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Date | Topic | Presenters | Summarizers | Notes
:----:|-------|:----------:|:-----------:|:-----:
| <center> <h4> **Differential Privacy** </h4> </center> | | |
9/4 | [Course welcome](../resources/slides/lecture-welcome.html) <br> **Reading:** [*How to Read a Paper*](https://web.stanford.edu/class/ee384m/Handouts/HowtoReadPaper.pdf) | JH | - |
9/6 | Basic private mechanisms <br> **Reading:** [AFDP](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) 3.2-4 | JH | - |
9/9 | Composition and closure properties <br> **Reading:** [AFDP](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) 3.5 | JH | - | [Signups](https://docs.google.com/spreadsheets/d/1hSbRy0mo3PjlozN0Ph1JkP5JwlRG8y7ukuCdorofncA/edit?usp=sharing) Due
9/11 | What does differential privacy actually mean? <br> **Reading:** [Lunchtime for Differential Privacy](https://github.com/frankmcsherry/blog/blob/master/posts/2016-08-16.md) | JH | - |
9/13 | Differentially private machine learning <br> **Reading:** [*On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches*](https://arxiv.org/pdf/1708.08022) <br> **Reading:** [*Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data*](https://arxiv.org/pdf/1610.05755) | | |
9/4 | [Course welcome](../resources/slides/lecture-welcome.html) <br> **Reading:** [*How to Read a Paper*](https://web.stanford.edu/class/ee384m/Handouts/HowtoReadPaper.pdf) | JH | --- |
9/6 | Basic private mechanisms <br> **Reading:** [AFDP](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) 3.2-4 | JH | --- |
9/9 | Composition and closure properties <br> **Reading:** [AFDP](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) 3.5 | JH | --- | [Signups](https://docs.google.com/spreadsheets/d/1hSbRy0mo3PjlozN0Ph1JkP5JwlRG8y7ukuCdorofncA/edit?usp=sharing) Due
9/11 | What does differential privacy actually mean? <br> **Reading:** [Lunchtime for Differential Privacy](https://github.com/frankmcsherry/blog/blob/master/posts/2016-08-16.md) | JH | --- |
9/13 | Differentially private machine learning <br> **Reading:** [*On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches*](https://arxiv.org/pdf/1708.08022) <br> **Reading:** [*Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data*](https://arxiv.org/pdf/1610.05755) | Robert/Shengwen | Zach/Jialu |
| <center> <h4> **Adversarial Machine Learning** </h4> </center> | |
9/16 | Overview and basic concepts | JH | - |
9/18 | Adversarial examples <br> **Reading:** [*Intriguing Properties of Neural Networks*](https://arxiv.org/pdf/1312.6199.pdf) <br> **Reading:** [*Explaining and Harnessing Adversarial Examples*](https://arxiv.org/abs/1412.6572) <br> **Reading:** [*Robust Physical-World Attacks on Deep Learning Models*](https://arxiv.org/pdf/1707.08945.pdf) | | |
9/20 | Data poisoning <br> **Reading:** [*Poisoning Attacks against Support Vector Machines*](https://arxiv.org/pdf/1206.6389) <br> **Reading:** [*Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks*](https://arxiv.org/pdf/1804.00792) | | |
9/23 | Defenses and detection: challenges <br> **Reading:** [*Towards Evaluating the Robustness of Neural Networks*](https://arxiv.org/pdf/1608.04644.pdf) <br> **Reading:** [*Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods*](https://arxiv.org/pdf/1705.07263.pdf) | JH | - |
9/25 | Certified defenses <br> **Reading:** [*Certified Defenses for Data Poisoning Attacks*](https://arxiv.org/pdf/1706.03691.pdf) <br> **Reading:** [*Certified Defenses against Adversarial Examples*](https://arxiv.org/pdf/1801.09344) | | |
9/27 | Adversarial training <br> **Reading:** [*Towards Deep Learning Models Resistant to Adversarial Attacks*](https://arxiv.org/pdf/1706.06083.pdf) <br> **Reading:** [*Ensemble Adversarial Training: Attacks and Defenses*](https://arxiv.org/pdf/1705.07204) | | |
9/16 | Overview and basic concepts | JH | --- |
9/18 | Adversarial examples <br> **Reading:** [*Intriguing Properties of Neural Networks*](https://arxiv.org/pdf/1312.6199.pdf) <br> **Reading:** [*Explaining and Harnessing Adversarial Examples*](https://arxiv.org/abs/1412.6572) <br> **Reading:** [*Robust Physical-World Attacks on Deep Learning Models*](https://arxiv.org/pdf/1707.08945.pdf) | JH | Robert/Shengwen |
9/20 | Data poisoning <br> **Reading:** [*Poisoning Attacks against Support Vector Machines*](https://arxiv.org/pdf/1206.6389) <br> **Reading:** [*Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks*](https://arxiv.org/pdf/1804.00792) | Somya/Zi | Miru/Pierre |
