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Signup and deadlines.

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Justin Hsu 1 year ago
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  1. 8
      website/docs/schedule/deadlines.md
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      website/docs/schedule/lectures.md

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website/docs/schedule/deadlines.md

@ -5,9 +5,9 @@ The first key date is **September 9**. By this date, you should:
come up with **1-2 sentences** describing your initial direction. This is not
a firm commitment---you can change your topic as you learn more.
The signup sheet is [here](https://docs.google.com/spreadsheets/d/1hSbRy0mo3PjlozN0Ph1JkP5JwlRG8y7ukuCdorofncA/edit?usp=sharing).
The signup sheet is [here](https://docs.google.com/spreadsheets/d/1Qiq6RtBiHD6x7t-wPqAykvTDdbbBvZYSMZ9FrKUHKm4/edit?usp=sharing).
## Project Deadlines
- Milestone 1: **October 11**
- Milestone 2: **November 8**
- Final writeup and presentation: **December 11** (TBD)
- Milestone 1: **October 12**
- Milestone 2: **November 6**
- Final writeup: **December 11**

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website/docs/schedule/lectures.md

@ -6,7 +6,7 @@
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 | --- |
9/4 | Basic private mechanisms <br> **Reading:** [Dwork and Roth](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) 3.2-4 | Justin | --- |
9/7 | <center> **NO CLASS: LABOR DAY** </center> | | |
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
9/9 | Composition and closure properties <br> **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? <br> **Reading:** [Lunchtime for Differential Privacy](https://github.com/frankmcsherry/blog/blob/master/posts/2016-08-16.md) | Justin | --- |
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) | --- | --- |
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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