diff --git a/website/docs/schedule/lectures.md b/website/docs/schedule/lectures.md
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9/7 |
**NO CLASS: LABOR DAY** | | |
9/9 | Composition and closure properties
**Reading:** [Dwork and Roth](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) 3.5 | Justin | --- | [Signups](https://docs.google.com/spreadsheets/d/1Qiq6RtBiHD6x7t-wPqAykvTDdbbBvZYSMZ9FrKUHKm4/edit?usp=sharing) Due
9/11 | What does differential privacy actually mean?
**Reading:** [Lunchtime for Differential Privacy](https://github.com/frankmcsherry/blog/blob/master/posts/2016-08-16.md) | Justin | --- |
-9/14 | Private machine learning
**Reading:** [*On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches*](https://arxiv.org/pdf/1708.08022)
**Reading:** [*Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data*](https://arxiv.org/pdf/1610.05755) | | |
-9/16 | Privately generating synthetic data
**Reading:** [*A Simple and Practical Algorithm for Differentially Private Data Release*](https://papers.nips.cc/paper/4548-a-simple-and-practical-algorithm-for-differentially-private-data-release.pdf)
**Reading:** [*Private Post-GAN Boosting*](https://arxiv.org/pdf/2007.11934) | | |
+9/14 | Private machine learning
**Reading:** [*On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches*](https://arxiv.org/pdf/1708.08022)
**Reading:** [*Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data*](https://arxiv.org/pdf/1610.05755) | Nathan/Matt T. | Saniya/Marcus |
+9/16 | Privately generating synthetic data
**Reading:** [*A Simple and Practical Algorithm for Differentially Private Data Release*](https://papers.nips.cc/paper/4548-a-simple-and-practical-algorithm-for-differentially-private-data-release.pdf)
**Reading:** [*Private Post-GAN Boosting*](https://arxiv.org/pdf/2007.11934) | Zijian/Yuchen | Deepan/Kendall |
| **Adversarial Machine Learning**
| |
9/18 | Overview and basic concepts | Justin | --- |
-9/21 | Adversarial examples
**Reading:** [*Intriguing Properties of Neural Networks*](https://arxiv.org/pdf/1312.6199.pdf)
**Reading:** [*Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples*](https://arxiv.org/pdf/1605.07277)
**See also:** [*Explaining and Harnessing Adversarial Examples*](https://arxiv.org/pdf/1412.6572) | | |
-9/23 | Data poisoning
**Reading:** [*Poisoning Attacks against Support Vector Machines*](https://arxiv.org/pdf/1206.6389)
**Reading:** [*Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks*](https://arxiv.org/pdf/1804.00792) | | |
+9/21 | Adversarial examples
**Reading:** [*Intriguing Properties of Neural Networks*](https://arxiv.org/pdf/1312.6199.pdf)
**Reading:** [*Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples*](https://arxiv.org/pdf/1605.07277)
**See also:** [*Explaining and Harnessing Adversarial Examples*](https://arxiv.org/pdf/1412.6572) | Deepan and Kendall | Keaton/Anna |
+9/23 | Data poisoning
**Reading:** [*Poisoning Attacks against Support Vector Machines*](https://arxiv.org/pdf/1206.6389)
**Reading:** [*Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks*](https://arxiv.org/pdf/1804.00792) | Grishma/Lokit | Amos/Suleman |
9/25 | Defenses and detection: challenges
**Reading:** [*Towards Evaluating the Robustness of Neural Networks*](https://arxiv.org/pdf/1608.04644.pdf)
**Reading:** [*Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods*](https://arxiv.org/pdf/1705.07263.pdf) | Justin | --- |
-9/28 | Certified defenses
**Reading:** [*Certified Defenses for Data Poisoning Attacks*](https://arxiv.org/pdf/1706.03691.pdf)
**Reading:** [*Certified Defenses against Adversarial Examples*](https://arxiv.org/pdf/1801.09344) | | |
-9/30 | Adversarial training
