http://leon.bottou.org/ 2010-07-13T18:32:01-04:00 http://leon.bottou.org/ http://leon.bottou.org/lib/images/favicon.ico text/html 2008-01-15T09:56:21-04:00 http://leon.bottou.org/news/blavatnik_award?rev=1200408981&do=diff During the 4th Annual Gala of the New York Academy of Sciences, I became one of the happy winners of the first Blavatnik Award for Young Scientists. The other finalists were very impressive. Choosing the winners must have been difficult. wp>Leonard_Blavatnik told me he attended the Nobel ceremony a few years ago and thought that something similar should be done in New York for younger scientists. Apparently he plans to fund a similar award every year. text/html 2010-04-29T23:01:44-04:00 http://leon.bottou.org/news/cos424?rev=1272596504&do=diff Rob Schapire and David Blei gave me the opportunity to teach the cos424 course at Princeton University for the spring 2010 semester. In fact Rob is on sabbatical leave at Yahoo! and David is parenting. Running the orphan course was a useful experience. One thousand slides later, I am really eager to see the student projects... text/html 2009-07-24T16:45:38-04:00 http://leon.bottou.org/news/icml_2009?rev=1248468338&do=diff ICML 2009 took place in June. Michael Littman and I were the program co-chairs. Since we were expecting a lot of work, we tried to make it interesting by experimenting with a number of changes in the review process. Read more for a little explanation and a few conclusions... text/html 2008-10-06T16:18:06-04:00 http://leon.bottou.org/news/larank_code?rev=1223324286&do=diff Antoine Bordes provides an implementation of the LaRank algorithm, together with the datasets. This new implementation runs slightly faster than the code we have used for the LaRank paper. In addition there is a special version for the case of linear kernels. text/html 2007-08-10T09:22:37-04:00 http://leon.bottou.org/news/lskm_book?rev=1186752157&do=diff MIT Press has announced the availability of the book Large-Scale Kernel Machines, edited by Léon Bottou, Olivier Chapelle, Dennis DeCoste, and Jason Weston. This book expands the theme of our NIPS 2005 workshop. The book homepage contains useful information. You can even find the complete BibTex file that was used to generate the list of references. text/html 2010-06-01T11:23:43-04:00 http://leon.bottou.org/news/microsoft?rev=1275405823&do=diff Patrice Simard and I have been friends since the old AT&T Bell Labs times. He eventually convinced me to work for him at Microsoft. He told me to expect “interesting times”. I can see several reasons for these interesting times. * The scientific point of view. There are few places where I can find machine learning problems with similar scale, similar challenges, and similar impact. This practical experience will surely feed my future machine learning research. In fact I believe that such … text/html 2008-01-15T16:06:15-04:00 http://leon.bottou.org/news/nips07_tutorial?rev=1200431175&do=diff A page has been allocated for my segment of the NIPS 2007 Tutorials. The second part of the tutorial Learning with Large Datasets was given by Alex Gray. Alex had to replace Andrew Moore on short notice because airplane delays conspired against our initial plans. The page contains the slides and a video recording a the lecture I gave at Microsoft Research a few days after NIPS. text/html 2008-10-06T16:23:13-04:00 http://leon.bottou.org/news/olarank_code?rev=1223324593&do=diff Antoine Bordes provides an implementation of the OLaRank algorithm. OLaRank is an online solver of the dual formulation of support vector machines for structured output spaces. The algorithm can use exact or greedy inference. Its running time scales linearly with the data size, competitive with a perceptron based on the same inference procedure. Its accuracy however is much better as it replicates the accuracy of a structured SVM. See the ECML/PKDD paper "Sequence Labelling SVMs Trained in One… text/html 2006-12-14T16:26:22-04:00 http://leon.bottou.org/news/old_web_site_offline?rev=1166131582&do=diff Browsing <http://leon.bottou.com> now redirects you to this new website. There is still much work to be done. text/html 2007-03-03T19:22:19-04:00 http://leon.bottou.org/news/papers?rev=1172967739&do=diff Thanks to a small lush script to parse BibTex files, all my publications are now indexed here. Most of them are available online. I still need to scan the oldest ones. My little BibTex parser now lives in the Lush CVS repository. text/html 2010-02-16T10:26:26-04:00 http://leon.bottou.org/news/senna_v2?rev=1266333986&do=diff Use BLAS, not PERL! It is the nineties again. Ronan Collobert from NEC Labs just released a noncommercial version of his neural network system for semantic extraction. Given an input sentence in plain english, Senna outputs a host of Natural Language Processing (NLP) tags: part-of-speech (POS) tags, chunking (CHK), name entity recognition (NER), and semantic role labeling (SRL). Senna does this with state-of-the-art accuracies, roughly two hundred times faster than competing approaches. text/html 2009-08-07T10:32:00-04:00 http://leon.bottou.org/news/sgdqn?rev=1249655520&do=diff The SGDQN paper has been published on the JMLR site. This variant of stochastic gradient got very good results during the first PASCAL Large Scale Learning Challenge. The paper gives a lot of explanation on the design of the algorithm. Source code is available from Antoine's web site. text/html 2006-02-12T11:02:00-04:00 http://leon.bottou.org/news/site_rewrite?rev=1139760120&do=diff Started to rewrite my web site using Dokuwiki. The old home page can be found at <http://leon.bottou.com>. The list of publication is still at <http://leon.bottou.com/publications>. text/html 2007-11-08T15:17:13-04:00 http://leon.bottou.org/news/stochastic_gradient_for_svm_and_crf?rev=1194553033&do=diff You can now download fast stochastic gradient optimizers for linear Support Vector Machines (SVMs) and Conditional Random Fields (CRFs). Stochastic Gradient Descent has been historically associated with back-propagation algorithms in multilayer neural networks. These nonlinear nonconvex problems can be very difficult. Therefore it is useful to see how Stochastic Gradient Descent performs on such simple linear and convex problems. The benchmarks are very clear! text/html 2007-11-08T15:21:16-04:00 http://leon.bottou.org/news/talks_online?rev=1194553276&do=diff The talks page contains pointers to my most significant lectures. Slides are available under both the PDF and DjVu formats.