#Random2026
LightNews LightNews
ccanonne.github.io
Now accepted at #RANDOM2026: congratulations, Nimitt! @nimittnim.com
ccanonne.github.io Clément Canonne @ccanonne.github.io · May 29
New preprint online: on multiple trade-offs b/w communication, sample, and randomness complexities for the fundamental task of Gaussian mean testing in a distributed setting.

Led by the brilliant Nimitt (undergrad at IIT Gandhinagar) during a remote internship last year!

📝 arxiv.org/abs/2605.29426
Distributed Gaussian Mean Testing under Communication Constraints: messages, samples, and coins
We revisit the problem of Gaussian mean testing in a distributed, communication constrained setting, where each of $n$ users independently observes samples from an unknown $d$-dimensional spherical Ga...
arxiv.org
June 25, 2026 at 8:33 AM
ccanonne.github.io
Our paper on Entropy Equivalence Testing was just accepted to #RANDOM2026. Congratulations to Joy (Qiping) Yang and Yash Pote, who led the charge! arxiv.org/abs/2605.23225
Entropy Equivalence Testing
We introduce the problem of \emph{entropy equivalence testing} for probability distributions, a relaxation of the well-studied closeness testing problem, where the distribution testing algorithm is no...
arxiv.org
June 25, 2026 at 8:32 AM
inkkka.bsky.social
LibrosVsPantallas #Lectura2026 #MetroLife #BookLover #LeerEsVivir #PapelNoPantalla #Random2026 #ÚltimoLector #Bookstagram #ReadingIsCool
June 24, 2026 at 9:01 PM
sydneycompsci.bsky.social
🎉 Clément Canonne and Joy Qiping Yang's paper "Entropy Equivalence Testing" has been accepted at #RANDOM2026!

🔗 RANDOM'26: randomconference.com/random-2026-...
📝 Paper: arxiv.org/abs/2605.23225

Joint work with Yash Pote and Jonathan Scarlett (NUS)

#algorithms #sublinearalgorithms
Title: Entropy Equivalence Testing Abstract: We introduce the problem of entropy equivalence testing for probability distributions, a relaxation of the well-studied closeness testing problem, where the distribution testing algorithm is now only required to distinguish, given samples from two unknown distributions p,q and a parameter ε∈(0,1/2], between p=q and |H​(p)−H​(q)|⩾ε (where H denotes the Shannon entropy). We provide a time- and sample-efficient algorithm for this task, showing that the optimal sample complexity for this task can be significantly lower than that of closeness testing. As an application, we leverage this result to provide the first non-trivial testing algorithm for (standard) closeness of low-degree Bayesian networks, which significantly improves on either the sample or time complexity of a baseline based on full learning.
June 30, 2026 at 8:30 AM
sydneycompsci.bsky.social
🎉 Clément Canonne and Nimitt (IIT Gandhinagar)'s paper "Distributed Gaussian Mean Testing under Communication Constraints: messages, samples, and coins" has been accepted at #RANDOM2026!

🔗 RANDOM'26: randomconference.com/random-2026-...
📝 Paper: arxiv.org/abs/2605.29426
Title: Distributed Gaussian Mean Testing under Communication Constraints: messages, samples, and coins Abstract: We revisit the problem of Gaussian mean testing in a distributed, communication constrained setting, where each of n users independently observes samples from an unknown d-dimensional spherical Gaussian distribution 𝒢​(𝝁,𝕀d), and can communicate up to ℓ bits to a central referee. The referee’s goal is then to distinguish between cases (i) ‖𝝁‖2=0 versus (ii) ‖𝝁‖2≥ε. This problem has been considered in the private- and public-coin settings, when each user holds exactly one sample, or more generally when each holds exactly m samples. In this work, we significantly generalize the question in three directions: when the users only share a small number s of random bits, when each user holds a different number of samples mk, and when each user can send a different number of bits ℓk to the referee.
June 30, 2026 at 8:50 AM