I am a research scientist with broad interests in practical and theoretical machine learning. 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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The report “Counterfactual Reasoning and Learning Systems” shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Such predictions allow both humans and algorithms to select changes that improve both the short-term and long-term performance of such systems. This work is illustrated by experiments carried out on the ad placement system associated with the Bing search engine.
Announcing version 2.0 of my Stochastic Gradient Descent package. This release provides implementations of the Stochastic Gradient Descent and Averaged Stochastic Gradient Descent algorithms for Linear SVMs and CRFs. The latter sometimes shows vastly superior performance. See the SGD package pages for details.
Ronan's masterpiece, "Natural Language Processing (Almost) from Scratch", has been published in JMLR. This paper describes how to use a unified neural network architecture to solve a collection of natural language processing tasks with near state-of-the-art accuracies and ridiculously fast processing speed. A couple thousand lines of C code processes english sentence at more than 10000 words per second and outputs part-of-speech tags, named entity tags, chunk boundaries, semantic role labeling tags, and, in the latest version, syntactic parse trees. Download SENNA!
Learning Semantics, Nips 2011 Workshop, Saturday December 17, 2011. Melia Sierra Nevada & Melia Sol y Nieve, Sierra Nevada, Spain.
This workshop is organized in collaboration with Antoine Bordes, Jason Weston, Ronan Collobert. This event should be very interesing: I believe that recent machine learning advances indicate new connections between machine learning and machine reasoning and lead to new opportunties for learning the semantics of the world.