Category Archives: Machine learning

Guest post by Julien Mairal: A Kernel Point of View on Convolutional Neural Networks, part II

This is a continuation of Julien Mairal‘s guest post on CNNs, see part I here. Stability to deformations of convolutional neural networks In their ICML paper Zhang et al. introduce a functional space for CNNs with one layer, by noticing … Continue reading

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Guest post by Julien Mairal: A Kernel Point of View on Convolutional Neural Networks, part I

    I (n.b., Julien Mairal) have been interested in drawing links between neural networks and kernel methods for some time, and I am grateful to Sebastien for giving me the opportunity to say a few words about it on … Continue reading

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Optimal bound for stochastic bandits with corruption

Guest post by Mark Sellke. In the comments of the previous blog post we asked if the new viewpoint on best of both worlds can be used to get clean “interpolation” results. The context is as follows: in a STOC … Continue reading

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Amazing progress in adversarially robust stochastic multi-armed bandits

In this post I briefly discuss some recent stunning progress on robust bandits (for more background on bandits see these two posts, part 1 and part 2, in particular what is described below gives a solution to Open Problem 3 … Continue reading

Posted in Machine learning, Optimization, Theoretical Computer Science | 2 Comments

How to generalize (algorithmically)

A couple of months ago I taught an introduction to statistical learning theory. I took inspiration from two very good introductory books on SLT: “Foundations of ML”, and “Understanding Machine Learning: From Theory to Algorithms”. I also covered some classical … Continue reading

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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 | 2 Comments

Notes on least-squares, part II

As promised at the end of Part I, I will give some intuition for the Bach and Moulines analysis of constant step size SGD for least-squares. Let us recall the setting. Let be a pair of random variables with where … Continue reading

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Notes on least-squares, part I

These are mainly notes for myself, but I figured that they might be of interest to some of the blog readers too. Comments on what is written below are most welcome! Let be a pair of random variables, and let … Continue reading

Posted in Machine learning, Optimization | 3 Comments

AlphaGo is born

Google DeepMind is making the front page of Nature (again) with a new AI for Go, named AlphaGo (see also this Nature youtube video). Computer Go is a notoriously difficult problem, and up to now AI were faring very badly … Continue reading

Posted in Machine learning, Uncategorized | 3 Comments

On the spirit of NIPS 2015 and OpenAI

I just came back from NIPS 2015 which was a clear success in terms of numbers (note that this growth is not all because of deep learning, only about 10% of the papers were on this topic, which is about … Continue reading

Posted in Conference/workshop, Machine learning | 19 Comments