Welcome
I am a research scientist with broad interests in machine learning and artificial intelligence. My work on large scale learning and stochastic gradient algorithms has received attention in the recent years. I am also known for the DjVu document compression system. I joined Facebook AI Research in March 2015.
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News
SGDQN
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.
ICML 2009
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…
OLaRank Implementation Released
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 Pass" for details.
LaRank Implementation Released
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.