From 70210d76587e738c3c380f2301736810314a33d8 Mon Sep 17 00:00:00 2001 From: Justin Hsu Date: Mon, 24 Aug 2020 18:29:45 -0500 Subject: [PATCH] Adjust readings. --- website/docs/schedule/lectures.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/website/docs/schedule/lectures.md b/website/docs/schedule/lectures.md index 1bd9e53..40e5489 100644 --- a/website/docs/schedule/lectures.md +++ b/website/docs/schedule/lectures.md @@ -8,11 +8,11 @@ 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 | 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) | --- | --- | +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/abs/2007.11934) | --- | --- | |

**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:** [*Explaining and Harnessing Adversarial Examples*](https://arxiv.org/pdf/1412.6572) | --- | --- | +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/abs/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) | --- | --- | 9/25 | 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) | --- | --- |