4 code implementations • 15 Jan 2017 • Yuchin Juan, Damien Lefortier, Olivier Chapelle
Predicting user response is one of the core machine learning tasks in computational advertising.
no code implementations • 11 Mar 2016 • Flavian Vasile, Damien Lefortier, Olivier Chapelle
One of the most challenging problems in computational advertising is the prediction of click-through and conversion rates for bidding in online advertising auctions.
no code implementations • NeurIPS 2011 • Olivier Chapelle, Lihong Li
Thompson sampling is one of oldest heuristic to address the exploration / exploitation trade-off, but it is surprisingly not very popular in the literature.
2 code implementations • 19 Oct 2011 • Alekh Agarwal, Olivier Chapelle, Miroslav Dudik, John Langford
We present a system and a set of techniques for learning linear predictors with convex losses on terascale datasets, with trillions of features, {The number of features here refers to the number of non-zero entries in the data matrix.}
1 code implementation • NIPS 2010 • Olivier Chapelle, Dumitru Erhan
One of the critical components in that algorithm is the choice of the preconditioner.
no code implementations • NeurIPS 2008 • Olivier Chapelle, Chuong B. Do, Choon H. Teo, Quoc V. Le, Alex J. Smola
Large-margin structured estimation methods work by minimizing a convex upper bound of loss functions.
no code implementations • NeurIPS 2008 • Kilian Q. Weinberger, Olivier Chapelle
The optimization of the semantic space incorporates large margin constraints that ensure that for each instance the correct class prototype is closer than any other.
no code implementations • NeurIPS 2007 • Zhaohui Zheng, Hongyuan Zha, Tong Zhang, Olivier Chapelle, Keke Chen, Gordon Sun
We present a general boosting method extending functional gradient boosting to optimize complex loss functions that are encountered in many machine learning problems.
no code implementations • Book 2006 • Olivier Chapelle, Bernhard Schölkopf, Alexander Zien
This chapter contains sections titled: Problem Settings, Problem of Generalization in Inductive and Transductive Inference, Structure of the VC Bounds and Transductive Inference, The Symmetrization Lemma and Transductive Inference, Bounds for Transductive Inference, The Structural Risk Minimization Principle for Induction and Transduction, Combinatorics in Transductive Inference, Measures of the Size of Equivalence Classes, Algorithms for Inductive and Transductive SVMs, Semi-Supervised Learning, Conclusion: Transductive Inference and the New Problems of Inference, Beyond Transduction: Selective Inference