Nov 2020.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 5; 2022: 8; 2023: 8; 2024: 2; 2025: 1.

24 papers.

Sort:

By area:

  1. 2023A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networkscited by 49
  2. 2021Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithmcited by 14
  3. 2022Distributed Differential Privacy in Multi-Armed Banditscited by 14
  4. 2021Sharper Sub-Weibull Concentrationscited by 8
  5. 2023Learning Dynamical Systems by Leveraging Data from Similar Systemscited by 8
  6. 2024Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networkscited by 8
  7. 2022Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Studycited by 7
  8. 2023Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regressioncited by 7
  9. 2022Learning Algorithm Generalization Error Bounds via Auxiliary Distributionscited by 6
  10. 2022Optimization on Manifolds via Graph Gaussian Processescited by 6
  11. 2023Learning Against Distributional Uncertainty: On the Trade-off Between Robustness and Specificitycited by 4
  12. 2023Multiplier Bootstrap-based Explorationcited by 3
12 more
  1. 2021Directional FDR Control for Sub-Gaussian Sparse GLMscited by 2
  2. 2022Distributional Robustness Bounds Generalization Errorscited by 2
  3. 2024Enhancing Semi-Supervised Learning via Representative and Diverse Sample Selectioncited by 2
  4. 2025Statistical Inference in Reinforcement Learning: A Selective Surveycited by 2
  5. 2021Heterogeneous Overdispersed Count Data Regressions via Double Penalized Estimationscited by 1
  6. 2022IPProtect: protecting the intellectual property of visual datasets during data valuationcited by 1
  7. 2023Tight Non-asymptotic Inference via Sub-Gaussian Intrinsic Moment Normcited by 1
  8. 2023Concentration inequalities for high-dimensional linear processes with dependent innovationscited by 1
  9. 2021Asymptotic in a class of network models with an increasing sub-Gamma degree sequencecited by 0
  10. 2022High-dimensional Inference and FDR Control for Simulated Markov Random Fieldscited by 0
  11. 2022Distribution Estimation of Contaminated Data via DNN-based MoM-GANscited by 0
  12. 2023Zero-Inflated Banditscited by 0