cs763/website/docs/schedule/lectures.md

Calendar (tentative)

Date	Topic	Presenters	Summarizers	Notes
Differential Privacy
9/2	Course welcome Reading: How to Read a Paper	Justin	---	[slides]
9/4	Basic private mechanisms Reading: Dwork and Roth 3.2-4	Justin	---
9/7	NO CLASS: LABOR DAY
9/9	Composition and closure properties Reading: Dwork and Roth 3.5	Justin	---	Signups Due
9/11	What does differential privacy actually mean? Reading: Lunchtime for Differential Privacy	Justin	---
9/14	Private machine learning Reading: On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches Reading: Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data	Nathan/Matt T.	Saniya/Marcus
9/16	Privately generating synthetic data Reading: A Simple and Practical Algorithm for Differentially Private Data Release Reading: Private Post-GAN Boosting	Zijian/Yuchen	Deepan/Kendall
Adversarial Machine Learning
9/18	Overview and basic concepts	Justin	---
9/21	Adversarial examples Reading: Intriguing Properties of Neural Networks Reading: Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples See also: Explaining and Harnessing Adversarial Examples	Deepan and Kendall	Keaton/Anna
9/23	Data poisoning Reading: Poisoning Attacks against Support Vector Machines Reading: Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks	Grishma/Lokit	Amos/Suleman
9/25	Defenses and detection: challenges Reading: Towards Evaluating the Robustness of Neural Networks Reading: Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods	Justin	---
9/28	Certified defenses Reading: Certified Defenses for Data Poisoning Attacks Reading: Certified Defenses against Adversarial Examples	Yucheng/Matt W.	Roger/Zifan
9/30	Adversarial training Reading: Towards Deep Learning Models Resistant to Adversarial Attacks See also: Ensemble Adversarial Training: Attacks and Defenses	Nikhil/Scott	Grishma/Lokit
Applied Cryptography
10/2	Overview and basic constructions Reading: Boneh and Shoup, 11.6, 19.4 See also: Evans, Kolesnikov, and Rosulek, Chapter 3	Justin	---
10/5	Secure data collection at scale Reading: Prio: Private, Robust, and Scalable Computation of Aggregate Statistics	Saniya/Marcus	Jinwoo/Mazharul
10/7	Verifiable computing Reading: SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud	Mike	Siyang/Dan
10/9	Side channels and implementation issues Reading: On Significance of the Least Significant Bits For Differential Privacy	Siyang/Dan	Nathan/Matt T.
10/12	Model watermarking Reading: Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring See also: Protecting Intellectual Property of Deep Neural Networks with Watermarking	Amos/Suleman	Sidharth/Martin	MS1 Due
Algorithmic Fairness
10/14	Overview and basic notions Reading: Barocas, Hardt, and Narayanan, Chapter 1-2 See also: 50 Years of Test (Un)fairness: Lessons for Machine Learning	Justin	---
10/16	Individual and group fairness Reading: Fairness through Awarness Reading: Equality of Opportunity in Supervised Learning	Sidharth/Martin	Vishal/Nikita
10/19	Inherent tradeoffs Reading: Inherent Trade-Offs in the Fair Determination of Risk Scores	Shiyu/Rita	Rishabh/Aaron
10/21	Fairness and causality Reading: Barocas, Hardt, and Narayanan, Chapter 4	Justin	---
10/23	Fairness in unsupervised learning Reading: Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings See also: Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints	Keaton/Anna	Shiyu/Rita
10/26	Testing fairness, empirically Reading: Barocas, Hardt, and Narayanan, Chapter 5	Rishabh/Aaron	Mike
PL and Verification
10/28	Overview and basic notions	Justin	---
10/30	Probabilistic programming languages Reading: Probabilistic Programming	Vishal/Nikita	Zijian/Yuchen
11/2	Verifying probabilistic programs Reading: A Program Logic for Union Bounds See also: Advances and Challenges of Probabilistic Model Checking	Jinwoo/Mazharul	Yucheng/Matt W.
11/4	Languages for differential privacy Reading: Distance Makes the Types Grow Stronger: A Calculus for Differential Privacy See also: Programming Language Techniques for Differential Privacy	Ashish/Athena	Nikhil/Scott
11/6	Verifying neural networks Reading: AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation See also: DL2: Training and Querying Neural Networks with Logic	Roger/Zifan	Ashish/Athena	MS2 Due
No Lectures: Work on Projects
12/7	Project Presentations
12/9	Project Presentations
12/11	PROJECTS DUE			Projects Due