Update schedule for Fall 2020.
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Justin Hsu
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@ -25,7 +25,7 @@ are no lectures scheduled in this period, I will be available to meet as needed.
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Please use the mailing list if you want to contact the whole course:
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- <mailto:compsci763-1-f20@lists.wisc.edu>
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- <mailto:compsci763-1-f20@g-groups.wisc.edu>
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All registered students should be on this list. If you are not registered but
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would like to follow along, please let me know and I will add you.
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@ -101,7 +101,7 @@
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USENIX 2019.
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- Vitaly Feldman.
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[*Does Learning Require Memorization? A Short Tale about a Long Tail*](https://arxiv.org/pdf/1906.05271).
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arXiv 2019.
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STOC 2020.
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### Applied Cryptography
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- Benjamin Braun, Ariel J. Feldman, Zuocheng Ren, Srinath Setty, Andrew J. Blumberg, and Michael Walfish.
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@ -261,10 +261,15 @@
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- Abhinav Verma, Hoang M. Le, Yisong Yue, and Swarat Chaudhuri.
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[*Imitation-Projected Programmatic Reinforcement Learning*](https://arxiv.org/pdf/1907.05431).
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NeurIPS 2019.
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- Kenneth L. McMillan
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[*Bayesian Interpolants as Explanations for Neural Inferences*](https://arxiv.org/abs/2004.04198).
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arXiv.
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# Supplemental Material
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- Cynthia Dwork and Aaron Roth.
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[*Algorithmic Foundations of Data Privacy*](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf).
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- Solon Barocas, Moritz Hardt, and Arvind Narayanan.
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[*Fairness and Machine Learning: Limitations and Opportunities*](https://fairmlbook.org/index.html).
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- Gilles Barthe, Marco Gaboardi, Justin Hsu, and Benjamin C. Pierce.
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[*Programming Language Techniques for Differential Privacy*](https://siglog.hosting.acm.org/wp-content/uploads/2016/01/siglog_news_7.pdf).
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- Michael Walfish and Andrew J. Blumberg.
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@ -3,39 +3,40 @@
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Date | Topic | Presenters | Summarizers | Notes
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:----:|-------|:----------:|:-----------:|:-----:
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| <center> <h4> **Differential Privacy** </h4> </center> | | |
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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 | --- |
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9/6 | Basic private mechanisms <br> **Reading:** [Dwork and Roth](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) 3.2-4 | JH | --- |
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9/9 | Composition and closure properties <br> **Reading:** [Dwork and Roth](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) 3.5 | JH | --- | [Signups](https://docs.google.com/spreadsheets/d/1hSbRy0mo3PjlozN0Ph1JkP5JwlRG8y7ukuCdorofncA/edit?usp=sharing) Due
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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 | --- |
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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 |
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9/2 | [Course welcome](../resources/slides/lecture-welcome.html) <br> **Reading:** [*How to Read a Paper*](https://web.stanford.edu/class/ee384m/Handouts/HowtoReadPaper.pdf) | Justin | --- |
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9/4 | Basic private mechanisms <br> **Reading:** [Dwork and Roth](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) 3.2-4 | Justin | --- |
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9/7 | <center> **NO CLASS: LABOR DAY** </center> | | |
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9/9 | Composition and closure properties <br> **Reading:** [Dwork and Roth](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) 3.5 | Justin | --- | Signups Due
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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) | Justin | --- |
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9/14 | 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) | --- | --- |
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9/16 | Privately generating synthetic data <br> **Reading:** [*Private Post-GAN Boosting*](https://arxiv.org/abs/2007.11934) <br> **See also:** [*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) | --- | --- |
