Registration is now open for this workshop! website: sites.google.com/view/newcast... registration: webstore.ncl.ac.uk/conferences-...

Sam Power@spmontecarlo.bsky.social · 5mo ago

graphic design remains my passion sites.google.com/view/newcast...

Guest post by John Abowd, @aloni-bologna.bsky.social, Cynthia Dwork, Jae June Lee, @jsarathy.bsky.social, @adamsmith.xyz and Salil Vadhan on the US govt's latest move to ban "noise injection" (at the core of all meaningful privacy protections) from all official statistics: scottaaronson.blog?p=9902

An American privacy emergency: Guest post from Cynthia Dwork et al.

Scott’s foreword: Cynthia Dwork is Gordon McKay Professor of Computer Science at Harvard, and a pioneer in the fields of differential privacy and algorithmic fairness. On my recent travels to…

scottaaronson.blog

If you meet the eligibility requirements for the LOTUS Program and are interested in working with me, feel free to reach out. www.jst.go.jp/program/indi...

Open call for applications | LOTUS Programme

This page provides the information regarding the open call for applications for FY2025. Applications only be accepted from Japanese organizations.

jst.go.jp

[1/4] Our paper “Characterization of Exponential Families of Lumpable Stochastic Matrices” (with Shun Watanabe) has been accepted for publication in Information Geometry. Preprint: arxiv.org/abs/2412.08400

Our paper is now available on #arXiv: arxiv.org/abs/2508.02637

Instance-Optimal Uniformity Testing and Tracking

In the uniformity testing task, an algorithm is provided with samples from an unknown probability distribution over a (known) finite domain, and must decide whether it is the uniform distribution, or,...

arxiv.org

Clément Canonne@ccanonne.github.io · last yr.

Pretty stoked to announce that I won't be presenting my paper at #FOCS2025: but one of my talented coauthors will have to come here 🌏 to do so! "Instance-Optimal Uniformity Testing and Tracking," with Guy Blanc (Stanford) and Erik Waingarten (UPenn). (arXiv coming soon!)

new work on computing distances between stochastic processes ***based on sample paths only***! we can now: - learn distances between Markov chains - extract "encoder-decoder" pairs for representation learning - with sample- and computational-complexity guarantees read on for some quick details.. 1/n

A new blog post with intuitions behind continuous-time Markov chains, a building block of diffusion language models, like @inceptionlabs.bsky.social's Mercury and Gemini Diffusion. This post touches on different ways of looking at Markov chains, connections to point processes, and more.

Discrete Diffusion: Continuous-Time Markov Chains

A tutorial explaining some key intuitions behind continuous time Markov chains for machine learners interested in discrete diffusion models: alternative representations, connections to point processes...

inference.vc

Published today in JMLR: our joint work with Geoffrey Wolfer (Waseda University) @gwolfer.bsky.social "Variance-Aware Estimation of Kernel Mean Embedding". jmlr.org/papers/v26/2...

Our joint paper with Geoffrey Wolfer @gwolfer.bsky.social "Variance-Aware Estimation of the Kernel Mean Embedding" accepted for publication in the Journal of Machine Learning Research 🥳 arxiv.org/abs/2210.06672

Variance-Aware Estimation of Kernel Mean Embedding

An important feature of kernel mean embeddings (KME) is that the rate of convergence of the empirical KME to the true distribution KME can be bounded independently of the dimension of the space, prope...

arxiv.org

Traveling to Copenhagen. Tomorrow, I will give a talk at the DeLTA lab seminar sites.google.com/diku.edu/del... I will talk about our joint work with Geoffrey Wolfer on the estimation of the average mixing time for Markov chains + consequences for machine learning. Link: arxiv.org/abs/2402.10506