Syllabus: link

Please e-mail typos/corrections to me (jsteinhardt@berkeley with a dot edu at the end).

See here if you prefer notes broken up on a per-lecture basis.

Problem Set 2 (due February 23rd before class) tex source

Problem Set 3 (due March 9th before class) tex source

Lecture 1: Overview and 1D Robust Estimation (video) (tablet)

Lecture 2: Minimum Distance Functionals and Resilience (video) (tablet)

Lecture 3: Concentration Inequalities (video) (tablet)

Lecture 4: Bounding Suprema via Concentration Inequalities (video) (tablet)

Lecture 5: Finite-Sample Analysis via Generalized KS Distance (video) (tablet)

Lecture 6: Finite-Sample Analysis via Expanding the Destination Set (video) (tablet)

Lecture 7: Truncated Moments and Ledoux-Talagrand (video) (tablet)

Lecture 8: Efficient Algorithms: Projecting onto Maximum Eigenvector (video) (tablet)

Lecture 9: Approximation Oracles and Grothendieck's Inequality (video) (tablet)

Lecture 10: Resilience Beyond Mean Estimation (video) (tablet)

Lecture 11: Resilience For Linear Regression (video) (tablet)

Lecture 12: Efficient Algorithms for Robust Linear Regression (video) (tablet)

Lecture 13: Resilience for Wasserstein Distances

Lecture 14: Wasserstein Resilience for Moment Estimation and Linear Regression

Lecture 15: Model Mis-specification in Generalized Linear Models

Lecture 16: Robust Inference via the Bootstrap

Lecture 17: Robust Inference via Partial Specification

Lecture 18: Nonparametric Regression I

Lecture 19: Nonparametric Regression II

Lecture 20: Domain Adaptation under Covariate Shift

Lecture 21: Doubly-Robust Estimators and Semi-Parametric Estimation

Lecture 22: Neural Networks and Pre-training

Lecture 23: Robustness of Neural Networks

Lecture 24: Scaling Laws for Neural Networks

Lecture 25: Partial Specification and Agnostic Clustering

Lecture 26: Agnostic Clustering via Resilience

Lecture 27: Efficient Clustering via SVD + k-means

Jerry Li taught a class related to the first 14 lectures.

Robust Learning: Information Theory and Algorithms (Jacob Steinhardt's thesis)

Concentration of Measure (lecture notes by Terence Tao)

Generalized Resilience and Robust Statistics (Zhu, Jiao, Steinhardt)

Principled Approaches to Robust Machine Learning and Beyond (Jerry Li's thesis)

Probability Bounds (John Duchi; contains exposition on Ledoux-Talagrand)

Approximating the Cut-Norm via Grothendieck's Inequality (Alon and Naor)

Better Agnostic Clustering via Relaxed Tensor Norms (Kothari and Steinhardt)

Ricci curvature of Markov chains on metric spaces (Ollivier; relation between Poincaré inequalities and Markov chain convergence)

Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope (Eric Wong and Zico Kolter)

Training Verified Learners with Learned Verifiers (Krishnamurthy Dvijotham et al.)

Semidefinite relaxations for certifying robustness to adversarial examples (Aditi Raghunathan et al.)