# Weekly Schedule

Week-to-week schedule and papers covered are tentative, and may change by the start of the quarter

## Introduction and taxonomy of distribution shifts

- Apr 3
- Introduction
**Lecture**

- Overview of the course
- Distribution shifts in the real world
- A taxonomy of distribution shifts and how they arise

- Apr 5
- Covariate and label shifts
**Lecture**+**Discussion**

- What is a covariate shift?
- Handling covariate shift under distribuitonal overlap.
- Shortcut Learning in Deep Neural Networks

- Apr 10
- Covariate and label shifts 2
**Discussion**

## Domain adaptation theory

- Apr 12
- Domain adaptation
**Lecture**

- When can we provably learn under distribution shift?
- Defining generalization bounds under distribution shift.
- Adversarial approaches to neural domain adaptation.

- Apr 17
- Domain adaptation 2
**Discussion**

## Neural and representation-based methods

- Apr 19
- Neural domain adaptation
**Lecture**

- Provable guarantees from representational indistinguishability
- Self-training based domain adaptation
- Self-supervision based domain adaptation

- Apr 24
- Neural domain adaptation 2
**Discussion**

- Apr 26
- Empirical phenomena in robust machine learning
**Lecture**

- How do different robustness interventions fare in practice?
- Can (data augmentation / unlabeled data / bigger models) help?

- May 1
- Empirical phenomena in robust machine learning 2 +
**Project**(**Progress report due**) **Discussion**

- Empirical phenomena in robust machine learning 2 +

## Robustness and domain generalization

- May 3
- Connections to causality
**Lecture**

- Distribution shifts as arising from causal interventions.
- Existing connections between causality and robustness.
- Robustness and invariance as tools for causal inference.

- May 8
- Connections to causality 2
**Discussion**

- May 10
- Minimax methods
**Lecture**

- Robustness as a minimax game between nature and the model.
- Tractable families of worst-case distributions and duality.
- Pitfalls and pessimism from worst-case bounds.

- May 15
- Minimax methods 2
**Discussion**

## Adversarial robustness

- May 17
- Adversarial examples
**Lecture**

- Defining and motivating adversarial examples.
- Heuristic defenses and their pitfalls
- Provable defenses.

- May 22
- Adversarial examples 2
**Discussion**

- May 24
- Data poisoning
**Lecture**

- What is data poisoning?
- Robust statistics and high-dimensional mean estimation
- Convex optimization under data poisoning

- May 29
- Memorial day
**Holiday**

- May 31
- Data Poisoning 2
**Discussion**

- June 5
- NO CLASS - Report due
**Project**

- June 7
- Short project presentations
**Project**