(Thread) How can I say no to this? My course will be made public! Stay tuned.
I was pulling your leg here. Here's my (hot? cold?) take: academics have a *moral obligation* to make as much of their material public as possible. Not just papers, but code, talks, and lectures. 1/n
95% of the work for most content is preparing it. So why not present it so the whole world can appreciate it? arXiv, Github, and video sites.
All academics should learn how to record videos in high quality, say using @OBSProject (not just Zoom). It's imperative for the future 2/n
See in particular this BIU Winter School, which I unfortunately neglected to mention before. Amazing introductory content by many experts in the field. Organized by @LindellYehuda, @bennypinkas, and @Kobbini in 2017, far ahead of the game. cyber.biu.ac.il/event/the-7th-… 4/4
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CS grad school and faculty app deadlines are coming up soon. If you are applying to the US, you should also be applying to Canada. The two are far more similar than different.
Ask me anything about Canada in this🧵, and I'll answer honestly.
A good place to start is my thread and AMA from last year. In short, Canada and US are incredibly similar in several dimensions, including culturally and geographically. We talk everything from admission requirements to money.
🧵Fields medalist June Huh shares an early math experience: a chess puzzle in the game "The 11th Hour." Story and figures from nytimes.com/live/2022/07/0….
Can you swap the positions of the black and white knights? Seems hard, right? A new perspective makes it almost trivial! 1/n
We're going to define a graph over the (irregular) chess board. First of all, let's number the squares to give them names. 2/n
The irregular shape makes the number of moves from each square limited. For example, from square 5, the only valid moves are to 1 and 7. Therefore, in the graph of valid moves, the neighbourhood of 5 would look like this: 3/n
New workshop at @icmlconf: Updatable Machine Learning (UpML 2022)!
Training models from scratch is expensive. How can we update a model post-deployment but avoid this cost? Applications to unlearning, domain shift, & more!
Ft stellar lineup of invited speakers including: Chelsea Finn (@chelseabfinn), Shafi Goldwasser, Zico Kolter (@zicokolter), Nicolas Papernot (@NicolasPapernot), & Aaron Roth (@Aaroth)
They've studied UpML in a variety of contexts, including unlearning, robustness, fairness (2/3)
Workshop is co-organized with lead organizer Ayush Sekhari (@ayush_sekhari) and Jayadev Acharya (@AcharyaJayadev), and supported by an excellent program committee (being finalized).
We look forward to seeing your best work in this emerging area! (3/3)
🧵One of the important principles in technical communication (i.e., writing a paper, giving a talk) of complex ideas is *organization*.
That is, making it clear what the major components are & how they fit together. If you do this well you are probably 90% of the way there. (1/n)
A "top down" approach is usually the tried and true method. First communicate the high-level ideas/steps, before delving into their details. This is good because it's "truncatable": at some point "down the tree" it's ok if the audience misses a step (and they know this). (2/n)
On the other hand, if this is not clear, the audience must maintain constant attention. They don't know what's important and what's not, so they can't miss a thing. And everyone gets lost in a technical talk/paper sometimes, so this is key. (3/n)
How many of your research papers do you think will be relevant a year from now? Five years from now? 100 years from now? How do you feel about your answer?
I think most works written right now will not be too relevant in a decade. And that's totally fine! Maybe it has its day in the sun, is interesting for a bit, may be useful if you're lucky, but then the community progresses. Hopefully it inspires new ideas in others along the way