Some CVPR 2025 new policies: 1. Submitters must review
2. Terrible reviewers will have their submissions rejected
3. Authors may not submit over 25 papers Reasonable policies, but the fact that the ML community has gotten to the point where any are required is embarrassing
@thegautamkamath
-
CVPR 2025 Implements New Submission Policies to Combat Review Quality Issues
By
–
-
SaTML Conference Call: Submit Your Secure ML Papers Now
By
–
Have a nice paper on secure and trustworthy ML? Consider sending it to SaTML! Note that the new deadline is one day after NeurIPS notifications come out, so a fantastic option for papers that NeurIPS reviewers just didn't get or appreciate.
-
Comparing ML and Theory Conference Review Workload Differences
By
–
All of the above. Contrast:
ML conf – 6 papers to review, written in a rush, often of tangential interest. Repeat 3x/yr
Theory conf – 3 papers to review *max*, generally long technical papers that couldn't be rushed, and I am an expert in every paper I review. Repeat 1-3x/yr 1/2 -
Differences Between Theory CS and ML Conferences
By
–
The difference between theory CS and ML conferences.
-
Finding the Middle Ground Between Theory and Applied DP Research
By
–
I agree that Dwork-Roth is written for a rather mathematical audience. The book you linked is really great for engineers who want to apply DP. But I think if you want to do *research* in DP, the ideal reference exists in a space between these two.
-
TPDP Workshop: Annual Free DP Community Event and Networking
By
–
For the DP community, we have a fun workshop every year, TPDP. This is a free-to-attend, high-acceptance rate venue where a large number of DP researchers go. Great opportunity to meet folks and learn about the latest work. Favorite event of the year. https://
tpdp.journalprivacyconfidentiality.org 8/n -
Making Advanced Learning Theory Research More Accessible Through Blog Posts
By
–
Beyond the basics: w/ a huge & dense literature, papers building on top of each other, it's hard to jump into research. Accessible technical material of more advanced topics is helpful. With @let4all we solicit and curate learning theory blog posts: https://
let-all.com/blog/ 4/n -
Differential Privacy: Dwork, Rothblum’s Monograph and Educational Resources
By
–
In the DP realm, Dwork and @Aaroth wrote a great monograph a decade ago (
https://
cis.upenn.edu/~aaroth/Papers
/privacybook.pdf
…). I (and others) have uploaded courses on the topic, videos and all (
https://
x.com/thegautamkamat
h/status/1306975126790037512
…). 3/n -
Importance of Open Educational Materials for Machine Learning Newcomers
By
–
One very important technical tool is good open introductory educational material: textbooks, lecture notes, videos. O/W it's very difficult for newcomers w/o connections to learn the basics. See eg the classic @shai_s_shwartz @shaibendavid5 book https://
cs.huji.ac.il/~shais/Underst
andingMachineLearning/
… 2/n