Fill out schedule with papers.
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@ -26,12 +26,13 @@ These three components are detailed below.
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### Paper presentations
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**Paper discussions** are one of the main components of this course. In groups
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of two (or very rarely three), you will present 2-3 papers on a related topic
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and lead the discussion; we will have presentations most Wednesdays and Fridays.
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Your presentation should last about **60 minutes** long, leaving the remainder
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of the time for a wrap-up discussion. Please sign up for a slot and a paper by
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**Monday, September 9**; while we will try to accommodate everyone's interests,
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we may need to adjust the selections for better balance and coverage.
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of two (or very rarely three), you will present 1-2 papers on a related topic
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and lead the discussion. We will have presentations most Wednesdays and Fridays,
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Each presentation should be about **60 minutes**, leaving the remainder of the
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time for a wrap-up discussion. Please sign up for a slot by **Monday, September
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9**; see the [calendar](schedule/lectures.md) for the topic and suggested papers
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for each slot. While we will try to accommodate everyone's interests, we may
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need to adjust the selections for better balance and coverage.
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Before every presentation, all students are expected to read the papers closely
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and understand their significance, including (a) the main problems, (b) the
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@ -1,4 +1,4 @@
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# Paper Suggestions
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# Assorted Papers
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### Differential Privacy
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- Frank McSherry and Kunal Talwar.
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@ -10,6 +10,9 @@
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- T.-H. Hubert Chan, Elaine Shi, and Dawn Song.
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[*Private and Continual Release of Statistics*](https://eprint.iacr.org/2010/076.pdf).
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ICALP 2010.
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- Ilya Mironov.
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[*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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CCS 2012.
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- Moritz Hardt, Katrina Ligett, and Frank McSherry.
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[*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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NIPS 2012.
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@ -22,44 +25,71 @@
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- Cynthia Dwork, Moni Naor, Omer Reingold, and Guy N. Rothblum.
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[*Pure Differential Privacy for Rectangle Queries via Private Partitions*](https://guyrothblum.files.wordpress.com/2017/06/dnrr15.pdf).
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ASIACRYPT 2015.
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- Martín Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang.
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[*Deep Learning with Differential Privacy*](https://arxiv.org/pdf/1607.00133).
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CCS 2016.
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- Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, and Li Zhang.
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[*On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches*](https://arxiv.org/pdf/1708.08022).
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CSF 2016.
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- Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, and Kunal Talwar.
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[*Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data*](https://arxiv.org/pdf/1610.05755).
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ICLR 2017.
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- Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson.
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[*Scalable Private Learning with PATE*](https://arxiv.org/pdf/1802.08908).
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ICLR 2018.
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- Matthew Joseph, Aaron Roth, Jonathan Ullman, and Bo Waggoner.
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[*Local Differential Privacy for Evolving Data*](https://arxiv.org/abs/1802.07128).
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NIPS 2018.
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NeurIPS 2018.
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- Albert Cheu, Adam Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev.
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[*Distributed Differential Privacy via Shuffling*](https://arxiv.org/pdf/1808.01394).
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EUROCRYPT 2019.
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- Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Abhradeep Thakurta.
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[*Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity*](https://arxiv.org/pdf/1811.12469).
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SODA 2019.
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- Jingcheng Liu and Kunal Talwar.
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[*Private Selection from Private Candidates*](https://arxiv.org/pdf/1811.07971).
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STOC 2019.
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### Adversarial Machine Learning
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### Adversarial ML
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- Battista Biggio, Blaine Nelson, and Pavel Laskov.
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[*Poisoning Attacks against Support Vector Machines*](https://arxiv.org/pdf/1206.6389).
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ICML 2012.
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- Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus.
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[*Intriguing Properties of Neural Networks*](https://arxiv.org/pdf/1312.6199.pdf).
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ICLR 2014.
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- Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy.
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[*Explaining and Harnessing Adversarial Examples*](https://arxiv.org/abs/1412.6572).
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ICLR 2015.
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- Matt Fredrikson, Somesh Jha, and Thomas Ristenpart.
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[*Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures*](https://www.cs.cmu.edu/~mfredrik/papers/fjr2015ccs.pdf).
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CCS 2015.
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- Nicholas Carlini and David Wagner.
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[*Towards Evaluating the Robustness of Neural Networks*](https://arxiv.org/pdf/1608.04644.pdf).
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S&P 2017.
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- Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song.
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[*Robust Physical-World Attacks on Deep Learning Models*](https://arxiv.org/pdf/1707.08945.pdf).
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CVPR 2018.
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- Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov.
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[*Membership Inference Attacks against Machine Learning Models*](https://arxiv.org/pdf/1610.05820).
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S&P 2017.
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- Nicholas Carlini and David Wagner.
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[*Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods*](https://arxiv.org/pdf/1705.07263.pdf).
