cs763/website/docs/schedule/lectures.md

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: AFDP 3.2-4	JH	-
9/9	Composition and closure properties Reading: AFDP 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
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 Reading: Robust Physical-World Attacks on Deep Learning Models
9/20	Data poisoning Reading: Poisoning Attacks against Support Vector Machines
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
9/27	Adversarial training Reading: Towards Deep Learning Models Resistant to Adversarial Attacks
Applied Cryptography
9/30	Overview and basic constructions	JH	-
10/2	SMC for machine learning Reading: Secure Computation for Machine Learning With SPDZ Reading: Helen: Maliciously Secure Coopetitive Learning for Linear Models
10/4	Secure data collection at scale Reading: Prio: Private, Robust, and Scalable Computation of Aggregate Statistics
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
10/11	Model watermarking Reading: Protecting Intellectual Property of Deep Neural Networks with Watermarking Reading: Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring			MS1 Due
Algorithmic Fairness
10/14	Overview and basic notions Reading: Chapter 2 from Barocas, Hardt, and Narayanan	JH	-
10/16	Individual and group fairness Reading: Fairness through Awarness Reading: Equality of Opportunity in Supervised Learning
10/18	Inherent tradeoffs Reading: Inherent Trade-Offs in the Fair Determination of Risk Scores
10/21	Defining fairness: challenges Reading: 50 Years of Test (Un)fairness: Lessons for Machine Learning	JH	-
10/23	Fairness in unsupervised learning Reading: Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings Reading: Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
10/25	Beyond observational measures Reading: Avoiding Discrimination through Causal Reasoning Reading: Counterfactual Fairness
PL and Verification
10/28	Overview and basic notions	JH	-
10/30	Probabilistic programming languages Reading: Probabilistic Programming
11/1	Automata learning and interpretability Reading: Model Learning Reading: Interpreting Finite Automata for Sequential Data
11/4	Programming languages for differential privacy Reading: Programming Language Techniques for Differential Privacy	JH	-
11/6	Verifying neural networks Reading: AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation Reading: DL2: Training and Querying Neural Networks with Logic
11/8	Verifying probabilistic programs Reading: Advances and Challenges of Probabilistic Model Checking Reading: A Program Logic for Union Bounds			MS2 Due
No Lectures: Work on Projects
12/11 (TBD)	Project Presentations