mlsanityData to 14 Oct 2026

An extension of McDiarmid's inequality

arXiv 1511.05240 ↗First posted 17 Nov 2015cs.LG math.PR math.STcited by 9

Not on the map. Why?

Abstract (arXiv)

We generalize McDiarmid's inequality for functions with bounded differences on a high probability set, using an extension argument. Those functions concentrate around their conditional expectations. We further extend the results to concentration in general metric spaces.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Nov 2015.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 5.

9 papers.

  1. 2024Generalising realisability in statistical learning theory under epistemic uncertaintycited by 5
  2. 2024Metric Entropy-Free Sample Complexity Bounds for Sample Average Approximation in Convex Stochastic Programmingcited by 1
  3. 2025Rethinking Federated Learning Over the Air: The Blessing of Scaling Upcited by 1
  4. 2026Harmful Overfitting in Sobolev Spacescited by 1
  5. 2025Sharper Risk Bound for Multi-Task Learning with Multi-Graph Dependent Datacited by 0
  6. 2026Statistical Learning from Attribution Setscited by 0
  7. 2026On McDiarmid's Inequality under Dependence via Approximate Tensorization of Entropycited by 0
  8. 2026Extreme Meta-Classification for Large-Scale Zero-Shot Retrievalcited by 0
  9. 2026A Generalized McDiarmid Inequality: Sample Reuse and Non-Uniform Unbounded Differencescited by 0