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AISec 2017.
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- Jacob Steinhardt, Pang Wei Koh, and Percy Liang.
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[*Certified Defenses for Data Poisoning Attacks*](https://arxiv.org/pdf/1706.03691.pdf).
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NIPS 2017.
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- Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song.
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[*Robust Physical-World Attacks on Deep Learning Models*](https://arxiv.org/pdf/1707.08945.pdf).
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CVPR 2018.
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- Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu.
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[*Towards Deep Learning Models Resistant to Adversarial Attacks*](https://arxiv.org/pdf/1706.06083.pdf).
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ICLR 2018.
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- Aditi Raghunathan, Jacob Steinhardt, and Percy Liang.
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[*Certified Defenses against Adversarial Examples*](https://arxiv.org/pdf/1801.09344).
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ICLR 2018.
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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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- Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song.
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[*The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks*](https://arxiv.org/pdf/1802.08232).
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USENIX Security 2019.
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USENIX 2019.
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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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- Henry Corrigan-Gibbs and Dan Boneh.
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[*Prio: Private, Robust, and Scalable Computation of Aggregate Statistics*](https://people.csail.mit.edu/henrycg/files/academic/papers/nsdi17prio.pdf).
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NSDI 2017.
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- Zahra Ghodsi, Tianyu Gu, Siddharth Garg.
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[*SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud*](https://arxiv.org/pdf/1706.10268).
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NIPS 2017.
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- Valerie Chen, Valerio Pastro, Mariana Raykova.
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[*Secure Computation for Machine Learning With SPDZ*](https://arxiv.org/pdf/1901.00329).
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NIPS 2018.
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NeurIPS 2018.
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- Jialong Zhang, Zhongshu Gu, Jiyong Jang, Hui Wu, Marc Ph. Stoecklin, Heqing Huang, and Ian Molloy.
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[*Protecting Intellectual Property of Deep Neural Networks with Watermarking*](https://gzs715.github.io/pubs/WATERMARK_ASIACCS18.pdf).
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AsiaCCS 2018.
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- Yossi Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, and Joseph Keshet.
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[*Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring*](https://arxiv.org/pdf/1802.04633).
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USENIX 2018.
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- Wenting Zheng, Raluca Ada Popa, Joseph E. Gonzalez, Ion Stoica.
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[*Helen: Maliciously Secure Coopetitive Learning for Linear Models*](https://arxiv.org/pdf/1907.07212).
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S&P 2019.
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- Bita Darvish Rouhani, Huili Chen, and Farinaz Koushanfar.
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[*DeepSigns: A Generic Watermarking Framework for IP Protection of Deep Learning Models*](https://arxiv.org/pdf/1804.00750).
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ASPLOS 2019.
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### Algorithmic Fairness
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- Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Rich Zemel.
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- Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai.
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[*Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings*](https://arxiv.org/pdf/1607.06520).
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NIPS 2016.
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- Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang.
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[*Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints*](https://arxiv.org/pdf/1707.09457).
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EMNLP 2017.
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- Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan.
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[*Inherent Trade-Offs in the Fair Determination of Risk Scores*](https://arxiv.org/pdf/1609.05807).
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ITCS 2017.
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- Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf.
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[*Avoiding Discrimination through Causal Reasoning*](https://arxiv.org/pdf/1706.02744).
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NIPS 2017.
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- Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva.
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[*Counterfactual Fairness*](https://arxiv.org/pdf/1703.06856).
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NIPS 2017.
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- Razieh Nabi and Ilya Shpitser.
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[*Fair Inference on Outcomes*](https://arxiv.org/pdf/1705.10378).
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AAAI 2018.
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- Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, and Guy N. Rothblum.
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[*Multicalibration: Calibration for the (Computationally-Identifiable) Masses*](https://arxiv.org/pdf/1711.08513.pdf).
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ICML 2018.
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[*50 Years of Test (Un)fairness: Lessons for Machine Learning*](https://arxiv.org/pdf/1811.10104).
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FAT\* 2019.
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### Programming Languages and Verification
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### PL and Verification
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- Martín Abadi and Andrew D. Gordon.
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[*A Calculus for Cryptographic Protocols: The Spi Calculus*](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/11/ic99spi.pdf).
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Information and Computation, 1999.
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- Noah Goodman, Vikash Mansinghka, Daniel M. Roy, Keith Bonawitz, Joshua B. Tenenbaum.
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[*Church: a language for generative models*](https://arxiv.org/pdf/1206.3255).
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UAI 2008.
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- Frank McSherry.
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[*Privacy Integrated Queries*](http://citeseerx.ist.psu.edu/viewdoc/download?rep=rep1&type=pdf&doi=10.1.1.211.4503).
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SIGMOD 2009.