9/23 | Defenses and detection: challenges <br> **Reading:** [*Towards Evaluating the Robustness of Neural Networks*](https://arxiv.org/pdf/1608.04644.pdf) <br> **Reading:** [*Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods*](https://arxiv.org/pdf/1705.07263.pdf) | JH | --- |
9/25 | Certified defenses <br> **Reading:** [*Certified Defenses for Data Poisoning Attacks*](https://arxiv.org/pdf/1706.03691.pdf) <br> **Reading:** [*Certified Defenses against Adversarial Examples*](https://arxiv.org/pdf/1801.09344) | Joseph/Nils | Siddhant/Goutham |
9/27 | Adversarial training <br> **Reading:** [*Towards Deep Learning Models Resistant to Adversarial Attacks*](https://arxiv.org/pdf/1706.06083.pdf) <br> **Reading:** [*Ensemble Adversarial Training: Attacks and Defenses*](https://arxiv.org/pdf/1705.07204) | Siddhant/Goutham | Somya/Zi |
| <center> <h4> **Applied Cryptography** </h4> </center> | | |
9/30 | Overview and basic constructions | JH | - |
10/2 | SMC for machine learning <br> **Reading:** [*Secure Computation for Machine Learning With SPDZ*](https://arxiv.org/pdf/1901.00329) <br> **Reading:** [*Helen: Maliciously Secure Coopetitive Learning for Linear Models*](https://arxiv.org/pdf/1907.07212) | | |
10/4 | Secure data collection at scale <br> **Reading:** [*Prio: Private, Robust, and Scalable Computation of Aggregate Statistics*](https://people.csail.mit.edu/henrycg/files/academic/papers/nsdi17prio.pdf) <br> **Reading:** [*RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response*](https://arxiv.org/pdf/1407.6981.pdf) | | |
10/7 | Verifiable computing <br> **Reading:** [*SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud*](https://arxiv.org/pdf/1706.10268) | JH | - |
10/9 | Side channels and implementation issues <br> **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) | | |
10/11 | Model watermarking <br> **Reading:** [*Protecting Intellectual Property of Deep Neural Networks with Watermarking*](https://gzs715.github.io/pubs/WATERMARK_ASIACCS18.pdf) <br> **Reading:** [*Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring*](https://arxiv.org/pdf/1802.04633) | | | MS1 Due
9/30 | Overview and basic constructions | JH | --- |
10/2 | SMC for machine learning <br> **Reading:** [*Secure Computation for Machine Learning With SPDZ*](https://arxiv.org/pdf/1901.00329) <br> **Reading:** [*Helen: Maliciously Secure Coopetitive Learning for Linear Models*](https://arxiv.org/pdf/1907.07212) | Varun/Vibhor | --- |
10/4 | Secure data collection at scale <br> **Reading:** [*Prio: Private, Robust, and Scalable Computation of Aggregate Statistics*](https://people.csail.mit.edu/henrycg/files/academic/papers/nsdi17prio.pdf) <br> **Reading:** [*RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response*](https://arxiv.org/pdf/1407.6981.pdf) | Abhirav/Rajan | --- |
10/7 | Verifiable computing <br> **Reading:** [*SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud*](https://arxiv.org/pdf/1706.10268) | JH | --- |
10/9 | Side channels and implementation issues <br> **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) | JH | --- |
10/11 | Model watermarking <br> **Reading:** [*Protecting Intellectual Property of Deep Neural Networks with Watermarking*](https://gzs715.github.io/pubs/WATERMARK_ASIACCS18.pdf) <br> **Reading:** [*Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring*](https://arxiv.org/pdf/1802.04633) | Noor/Shashank | Joseph/Nils | MS1 Due
| <center> <h4> **Algorithmic Fairness** </h4> </center> | | |
10/14 | Overview and basic notions <br> **Reading:** Chapter 2 from [Barocas, Hardt, and Narayanan](https://fairmlbook.org/demographic.html) | JH | - |
10/16 | Individual and group fairness <br> **Reading:** [*Fairness through Awarness*](https://arxiv.org/pdf/1104.3913) <br> **Reading:** [*Equality of Opportunity in Supervised Learning*](https://arxiv.org/pdf/1610.02413) | | |
10/18 | Inherent tradeoffs <br> **Reading:** [*Inherent Trade-Offs in the Fair Determination of Risk Scores*](https://arxiv.org/pdf/1609.05807) | | |
10/21 | Defining fairness: challenges <br> **Reading:** [*50 Years of Test (Un)fairness: Lessons for Machine Learning*](https://arxiv.org/pdf/1811.10104) | JH | - |
10/23 | Fairness in unsupervised learning <br> **Reading:** [*Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings*](https://arxiv.org/pdf/1607.06520) <br> **Reading:** [*Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints*](https://arxiv.org/pdf/1707.09457) | | |
