From a433994eb26a813670354c7bf2a900a015486b12 Mon Sep 17 00:00:00 2001 From: Justin Hsu Date: Fri, 14 Aug 2020 16:57:21 -0500 Subject: [PATCH] Update schedule for Fall 2020. --- website/docs/index.md | 2 +- website/docs/resources/readings.md | 7 +++- website/docs/schedule/lectures.md | 65 +++++++++++++++--------------- 3 files changed, 40 insertions(+), 34 deletions(-) diff --git a/website/docs/index.md b/website/docs/index.md index ef47894..2709ca4 100644 --- a/website/docs/index.md +++ b/website/docs/index.md @@ -25,7 +25,7 @@ are no lectures scheduled in this period, I will be available to meet as needed. Please use the mailing list if you want to contact the whole course: -- +- All registered students should be on this list. If you are not registered but would like to follow along, please let me know and I will add you. diff --git a/website/docs/resources/readings.md b/website/docs/resources/readings.md index 38a5334..fae19af 100644 --- a/website/docs/resources/readings.md +++ b/website/docs/resources/readings.md @@ -101,7 +101,7 @@ USENIX 2019. - Vitaly Feldman. [*Does Learning Require Memorization? A Short Tale about a Long Tail*](https://arxiv.org/pdf/1906.05271). - arXiv 2019. + STOC 2020. ### Applied Cryptography - Benjamin Braun, Ariel J. Feldman, Zuocheng Ren, Srinath Setty, Andrew J. Blumberg, and Michael Walfish. @@ -261,10 +261,15 @@ - Abhinav Verma, Hoang M. Le, Yisong Yue, and Swarat Chaudhuri. [*Imitation-Projected Programmatic Reinforcement Learning*](https://arxiv.org/pdf/1907.05431). NeurIPS 2019. +- Kenneth L. McMillan + [*Bayesian Interpolants as Explanations for Neural Inferences*](https://arxiv.org/abs/2004.04198). + arXiv. # Supplemental Material - Cynthia Dwork and Aaron Roth. [*Algorithmic Foundations of Data Privacy*](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf). +- Solon Barocas, Moritz Hardt, and Arvind Narayanan. + [*Fairness and Machine Learning: Limitations and Opportunities*](https://fairmlbook.org/index.html). - Gilles Barthe, Marco Gaboardi, Justin Hsu, and Benjamin C. Pierce. [*Programming Language Techniques for Differential Privacy*](https://siglog.hosting.acm.org/wp-content/uploads/2016/01/siglog_news_7.pdf). - Michael Walfish and Andrew J. Blumberg. diff --git a/website/docs/schedule/lectures.md b/website/docs/schedule/lectures.md index eebfa1a..4a498b7 100644 --- a/website/docs/schedule/lectures.md +++ b/website/docs/schedule/lectures.md @@ -3,39 +3,40 @@ Date | Topic | Presenters | Summarizers | Notes :----:|-------|:----------:|:-----------:|:-----: |

**Differential Privacy**

| | | -9/4 | [Course welcome](../resources/slides/lecture-welcome.html)
**Reading:** [*How to Read a Paper*](https://web.stanford.edu/class/ee384m/Handouts/HowtoReadPaper.pdf) | JH | --- | -9/6 | Basic private mechanisms
**Reading:** [Dwork and Roth](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) 3.2-4 | JH | --- | -9/9 | Composition and closure properties
**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 -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) | JH | --- | -9/13 | Differentially 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) | Robert/Shengwen | Zach/Jialu | +9/2 | [Course welcome](../resources/slides/lecture-welcome.html)
**Reading:** [*How to Read a Paper*](https://web.stanford.edu/class/ee384m/Handouts/HowtoReadPaper.pdf) | Justin | --- | +9/4 | Basic private mechanisms
**Reading:** [Dwork and Roth](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) 3.2-4 | Justin | --- | +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 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 | Differentially 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:** [*Private Post-GAN Boosting*](https://arxiv.org/abs/2007.11934)
**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) | --- | --- | |