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- Marta Kwiatkowska, Gethin Norman, and David Parker.
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[*Advances and Challenges of Probabilistic Model Checking*](https://www.prismmodelchecker.org/papers/allerton10.pdf).
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Allerton 2010.
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- Jason Reed and Benjamin C. Pierce.
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[*Distance Makes the Types Grow Stronger: A Calculus for Differential Privacy*](https://www.cis.upenn.edu/~bcpierce/papers/dp.pdf).
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ICFP 2010.
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- Andrew Miller, Michael Hicks, Jonathan Katz, and Elaine Shi.
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[*Authenticated Data Structures, Generically*](https://www.cs.umd.edu/~mwh/papers/gpads.pdf).
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POPL 2014.
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- Andrew D. Gordon, Thomas A. Henzinger, Aditya V. Nori, and Sriram K. Rajamani.
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[*Probabilistic Programming*](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/fose-icse2014.pdf).
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ICSE 2014.
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- Gilles Barthe, Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, and Pierre-Yves Strub.
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[*Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacy*](https://arxiv.org/pdf/1407.6845.pdf).
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POPL 2015.
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- Chang Liu, Xiao Shaun Wang, Kartik Nayak, Yan Huang, and Elaine Shi.
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[*ObliVM: A Programming Framework for Secure Computation*](http://www.cs.umd.edu/~elaine/docs/oblivm.pdf).
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S&P 2015.
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- Gilles Barthe, Marco Gaboardi, Benjamin Grégoire, Justin Hsu, and Pierre-Yves Strub.
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[*A Program Logic for Union Bounds*](https://arxiv.org/pdf/1602.05681).
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ICALP 2016.
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- Christian Albert Hammerschmidt, Sicco Verwer, Qin Lin, and Radu State.
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[*Interpreting Finite Automata for Sequential Data*](https://arxiv.org/pdf/1611.07100).
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NIPS 2016.
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- Joost-Pieter Katoen.
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[*The Probabilistic Model Checking Landscape*](https://moves.rwth-aachen.de/wp-content/uploads/lics2016_tutorial_katoen.pdf).
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LICS 2016.
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- Andrew Ferraiuolo, Rui Xu, Danfeng Zhang, Andrew C. Myers, and G. Edward Suh.
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[*Verification of a Practical Hardware Security Architecture Through Static Information Flow Analysis*](http://www.cse.psu.edu/~dbz5017/pub/asplos17.pdf).
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ASPLOS 2017.
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- Frits Vaandrager.
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[*Model Learning*](https://m-cacm.acm.org/magazines/2017/2/212445-model-learning/fulltext).
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CACM 2017.
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- Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, and Martin Vechev
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[*AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation*](https://files.sri.inf.ethz.ch/website/papers/sp2018.pdf).
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S&P 2018.
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- Marc Fischer, Mislav Balunovic, Dana Drachsler-Cohen, Timon Gehr, Ce Zhang, and Martin Vechev.
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[*DL2: Training and Querying Neural Networks with Logic*](http://proceedings.mlr.press/v97/fischer19a/fischer19a.pdf).
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ICML 2019.
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# Supplemental Material
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- Cynthia Dwork and Aaron Roth.
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[*A Survey of Symbolic Methods in Computational Analysis of Cryptographic Systems*](https://hal.inria.fr/inria-00379776/document).
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- Dan Boneh and Victor Shoup.
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[*A Graduate Course in Applied Cryptography*](https://crypto.stanford.edu/~dabo/cryptobook/BonehShoup_0_4.pdf).
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- David Hand.
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[*Statistics and the Theory of Measurement*](http://www.lps.uci.edu/~johnsonk/CLASSES/MeasurementTheory/Hand1996.StatisticsAndTheTheoryOfMeasurement.pdf).
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- Judea Pearl.
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[*Causal inference in statistics: An overview*](http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf).
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- Yehuda Lindell and Benny Pinkas.
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[*Secure Multiparty Computation for Privacy-Preserving Data Mining*](https://eprint.iacr.org/2008/197.pdf).
