justhsu
/
cs763
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Tweak.

This commit is contained in:
Justin Hsu 2019-10-02 12:08:21 -05:00
parent d4f90b5ae6
commit 49dd372b6d
1 changed files with 2 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
website/docs/schedule/lectures.md
View File

@ -4,8 +4,8 @@
:----:|-------|:----------:|:-----------:|:-----:
| <center> <h4> **Differential Privacy** </h4> </center> | | |
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 | --- |
9/6 | Basic private mechanisms <br> **Reading:** [AFDP](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) 3.2-4 | JH | --- |
9/9 | Composition and closure properties <br> **Reading:** [AFDP](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) 3.5 | JH | --- | [Signups](https://docs.google.com/spreadsheets/d/1hSbRy0mo3PjlozN0Ph1JkP5JwlRG8y7ukuCdorofncA/edit?usp=sharing) Due
9/6 | Basic private mechanisms <br> **Reading:** [Dwork and Roth](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) 3.2-4 | JH | --- |
9/9 | Composition and closure properties <br> **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? <br> **Reading:** [Lunchtime for Differential Privacy](https://github.com/frankmcsherry/blog/blob/master/posts/2016-08-16.md) | JH | --- |
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) | Robert/Shengwen | Zach/Jialu |
| <center> <h4> **Adversarial Machine Learning** </h4> </center> | |