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| <center> <h4> **Adversarial Machine Learning** </h4> </center> | |
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9/16 | Overview and basic concepts | JH | --- |
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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/pdf/1412.6572) | JH | Robert/Shengwen |
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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 |
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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 | --- |
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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 |
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9/27 | Adversarial training <br> **Reading:** [*Towards Deep Learning Models Resistant to Adversarial Attacks*](https://arxiv.org/pdf/1706.06083.pdf) <br> **See also:** [*Ensemble Adversarial Training: Attacks and Defenses*](https://arxiv.org/pdf/1705.07204) | Siddhant/Goutham | Somya/Zi |
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9/18 | Overview and basic concepts | Justin | --- |
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9/21 | 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/pdf/1412.6572) | --- | --- |
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9/23 | 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) | --- | --- |
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9/25 | 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) | Justin | --- |
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9/28 | 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) | --- | --- |
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9/30 | Adversarial training <br> **Reading:** [*Towards Deep Learning Models Resistant to Adversarial Attacks*](https://arxiv.org/pdf/1706.06083.pdf) <br> **See also:** [*Ensemble Adversarial Training: Attacks and Defenses*](https://arxiv.org/pdf/1705.07204) | --- | --- |
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| <center> <h4> **Applied Cryptography** </h4> </center> | | |
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9/30 | Overview and basic constructions <br> **Reading:** [Boneh and Shoup](https://crypto.stanford.edu/~dabo/cryptobook/BonehShoup_0_4.pdf), 11.6, 19.4 <br> **See also:** [Evans, Kolesnikov, and Rosulek](https://securecomputation.org/), Chapter 3 | JH | --- |
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10/2 | SMC for machine learning <br> **Reading:** [*Helen: Maliciously Secure Coopetitive Learning for Linear Models*](https://arxiv.org/pdf/1907.07212) <br> **See also:** [*Secure Computation for Machine Learning With SPDZ*](https://arxiv.org/pdf/1901.00329) | Varun/Vibhor/Adarsh | --- |
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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) | Abhirav/Rajan | --- |
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10/7 | Verifiable computing <br> **Reading:** [*SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud*](https://arxiv.org/pdf/1706.10268) | JH | --- |
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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 | --- |
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10/11 | Model watermarking <br> **Reading:** [*Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring*](https://arxiv.org/pdf/1802.04633) <br> **See also:** [*Protecting Intellectual Property of Deep Neural Networks with Watermarking*](https://gzs715.github.io/pubs/WATERMARK_ASIACCS18.pdf) | Noor/Shashank | Joseph/Nils| MS1 Due
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10/2 | Overview and basic constructions <br> **Reading:** [Boneh and Shoup](https://crypto.stanford.edu/~dabo/cryptobook/BonehShoup_0_4.pdf), 11.6, 19.4 <br> **See also:** [Evans, Kolesnikov, and Rosulek](https://securecomputation.org/), Chapter 3 | Justin | --- |
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10/5 | 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) | --- | --- |
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10/7 | Verifiable computing <br> **Reading:** [*SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud*](https://arxiv.org/pdf/1706.10268) | --- | --- |
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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) | --- | --- |
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10/12 | Model watermarking <br> **Reading:** [*Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring*](https://arxiv.org/pdf/1802.04633) <br> **See also:** [*Protecting Intellectual Property of Deep Neural Networks with Watermarking*](https://gzs715.github.io/pubs/WATERMARK_ASIACCS18.pdf) | --- | --- | MS1 Due
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| <center> <h4> **Algorithmic Fairness** </h4> </center> | | |
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10/14 | Overview and basic notions <br> **Reading:** [Barocas, Hardt, and Narayanan](https://fairmlbook.org/index.html), Chapter 1-2 | JH | --- |
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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) | JH | Jack/Jack |
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10/18 | Inherent tradeoffs <br> **Reading:** [*Inherent Trade-Offs in the Fair Determination of Risk Scores*](https://arxiv.org/pdf/1609.05807) | Bobby | --- |
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10/21 | Defining fairness: challenges <br> **Reading:** [*50 Years of Test (Un)fairness: Lessons for Machine Learning*](https://arxiv.org/pdf/1811.10104) <br> **Reading:** [Barocas, Hardt, and Narayanan](https://fairmlbook.org/causal.html), Chapter 4 | JH | Bobby |