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- CSE 291: [Language-Based Security](https://cseweb.ucsd.edu/~dstefan/cse291-winter18/) (Deian Stefan, UCSD)
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- CSE 291: [Language-Based Security](https://cseweb.ucsd.edu/~dstefan/cse291-winter18/) (Deian Stefan, UC San Diego)
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- CSE 711: [Topics in Differential Privacy](https://www.acsu.buffalo.edu/~gaboardi/teaching/CSE711-spring16.html) (Marco Gaboardi, University at Buffalo)
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- CS 800: [The Algorithmic Foundations of Data Privacy](https://www.cis.upenn.edu/~aaroth/courses/privacyF11.html) (Aaron Roth, UPenn)
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- CS 229r: [Mathematical Approaches to Data Privacy](http://people.seas.harvard.edu/~salil/diffprivcourse/spring13/) (Salil Vadhan, Harvard)
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- CS 294: [Fairness in Machine Learning](https://fairmlclass.github.io/) (Moritz Hardt, UC Berkeley)
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- CS 598: [Special Topics in Adversarial Machine Learning](http://www.crystal-boli.com/teaching.html) (Bo Li, UIUC)
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# Calendar
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# Calendar (tentative)
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Date | Topic | 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:** Keshav. [*How to Read a Paper*](https://web.stanford.edu/class/ee384m/Handouts/HowtoReadPaper.pdf). | HW1 Out
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9/6 | Basic private mechanisms <br> **Reading:** AFDP 3.2-4 |
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9/9 | Composition and closure properties <br> **Reading:** AFDP 3.5 | Signups
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9/11 | What does differential privacy actually mean? <br> **Reading:** McSherry. [Lunchtime for Differential Privacy](https://github.com/frankmcsherry/blog/blob/master/posts/2016-08-16.md) |
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9/13 | Paper presentations: Differential privacy | HW1 Due
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| <center> <h4> **Adversarial Machine Learning** </h4> </center> |
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9/16 | Overview and basic concepts | HW2 Out
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9/18 | Paper presentations: Adversarial attacks |
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9/20 | Paper presentations: ??? |
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9/23 | Adversarial training |
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9/25 | Paper presentations: Certified defenses |
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9/27 | Paper presentations: ??? | HW2 Due
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| <center> <h4> **Applied Cryptography** </h4> </center> |
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9/30 | Overview and basic constructions | HW3 Out
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10/2 | Paper presentations: Secure Multiparty Computation |
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10/4 | Paper presentations: ??? |
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10/7 | Homomorphic Encryption |
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10/9 | Paper presentations: Oblivious computing and side channels |
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10/11 | Paper presentations: ??? | HW3 Due <br> MS1 Due
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| <center> <h4> **Advanced Topic: Algorithmic Fairness** </h4> </center> |
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10/14 | Overview and basic notions |
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10/16 | Paper presentations: Individual and group fairness |
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10/18 | Paper presentations: ??? |
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10/21 | Challenges in defining fairness |
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10/23 | Paper presentations: Repairing fairness |
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10/25 | Paper presentations: ??? |
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| <center> <h4> **Advanced Topic: PL and Verification** </h4> </center> |
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10/28 | Overview and basic notions |
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10/30 | Paper presentations: Probabilistic programming languages |
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11/1 | Paper presentations: ??? |
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11/4 | Programming languages for differential privacy |
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11/6 | Paper presentations: Verifying probabilistic programs |
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11/8 | Paper presentations: ??? | MS2 Due
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| <center> <h4> **No Lectures: Work on Projects** </h4> </center> |
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12/11 (TBD) | Project Presentations |
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Date | Topic | Presenters | 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 | HW1 Out
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9/6 | Basic private mechanisms <br> **Reading:** AFDP 3.2-4 | JH |
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9/9 | Composition and closure properties <br> **Reading:** AFDP 3.5 | JH | Paper Signups
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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) | | HW1 Due
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| <center> <h4> **Adversarial Machine Learning** </h4> </center> | |
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9/16 | Overview and basic concepts | JH | HW2 Out
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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/abs/1412.6572) <br> **Reading:** [*Robust Physical-World Attacks on Deep Learning Models*](https://arxiv.org/pdf/1707.08945.pdf) | |
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9/20 | Data poisoning <br> **Reading:** [*Poisoning Attacks against Support Vector Machines*](https://arxiv.org/pdf/1206.6389) | |
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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) | |
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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) | | HW2 Due
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| <center> <h4> **Applied Cryptography** </h4> </center> | |
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9/30 | Overview and basic constructions | JH | HW3 Out
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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) | |
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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) | |
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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) | |
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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) | | HW3 Due <br> MS1 Due
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| <center> <h4> **Advanced Topic: Algorithmic Fairness** </h4> </center> | |
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10/14 | Overview and basic notions <br> **Reading:** Chapter 2 from [Barocas, Hardt, and Narayanan](https://fairmlbook.org/demographic.html) | 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) | |
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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) | |
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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) | JH |
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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> **Reading:** [*Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints*](https://arxiv.org/pdf/1707.09457) | |
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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) | |
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| <center> <h4> **Advanced Topic: 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) | |
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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) | |
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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 |
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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> **Reading:** [*DL2: Training and Querying Neural Networks with Logic*](http://proceedings.mlr.press/v97/fischer19a/fischer19a.pdf) | |
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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
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| <center> <h4> **No Lectures: Work on Projects** </h4> </center> | |
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12/11 (TBD) | Project Presentations | |
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Reference in New Issue