**Adversarial Machine Learning**

| | -9/16 | Overview and basic concepts | JH | --- | -9/18 | Adversarial examples
**Reading:** [*Intriguing Properties of Neural Networks*](https://arxiv.org/pdf/1312.6199.pdf)
**Reading:** [*Explaining and Harnessing Adversarial Examples*](https://arxiv.org/pdf/1412.6572) | JH | Robert/Shengwen | -9/20 | 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) | Somya/Zi | Miru/Pierre | -9/23 | 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) | JH | --- | -9/25 | 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) | Joseph/Nils | Siddhant/Goutham | -9/27 | 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) | Siddhant/Goutham | Somya/Zi | +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:** [*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) | --- | --- | +9/25 | 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) | --- | --- | |

**Applied Cryptography**

| | | -9/30 | Overview and basic constructions
**Reading:** [Boneh and Shoup](https://crypto.stanford.edu/~dabo/cryptobook/BonehShoup_0_4.pdf), 11.6, 19.4
**See also:** [Evans, Kolesnikov, and Rosulek](https://securecomputation.org/), Chapter 3 | JH | --- | -10/2 | SMC for machine learning
**Reading:** [*Helen: Maliciously Secure Coopetitive Learning for Linear Models*](https://arxiv.org/pdf/1907.07212)
**See also:** [*Secure Computation for Machine Learning With SPDZ*](https://arxiv.org/pdf/1901.00329) | Varun/Vibhor/Adarsh | --- | -10/4 | 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) | Abhirav/Rajan | --- | -10/7 | Verifiable computing
**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
**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
**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) | Noor/Shashank | Joseph/Nils| MS1 Due +10/2 | Overview and basic constructions
**Reading:** [Boneh and Shoup](https://crypto.stanford.edu/~dabo/cryptobook/BonehShoup_0_4.pdf), 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 |

**Algorithmic Fairness**

| | | -10/14 | Overview and basic notions
**Reading:** [Barocas, Hardt, and Narayanan](https://fairmlbook.org/index.html), Chapter 1-2 | JH | --- | -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) | JH | Jack/Jack | -10/18 | Inherent tradeoffs
**Reading:** [*Inherent Trade-Offs in the Fair Determination of Risk Scores*](https://arxiv.org/pdf/1609.05807) | Bobby | --- | -10/21 | Defining fairness: challenges
**Reading:** [*50 Years of Test (Un)fairness: Lessons for Machine Learning*](https://arxiv.org/pdf/1811.10104)
**Reading:** [Barocas, Hardt, and Narayanan](https://fairmlbook.org/causal.html), Chapter 4 | JH | Bobby | -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) | Zach/Jialu | Noor/Shashank | -10/25 | Beyond observational measures
**Reading:** [*Avoiding Discrimination through Causal Reasoning*](https://arxiv.org/pdf/1706.02744)
**See also:** [*Counterfactual Fairness*](https://arxiv.org/pdf/1703.06856) | Nat/Geetika | Varun/Vibhor/Adarsh | +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) | --- | --- | +10/21 | 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/causal.html), Chapter 5 | Justin | --- | |

**PL and Verification**

| | | -10/28 | Overview and basic notions | JH | --- | -10/30 | Probabilistic programming languages
**Reading:** [*Probabilistic Programming*](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/fose-icse2014.pdf) | Miru/Pierre | Nat/Geetika | -11/1 | Automata learning and interpretability
**Reading:** [*Model Learning*](https://m-cacm.acm.org/magazines/2017/2/212445-model-learning/fulltext)
**Reading:** [*Interpreting Finite Automata for Sequential Data*](https://arxiv.org/pdf/1611.07100) | Jack/Jack | Abhirav/Rajan | -11/4 | Programming 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) | JH | --- | -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) | JH | --- | -11/8 | 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) | JH | | MS2 Due - |

**No Lectures: Work on Projects**

| | | -12/9 | Project Presentations 1
- Nils, Joseph, Abhirav
- Robert, Noor, Shashank
- Jack L., Geetika
- Zi | | | -12/11 | Project Presentations 2
- Vibhor, Varun, Adarsh
- Siddhant, Goutham, Somya
- Nat, Zach, Jialu
- Miru, Pierre, Jack S.
- Shengwen, Rajan, Bobby | | | Projects Due +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 + |

**No Lectures: Work on Projects**

| | | +12/7 |
**Project Presentations**
| | | +12/9 |
**Project Presentations**
| | | +12/11 |
**PROJECTS DUE**
| | | Projects Due