Clément Canonne
@ccanonne
Senior Lecturer #USydCompSci at the University of Sydney. Postdocs IBM Research and Stanford; PhD at Columbia. Converts ☕ into puns: sometimes theorems. He/him.
Given the title of this review (opinion piece), feels pretty important to read the disclosure statement on that one.
This is beautiful! This should be better known! And the proof is... so simple, short, and neat. Read it—it's worth your time! 📝 www.stat.yale.edu/~yw562/teach... [Lecture notes by Yihong Wu on this result and proof (PDF)] /end
So... does the inequality holds for arbitrary (not just product) distributions with square Hellinger? ALMOST! 🤯 This is surprisingly non-trivial, and this is surprisingly true, and this is due to T.S. Jayram (2009): it's true, if you put a weird constant in front of the RHS! 5/
This is much better because of the squares there (this saves a quadratic factor in the dimension n, often crucial). But what if the distributions are *not* product distributions? Can we say anything? 🤔 We know (a suitable version) holds for *KL Divergence* (that's the chain rule!), after all! 3/
"Well, of course not, duh." You could have all marginal distances equal to 0, yet LHS close to one (check it out!). It is true though for *product* distributions as a simple consequence of the triangle inequality (also holds for TV distance). But for products, we have the much stronger version: 2/
As promised yesterday, a short thread on an inequality I believe deserves to be much better-known: a chain-rule-type for Hellinger distance! You have two probability distributions p,q over product space Ω₁×...×Ωₙ. Can you relate their distance H(p,q) to the distances between their marginals? 1/
It took us a while to work out the details, but our group finally formulated a detailed and consistent theory of quantum Pokémon! Also, please don't observe Psiduck, it stresses it out and it *will* collapse.
So many good points in this post by @nsaphra.bsky.social: only quoting a couple, to encourage you to read the others. "My colleagues and students adopt the writing quirks they read throughout the day, and their own writing becomes more like an LLM’s." nsaphra.net/post/uncanny/
David Pollard has a new draft, "Probability tools, tricks, and miracles" (last updated June 2006). Lots of good things in there, from a quick skim! And, if nothing else, worth reading for the quality of the writing and the exposition choices and notes. www.stat.yale.edu/~pollard/Boo...
I'm (finally) reading the Australian Research Council (ARC)'s National Competitive Grants Program (NCGP) Policy Review, and... I'm no finance wizard, but if I had an investment scheme yielding 232% benefit, I'd consider investing slightly more in it? www.arc.gov.au/news-and-pub...
www.unsw.edu.au/science/abou... (Includes "climate modelling and prediction", by the way!)
"If I have seen further than others, it's because I have been sitting on the chair of GIANTS"
Mathematicians! Book a function! Reserve a group! Looking for a nice... space?
Saw the latest blog post by Bill Gasarch on @lance.fortnow.com's blog, and I need to get it out of my system: this is not "the obvious thing." This isn't insightful. This is, from a senior and respected member of our community, plainly disappointing. A ouija board w/ a varnish of misunderstood tech.
An aperiodic reminder that our community (theoretical computer science), while amazing, really sucks at naming things.
Workshop in the Club de la Chasse in Paris, a very old building moonlighting as a museum dedicated to... hunting? Seeing this place, it changes you.
Rummaging through my old lecture notes at my parents' house... found this exercise sheet. Good times.
Pros: a dynamic and collegial department (School), expanding, with excellent researchers and wonderful colleagues, in an incredible city, in a beautiful country! Cons: some birds look funny
Kenny's results improve on those recently obtained by Doosti, Sweke, and Wadhwa (arxiv.org/abs/2604.05962), who formalized this distributed quantum property testing setting (analogue of the classical setting Jayadev Acharya, Himanshu Tyagi and I introduced a few years ago) Truly impressed by Kenny!
These are meant to complement the second part of my course's lecture notes on randomized graph algorithms: ccanonne.github.io/files/compx2... (I'm not just subjecting students to my handwriting with nothing to save them)
The Karger–Klein–Tarjan algorithm (MST in expected linear time) is incredibly beautiful. A joy to teach and share (at least for me; at least one happy person in the classroom, I guess.) To compensate for how beautiful that algo is, I made handwritten slides: ccanonne.github.io/files/compx2...
The Curious Incident of The Balcony Flower Being Eaten In the Night-Time