Lecture 11 (I)

Scalable private learning with PATE

Zehao Li

Lecture 11 (II)

Differentially private fair learning

Burak Varici

Lecture 12 (I)

On sampling, anonymization, and differential privacy or, k- anonymization meets differential privacy

Charlie Cook

Lecture 12 (II)

Evaluating differentially private machine learning in practice

Sharmishtha Dutta

Lecture 13

Robustness, robust training, certified defense, robust optimization, adversarial examples, black-box attacks

Lecture 14

Poisoning attacks against support vector machines

Arpan Mukherjee

Lecture 15

Manipulating machine learning: Poisoning attacks and countermeasures for regression learning

Dong Hu

Lecture 16

Explaining and harnessing adversarial examples

Vijay Sadashivaiah

Lecture 17 (I)

Practical black-box attacks against machine learning

Alex Mankowski

Lecture 17 (II)

A robust meta-algorithm for stochastic optimization

Bao Pham

Lecture 18

Mitigating unwanted biases with adversarial learning

Kara Davis

Lecture 19

Fairness, fairness measures, counterfactuals, fair representation, certified fairness, bias mitigation, fair classification