# Lecture Notes: Selected topics on robust statistical learning theory

arXiv: https://arxiv.org/abs/1908.10761 · HTML: https://mlsanity.com/paper/1908.10761/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2019.

## Built on by (most cited first, 17 of 17)

- [How Does Adaptive Optimization Impact Local Neural Network Geometry?](https://mlsanity.com/paper/2211.02254/) (2022)
- [Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification](https://mlsanity.com/paper/2502.12465/) (2025)
- [Uniform Concentration Bounds toward a Unified Framework for Robust Clustering](https://mlsanity.com/paper/2110.14148/) (2021)
- [On the Curses of Future and History in Future-dependent Value Functions for Off-policy Evaluation](https://mlsanity.com/paper/2402.14703/) (2024)
- [Offline Reinforcement Learning in Large State Spaces: Algorithms and Guarantees](https://mlsanity.com/paper/2510.04088/) (2025)
- [Sharper Sub-Weibull Concentrations](https://mlsanity.com/paper/2102.02450/) (2021)
- [Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption](https://mlsanity.com/paper/2201.03182/) (2022)
- [Do you know what q-means?](https://mlsanity.com/paper/2308.09701/) (2023)
- [Learning pure quantum states (almost) without regret](https://mlsanity.com/paper/2406.18370/) (2024)
- [Robust Principal Component Analysis: A Median of Means Approach](https://mlsanity.com/paper/2102.03403/) (2021)
- [Sample-optimal classical shadows for pure states](https://mlsanity.com/paper/2211.11810/) (2022)
- [Robust Linear Predictions: Analyses of Uniform Concentration, Fast Rates and Model Misspecification](https://mlsanity.com/paper/2201.01973/) (2022)
- [Distribution Estimation of Contaminated Data via DNN-based MoM-GANs](https://mlsanity.com/paper/2212.13741/) (2022)
- [Principal eigenstate classical shadows](https://mlsanity.com/paper/2405.13939/) (2024)
- [Convex Clustering Redefined: Robust Learning with the Median of Means Estimator](https://mlsanity.com/paper/2511.14784/) (2025)
- [Tail-Aware Information-Theoretic Bounds for LLM Alignment under Heavy-Tailed Rewards](https://mlsanity.com/paper/2604.10727/) (2026)
- [Geometric Domain Adaptation via Optimal Transport for Linear Regression in R^2](https://mlsanity.com/paper/2606.14023/) (2026)

## Abstract (arXiv)

These notes gather recent results on robust statistical learning theory. The goal is to stress the main principles underlying the construction and theoretical analysis of these estimators rather than provide an exhaustive account on this rapidly growing field. The notes are the basis of lectures given at the conference StatMathAppli 2019.