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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> **See also:** [*Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints*](https://arxiv.org/pdf/1707.09457) | Zach/Jialu | Noor/Shashank |
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10/25 | Beyond observational measures <br> **Reading:** [*Avoiding Discrimination through Causal Reasoning*](https://arxiv.org/pdf/1706.02744) <br> **See also:** [*Counterfactual Fairness*](https://arxiv.org/pdf/1703.06856) | Nat/Geetika | Varun/Vibhor/Adarsh |
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10/14 | Overview and basic notions <br> **Reading:** [Barocas, Hardt, and Narayanan](https://fairmlbook.org/index.html), Chapter 1-2 <br> **See also:** [*50 Years of Test (Un)fairness: Lessons for Machine Learning*](https://arxiv.org/pdf/1811.10104) | Justin | --- |
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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) | --- | --- |
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10/19 | Inherent tradeoffs <br> **Reading:** [*Inherent Trade-Offs in the Fair Determination of Risk Scores*](https://arxiv.org/pdf/1609.05807) | --- | --- |
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10/21 | Fairness and causality <br> **Reading:** [Barocas, Hardt, and Narayanan](https://fairmlbook.org/causal.html), Chapter 4 | Justin | --- |
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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> **See also:** [*Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints*](https://arxiv.org/pdf/1707.09457) | --- | --- |
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10/26 | Testing fairness, empirically <br> **Reading:** [Barocas, Hardt, and Narayanan](https://fairmlbook.org/causal.html), Chapter 5 | Justin | --- |
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| <center> <h4> **PL and Verification** </h4> </center> | | |
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10/28 | Overview and basic notions | JH | --- |
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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/Geetika |
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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 |
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11/4 | Programming languages for differential privacy <br> **Reading:** [*Distance Makes the Types Grow Stronger: A Calculus for Differential Privacy*](https://www.cis.upenn.edu/~bcpierce/papers/dp.pdf) <br> **See also:** [*Programming Language Techniques for Differential Privacy*](https://siglog.hosting.acm.org/wp-content/uploads/2016/01/siglog_news_7.pdf) | JH | --- |
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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> **See also:** [*DL2: Training and Querying Neural Networks with Logic*](http://proceedings.mlr.press/v97/fischer19a/fischer19a.pdf) | JH | --- |
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11/8 | Verifying probabilistic programs <br> **Reading:** [*A Program Logic for Union Bounds*](https://arxiv.org/pdf/1602.05681) <br> **See also:** [*Advances and Challenges of Probabilistic Model Checking*](https://www.prismmodelchecker.org/papers/allerton10.pdf) | JH | | MS2 Due
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| <center> <h4> **No Lectures: Work on Projects** </h4> </center> | | |
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12/9 | Project Presentations 1 <br> - Nils, Joseph, Abhirav <br> - Robert, Noor, Shashank <br> - Jack L., Geetika <br> - Zi | | |
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12/11 | Project Presentations 2 <br> - Vibhor, Varun, Adarsh <br> - Siddhant, Goutham, Somya <br> - Nat, Zach, Jialu <br> - Miru, Pierre, Jack S. <br> - Shengwen, Rajan, Bobby | | | Projects Due
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10/28 | Overview and basic notions | Justin | --- |
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10/30 | Probabilistic programming languages <br> **Reading:** [*Probabilistic Programming*](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/fose-icse2014.pdf) | --- | --- |
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11/2 | Verifying probabilistic programs <br> **Reading:** [*A Program Logic for Union Bounds*](https://arxiv.org/pdf/1602.05681) <br> **See also:** [*Advances and Challenges of Probabilistic Model Checking*](https://www.prismmodelchecker.org/papers/allerton10.pdf) | --- | --- |
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11/4 | Languages for differential privacy <br> **Reading:** [*Distance Makes the Types Grow Stronger: A Calculus for Differential Privacy*](https://www.cis.upenn.edu/~bcpierce/papers/dp.pdf) <br> **See also:** [*Programming Language Techniques for Differential Privacy*](https://siglog.hosting.acm.org/wp-content/uploads/2016/01/siglog_news_7.pdf) | --- | --- |
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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> **See also:** [*DL2: Training and Querying Neural Networks with Logic*](http://proceedings.mlr.press/v97/fischer19a/fischer19a.pdf) | --- | --- | MS2 Due
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| <center> <h4> **No Lectures: Work on Projects** </h4> </center> | | |
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12/7 | <center> **Project Presentations** </center> | | |
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12/9 | <center> **Project Presentations** </center> | | |
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12/11 | <center> **PROJECTS DUE** </center> | | | Projects Due
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