**Reading:** [*Towards Deep Learning Models Resistant to Adversarial Attacks*](https://arxiv.org/pdf/1706.06083.pdf)
**See also:** [*Ensemble Adversarial Training: Attacks and Defenses*](https://arxiv.org/pdf/1705.07204) | | |
+9/28 | Certified defenses
**Reading:** [*Certified Defenses for Data Poisoning Attacks*](https://arxiv.org/pdf/1706.03691.pdf)
**Reading:** [*Certified Defenses against Adversarial Examples*](https://arxiv.org/pdf/1801.09344) | Yucheng/Matt W. | Roger/Zifan |
+9/30 | Adversarial training
**Reading:** [*Towards Deep Learning Models Resistant to Adversarial Attacks*](https://arxiv.org/pdf/1706.06083.pdf)
**See also:** [*Ensemble Adversarial Training: Attacks and Defenses*](https://arxiv.org/pdf/1705.07204) | Nikhil/Scott | Grishma/Lokit |
| **Applied Cryptography**
| | |
10/2 | Overview and basic constructions
**Reading:** [Boneh and Shoup](http://toc.cryptobook.us/), 11.6, 19.4
**See also:** [Evans, Kolesnikov, and Rosulek](https://securecomputation.org/), Chapter 3 | Justin | --- |
-10/5 | Secure data collection at scale
**Reading:** [*Prio: Private, Robust, and Scalable Computation of Aggregate Statistics*](https://people.csail.mit.edu/henrycg/files/academic/papers/nsdi17prio.pdf) | | |
-10/7 | Verifiable computing
**Reading:** [*SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud*](https://arxiv.org/pdf/1706.10268) | | |
-10/9 | Side channels and implementation issues
**Reading:** [*On Significance of the Least Significant Bits For Differential Privacy*](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.366.5957&rep=rep1&type=pdf) | | |
-10/12 | Model watermarking
**Reading:** [*Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring*](https://arxiv.org/pdf/1802.04633)
**See also:** [*Protecting Intellectual Property of Deep Neural Networks with Watermarking*](https://gzs715.github.io/pubs/WATERMARK_ASIACCS18.pdf) | | | MS1 Due
+10/5 | Secure data collection at scale
**Reading:** [*Prio: Private, Robust, and Scalable Computation of Aggregate Statistics*](https://people.csail.mit.edu/henrycg/files/academic/papers/nsdi17prio.pdf) | Saniya/Marcus | Jinwoo/Mazharul |
+10/7 | Verifiable computing
**Reading:** [*SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud*](https://arxiv.org/pdf/1706.10268) | Mike | Siyang/Dan |
+10/9 | Side channels and implementation issues
**Reading:** [*On Significance of the Least Significant Bits For Differential Privacy*](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.366.5957&rep=rep1&type=pdf) | Siyang/Dan | Nathan/Matt T. |
+10/12 | Model watermarking
**Reading:** [*Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring*](https://arxiv.org/pdf/1802.04633)
**See also:** [*Protecting Intellectual Property of Deep Neural Networks with Watermarking*](https://gzs715.github.io/pubs/WATERMARK_ASIACCS18.pdf) | Amos/Suleman | Sidharth/Martin | MS1 Due
| **Algorithmic Fairness**
| | |
10/14 | Overview and basic notions
**Reading:** [Barocas, Hardt, and Narayanan](https://fairmlbook.org/index.html), Chapter 1-2
**See also:** [*50 Years of Test (Un)fairness: Lessons for Machine Learning*](https://arxiv.org/pdf/1811.10104) | Justin | --- |
-10/16 | Individual and group fairness
**Reading:** [*Fairness through Awarness*](https://arxiv.org/pdf/1104.3913)
**Reading:** [*Equality of Opportunity in Supervised Learning*](https://arxiv.org/pdf/1610.02413) | | |
-10/19 | Inherent tradeoffs
**Reading:** [*Inherent Trade-Offs in the Fair Determination of Risk Scores*](https://arxiv.org/pdf/1609.05807) | | |
+10/16 | Individual and group fairness
**Reading:** [*Fairness through Awarness*](https://arxiv.org/pdf/1104.3913)
**Reading:** [*Equality of Opportunity in Supervised Learning*](https://arxiv.org/pdf/1610.02413) | Sidharth/Martin | Vishal/Nikita |
+10/19 | Inherent tradeoffs
