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sydneycompsci.bsky.social
🎉 Clément and Yun's paper "Local Computation Algorithms for Knapsack: impossibility results, and how to avoid them" is accepted at RANDOM 2025! Paper: arxiv.org/abs/2504.01543

🔗More about RANDOM: randomconference.com
👤
@ccanonne.github.io

#algorithms #sublinearalgorithms #distributedalgorithms
Local Computation Algorithms for Knapsack: impossibility results, and how to avoid them
Local Computation Algorithms (LCA), as introduced by Rubinfeld, Tamir, Vardi, and Xie (2011), are a type of ultra-efficient algorithms which, given access to a (large) input for a given computational ...
arxiv.org
July 3, 2025 at 2:37 AM
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