Calendar (tentative)

Date	Topic	Presenters	Summarizers	Notes
	Differential Privacy
9/4	Course welcome Reading: How to Read a Paper	JH	---
9/6	Basic private mechanisms Reading: Dwork and Roth 3.2-4	JH	---
9/9	Composition and closure properties Reading: Dwork and Roth 3.5	JH	---	Signups Due
9/11	What does differential privacy actually mean? Reading: Lunchtime for Differential Privacy	JH	---
9/13	Differentially 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	Robert/Shengwen	Zach/Jialu
	Adversarial Machine Learning
9/16	Overview and basic concepts	JH	---
9/18	Adversarial examples Reading: Intriguing Properties of Neural Networks Reading: Explaining and Harnessing Adversarial Examples	JH	Robert/Shengwen
9/20	Data poisoning Reading: Poisoning Attacks against Support Vector Machines Reading: Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks	Somya/Zi	Miru/Pierre
9/23	Defenses and detection: challenges Reading: Towards Evaluating the Robustness of Neural Networks Reading: Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods	JH	---
9/25	Certified defenses Reading: Certified Defenses for Data Poisoning Attacks Reading: Certified Defenses against Adversarial Examples	Joseph/Nils	Siddhant/Goutham
9/27	Adversarial training Reading: Towards Deep Learning Models Resistant to Adversarial Attacks See also: Ensemble Adversarial Training: Attacks and Defenses	Siddhant/Goutham	Somya/Zi
	Applied Cryptography
9/30	Overview and basic constructions Reading: Boneh and Shoup, 11.6, 19.4 See also: Evans, Kolesnikov, and Rosulek, Chapter 3	JH	---
10/2	SMC for machine learning Reading: Helen: Maliciously Secure Coopetitive Learning for Linear Models See also: Secure Computation for Machine Learning With SPDZ	Varun/Vibhor/Adarsh	---
10/4	Secure data collection at scale Reading: Prio: Private, Robust, and Scalable Computation of Aggregate Statistics	Abhirav/Rajan	---
10/7	Verifiable computing Reading: SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud	JH	---
10/9	Side channels and implementation issues Reading: On Significance of the Least Significant Bits For Differential Privacy	JH	---
10/11	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	Noor/Shashank	Joseph/Nils	MS1 Due
	Algorithmic Fairness
10/14	Overview and basic notions Reading: Barocas, Hardt, and Narayanan, Chapter 1-2	JH	---
10/16	Individual and group fairness Reading: Fairness through Awarness Reading: Equality of Opportunity in Supervised Learning	JH	Jack/Jack
10/18	Inherent tradeoffs Reading: Inherent Trade-Offs in the Fair Determination of Risk Scores	Bobby	---
10/21	Defining fairness: challenges Reading: 50 Years of Test (Un)fairness: Lessons for Machine Learning Reading: Barocas, Hardt, and Narayanan, Chapter 4	JH	Bobby
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	Zach/Jialu	Noor/Shashank
10/25	Beyond observational measures Reading: Avoiding Discrimination through Causal Reasoning See also: Counterfactual Fairness	Nat/Geetika	Varun/Vibhor/Adarsh
	PL and Verification
10/28	Overview and basic notions	JH	---
10/30	Probabilistic programming languages Reading: Probabilistic Programming	Miru/Pierre	Nat/Geetika
11/1	Automata learning and interpretability Reading: Model Learning Reading: Interpreting Finite Automata for Sequential Data	Jack/Jack	Abhirav/Rajan
11/4	Programming languages for differential privacy Reading: Distance Makes the Types Grow Stronger: A Calculus for Differential Privacy See also: Programming Language Techniques for Differential Privacy	JH	---
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	JH	---
11/8	Verifying probabilistic programs Reading: A Program Logic for Union Bounds See also: Advances and Challenges of Probabilistic Model Checking	JH		MS2 Due
	No Lectures: Work on Projects
12/9	Project Presentations 1 - Nils, Joseph, Abhirav - Robert, Noor, Shashank - Jack L., Geetika - Zi
12/11	Project Presentations 2 - Vibhor, Varun, Adarsh - Siddhant, Goutham, Somya - Nat, Zach, Jialu - Miru, Pierre, Jack S. - Shengwen, Rajan, Bobby			Projects Due