From cfd3c84c4fc44f53905f8361f7f539474d4f5fed Mon Sep 17 00:00:00 2001 From: Justin Hsu Date: Thu, 29 Aug 2019 11:04:49 -0500 Subject: [PATCH] Remove HWs. --- website/docs/schedule/lectures.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/website/docs/schedule/lectures.md b/website/docs/schedule/lectures.md index 018d937..941b733 100644 --- a/website/docs/schedule/lectures.md +++ b/website/docs/schedule/lectures.md @@ -3,25 +3,25 @@ Date | Topic | Presenters | 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 | HW1 Out +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:** AFDP 3.2-4 | JH | 9/9 | Composition and closure properties
**Reading:** AFDP 3.5 | JH | Paper Signups 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) | | HW1 Due +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) | | |

**Adversarial Machine Learning**

| | -9/16 | Overview and basic concepts | JH | HW2 Out +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/abs/1412.6572)
**Reading:** [*Robust Physical-World Attacks on Deep Learning Models*](https://arxiv.org/pdf/1707.08945.pdf) | | 9/20 | Data poisoning
**Reading:** [*Poisoning Attacks against Support Vector Machines*](https://arxiv.org/pdf/1206.6389) | | 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) | | -9/27 | Adversarial training
**Reading:** [*Towards Deep Learning Models Resistant to Adversarial Attacks*](https://arxiv.org/pdf/1706.06083.pdf) | | HW2 Due +9/27 | Adversarial training
**Reading:** [*Towards Deep Learning Models Resistant to Adversarial Attacks*](https://arxiv.org/pdf/1706.06083.pdf) | | |

**Applied Cryptography**

| | -9/30 | Overview and basic constructions | JH | HW3 Out +9/30 | Overview and basic constructions | JH | 10/2 | SMC for machine learning
**Reading:** [*Secure Computation for Machine Learning With SPDZ*](https://arxiv.org/pdf/1901.00329)
**Reading:** [*Helen: Maliciously Secure Coopetitive Learning for Linear Models*](https://arxiv.org/pdf/1907.07212) | | 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) | | 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) | | -10/11 | Model watermarking
**Reading:** [*Protecting Intellectual Property of Deep Neural Networks with Watermarking*](https://gzs715.github.io/pubs/WATERMARK_ASIACCS18.pdf)
**Reading:** [*Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring*](https://arxiv.org/pdf/1802.04633) | | HW3 Due
MS1 Due +10/11 | Model watermarking
**Reading:** [*Protecting Intellectual Property of Deep Neural Networks with Watermarking*](https://gzs715.github.io/pubs/WATERMARK_ASIACCS18.pdf)
**Reading:** [*Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring*](https://arxiv.org/pdf/1802.04633) | | MS1 Due |

**Advanced Topic: Algorithmic Fairness**

| | 10/14 | Overview and basic notions
**Reading:** Chapter 2 from [Barocas, Hardt, and Narayanan](https://fairmlbook.org/demographic.html) | 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) | |