**Reading:** [*Inherent Trade-Offs in the Fair Determination of Risk Scores*](https://arxiv.org/pdf/1609.05807) | Shiyu/Rita | Rishabh/Aaron |
10/21 | Fairness and causality
**Reading:** [Barocas, Hardt, and Narayanan](https://fairmlbook.org/causal.html), Chapter 4 | Justin | --- |
-10/23 | Fairness in unsupervised learning
**Reading:** [*Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings*](https://arxiv.org/pdf/1607.06520)
**See also:** [*Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints*](https://arxiv.org/pdf/1707.09457) | | |
-10/26 | Testing fairness, empirically
**Reading:** [Barocas, Hardt, and Narayanan](https://fairmlbook.org/testing.html), Chapter 5 | Justin | --- |
+10/23 | Fairness in unsupervised learning
**Reading:** [*Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings*](https://arxiv.org/pdf/1607.06520)
**See also:** [*Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints*](https://arxiv.org/pdf/1707.09457) | Keaton/Anna | Shiyu/Rita |
+10/26 | Testing fairness, empirically
**Reading:** [Barocas, Hardt, and Narayanan](https://fairmlbook.org/testing.html), Chapter 5 | Rishabh/Aaron | Mike |
| **PL and Verification**
| | |
10/28 | Overview and basic notions | Justin | --- |
-10/30 | Probabilistic programming languages
**Reading:** [*Probabilistic Programming*](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/fose-icse2014.pdf) | | |
-11/2 | Verifying probabilistic programs
**Reading:** [*A Program Logic for Union Bounds*](https://arxiv.org/pdf/1602.05681)
**See also:** [*Advances and Challenges of Probabilistic Model Checking*](https://www.prismmodelchecker.org/papers/allerton10.pdf) | | |
-11/4 | Languages for differential privacy
**Reading:** [*Distance Makes the Types Grow Stronger: A Calculus for Differential Privacy*](https://www.cis.upenn.edu/~bcpierce/papers/dp.pdf)
**See also:** [*Programming Language Techniques for Differential Privacy*](https://siglog.hosting.acm.org/wp-content/uploads/2016/01/siglog_news_7.pdf) | | |
-11/6 | Verifying neural networks
**Reading:** [*AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation*](https://files.sri.inf.ethz.ch/website/papers/sp2018.pdf)
**See also:** [*DL2: Training and Querying Neural Networks with Logic*](http://proceedings.mlr.press/v97/fischer19a/fischer19a.pdf) | | | MS2 Due
+10/30 | Probabilistic programming languages
**Reading:** [*Probabilistic Programming*](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/fose-icse2014.pdf) | Vishal/Nikita | Zijian/Yuchen |
+11/2 | Verifying probabilistic programs
**Reading:** [*A Program Logic for Union Bounds*](https://arxiv.org/pdf/1602.05681)
**See also:** [*Advances and Challenges of Probabilistic Model Checking*](https://www.prismmodelchecker.org/papers/allerton10.pdf) | Jinwoo/Mazharul | Yucheng/Matt W. |
+11/4 | Languages for differential privacy
**Reading:** [*Distance Makes the Types Grow Stronger: A Calculus for Differential Privacy*](https://www.cis.upenn.edu/~bcpierce/papers/dp.pdf)
**See also:** [*Programming Language Techniques for Differential Privacy*](https://siglog.hosting.acm.org/wp-content/uploads/2016/01/siglog_news_7.pdf) | Ashish/Athena | Nikhil/Scott |
+11/6 | Verifying neural networks
**Reading:** [*AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation*](https://files.sri.inf.ethz.ch/website/papers/sp2018.pdf)
**See also:** [*DL2: Training and Querying Neural Networks with Logic*](http://proceedings.mlr.press/v97/fischer19a/fischer19a.pdf) | Roger/Zifan | Ashish/Athena | MS2 Due
| **No Lectures: Work on Projects**
| | |
12/7 | **Project Presentations** | | |
12/9 | **Project Presentations** | | |