Author Archives: Sebastien Bubeck
Smooth distributed convex optimization
A couple of months ago we (Kevin Scaman, Francis Bach, Yin Tat Lee, Laurent Massoulie and myself) uploaded a new paper on distributed convex optimization. We came up with a pretty clean picture for the optimal oracle complexity of this … Continue reading
Posted in Optimization
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COLT 2017 accepted papers
The list of accepted papers at COLT 2017 has been published and it looks particularly good (see below with links to arxiv version)! The growth trend of previous years continues with 228 submissions (14% increase from 2016) and 73 accepted … Continue reading
Posted in Conference/workshop
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Discrepancy algorithm inspired by gradient descent and multiplicative weights; after Levy, Ramadas and Rothvoss
A week or so ago at our Theory Lunch we had the pleasure to listen to Harishchandra Ramadas (student of Thomas Rothvoss) who told us about their latest discrepancy algorithm. I think the algorithm is quite interesting as it combines … Continue reading
Posted in Theoretical Computer Science
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New journal: Mathematical Statistics and Learning
I am thrilled to announce the launch of a new journal, “Mathematical Statistics and Learning”, to be edited be the European Mathematical Society. The goal of the journal is be the natural home for the top works addressing the mathematical … Continue reading
Posted in Announcement
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STOC 2017 accepted papers
The list of accepted papers to STOC 2017 has just been released. Following the trend in recent years there are quite a few learning theory papers! I have already blogged about the kernel-based convex bandit algorithm; as well as the … Continue reading
Guest post by Miklos Racz: Confidence sets for the root in uniform and preferential attachment trees
In the final post of this series (see here for the previous posts) we consider yet another point of view for understanding networks. In the previous posts we studied random graph models with community structure and also models with an … Continue reading
Posted in Probability theory, Random graphs
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Geometry of linearized neural networks
This week we had the pleasure to host Tengyu Ma from Princeton University who told us about the recent progress he has made with co-authors to understand various linearized versions of neural networks. I will describe here two such results, … Continue reading
Posted in Machine learning, Optimization
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Local max-cut in smoothed polynomial time
Omer Angel, Yuval Peres, Fan Wei, and myself have just posted to the arXiv our paper showing that local max-cut is in smoothed polynomial time. In this post I briefly explain what is the problem, and I give a short … Continue reading
Posted in Optimization, Probability theory
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Prospective graduate student? Consider University of Washington!
This post is targeted at prospective graduate students, especially foreigners from outside the US, who are primarily interested in optimization but also have a taste for probability theory (basically readers of this blog!). As a foreigner myself I remember that … Continue reading
Posted in Uncategorized
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Guest post by Miklos Racz: Entropic central limit theorems and a proof of the fundamental limits of dimension estimation
In this post we give a proof of the fundamental limits of dimension estimation in random geometric graphs, based on the recent work of Bubeck and Ganguly. We refer the reader to the previous post for a detailed introduction; here … Continue reading
Posted in Probability theory, Random graphs
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