B9145: Reliable Statistical Learning
Course Schedule (subject to change)
| Topics | Lecture Notes | Readings |
| Sep 8 | course overview | slides, annotated slides, bdd-diff-notes | Ch. 2, Wainwright (2019) |
| Sep 15 | generalization bounds | notes | Ch. 4.2-4.3, 5, Wainwright (2019) |
| Sep 22 | M-estimation, SGD | notes | Ch. 5, van der Vaart (1998),
Ch. 2-3, Duchi (2016) |
| Sep 29 | information theoretic lower bounds | notes | Ch. 15, Wainwright (2019), Yu (1997),
Stoch. opt.: Ch. 5, Duchi (2016), Agarwal et al. (2012) |
| Oct 6 | distributional robustness | notes-1, notes-2 | Blanchet and Murthy (2019) |
| Oct 13 | distributional robustness | notes | Ch. 6.3, Shapiro et al. (2014), Blanchet et al. (2019),
Duchi and Namkoong (2019) |
| Oct 27 | adversarial training | notes | Wong and Kolter (2018), Raghunathan et al. (2018) |
| Nov 10 | ethics in ML, fairness definitions | slides | Gebru and Denton (2020), Chouldechova (2017), Kleinberg et al. (2017) |
| Nov 17 | Ludwig Schmidt: benchmarking | slides | Recht et al. (2019) |
| Nov 24 | subpopulations & DRO | slides | Corbett-Davies and Goel (2018),
Ch. 6.3, Shapiro et al. (2014), Duchi and Namkoong (2020) |
| Dec 1 | causality | slides, annotated slides | Imbens and Rubin (2015) |
| Dec 8 | causality | slides, annotated slides | Yadlowsky et al. (2018)
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Email the instructor if you want access to lecture recordings.
References
Alexander Shapiro, Darinka Dentcheva, and Andrzej
Ruszczynski. Lectures on Stochastic Programming: Modeling and
Theory. Second Edition. SIAM and Mathematical Programming Society, 2014.
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