10/25 | Beyond observational measures <br> **Reading:** [*Avoiding Discrimination through Causal Reasoning*](https://arxiv.org/pdf/1706.02744) <br> **Reading:** [*Counterfactual Fairness*](https://arxiv.org/pdf/1703.06856) | | |
10/14 | Overview and basic notions <br> **Reading:** Chapter 2 from [Barocas, Hardt, and Narayanan](https://fairmlbook.org/demographic.html) | JH | --- |
10/16 | Individual and group fairness <br> **Reading:** [*Fairness through Awarness*](https://arxiv.org/pdf/1104.3913) <br> **Reading:** [*Equality of Opportunity in Supervised Learning*](https://arxiv.org/pdf/1610.02413) | Aditya/Adarsh | Jack/Jack |
10/18 | Inherent tradeoffs <br> **Reading:** [*Inherent Trade-Offs in the Fair Determination of Risk Scores*](https://arxiv.org/pdf/1609.05807) | JH | Aditya/Adarsh |
10/21 | Defining fairness: challenges <br> **Reading:** [*50 Years of Test (Un)fairness: Lessons for Machine Learning*](https://arxiv.org/pdf/1811.10104) | JH | --- |
10/23 | Fairness in unsupervised learning <br> **Reading:** [*Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings*](https://arxiv.org/pdf/1607.06520) <br> **Reading:** [*Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints*](https://arxiv.org/pdf/1707.09457) | Zach/Jialu | --- |
10/25 | Beyond observational measures <br> **Reading:** [*Avoiding Discrimination through Causal Reasoning*](https://arxiv.org/pdf/1706.02744) <br> **Reading:** [*Counterfactual Fairness*](https://arxiv.org/pdf/1703.06856) | Nat | Varun/Vibhor |
| <center> <h4> **PL and Verification** </h4> </center> | | |
10/28 | Overview and basic notions | JH | - |
10/30 | Probabilistic programming languages <br> **Reading:** [*Probabilistic Programming*](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/fose-icse2014.pdf) | | |
11/1 | Automata learning and interpretability <br> **Reading:** [*Model Learning*](https://m-cacm.acm.org/magazines/2017/2/212445-model-learning/fulltext) <br> **Reading:** [*Interpreting Finite Automata for Sequential Data*](https://arxiv.org/pdf/1611.07100) | | |
11/4 | Programming languages for differential privacy <br> **Reading:** [*Programming Language Techniques for Differential Privacy*](https://dl.acm.org/citation.cfm?id=2893591&dl=ACM&coll=DL) | JH | - |
11/6 | Verifying neural networks <br> **Reading:** [*AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation*](https://files.sri.inf.ethz.ch/website/papers/sp2018.pdf) <br> **Reading:** [*DL2: Training and Querying Neural Networks with Logic*](http://proceedings.mlr.press/v97/fischer19a/fischer19a.pdf) | | |
11/8 | Verifying probabilistic programs <br> **Reading:** [*Advances and Challenges of Probabilistic Model Checking*](https://www.prismmodelchecker.org/papers/allerton10.pdf) <br> **Reading:** [*A Program Logic for Union Bounds*](https://arxiv.org/pdf/1602.05681) | | | MS2 Due
| <center> <h4> **No Lectures: Work on Projects** </h4> </center> | | |
10/28 | Overview and basic notions | JH | --- |
10/30 | Probabilistic programming languages <br> **Reading:** [*Probabilistic Programming*](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/fose-icse2014.pdf) | Miru/Pierre | Nat |
11/1 | Automata learning and interpretability <br> **Reading:** [*Model Learning*](https://m-cacm.acm.org/magazines/2017/2/212445-model-learning/fulltext) <br> **Reading:** [*Interpreting Finite Automata for Sequential Data*](https://arxiv.org/pdf/1611.07100) | Jack/Jack | Abhirav/Rajan |
11/4 | Programming languages for differential privacy <br> **Reading:** [*Programming Language Techniques for Differential Privacy*](https://dl.acm.org/citation.cfm?id=2893591&dl=ACM&coll=DL) | JH | --- |
11/6 | Verifying neural networks <br> **Reading:** [*AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation*](https://files.sri.inf.ethz.ch/website/papers/sp2018.pdf) <br> **Reading:** [*DL2: Training and Querying Neural Networks with Logic*](http://proceedings.mlr.press/v97/fischer19a/fischer19a.pdf) | JH | --- |
11/8 | Verifying probabilistic programs <br> **Reading:** [*Advances and Challenges of Probabilistic Model Checking*](https://www.prismmodelchecker.org/papers/allerton10.pdf) <br> **Reading:** [*A Program Logic for Union Bounds*](https://arxiv.org/pdf/1602.05681) | JH | --- | MS2 Due
| <center> <h4> **No&nbsp;Lectures:&nbsp;Work&nbsp;on&nbsp;Projects** </h4> </center> | | |
12/11 (TBD) | Project Presentations | | |

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