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Neil Lawrence, Francis Bach and François Laviolette
Neil Lawrence · Francis Bach · Francois Laviolette
Sat Dec 09 05:30 PM -- 06:25 PM (PST) @ None
Author Information
Neil Lawrence
Francis Bach (Inria)
Francis Bach is a researcher at INRIA, leading since 2011 the SIERRA project-team, which is part of the Computer Science Department at Ecole Normale Supérieure in Paris, France. After completing his Ph.D. in Computer Science at U.C. Berkeley, he spent two years at Ecole des Mines, and joined INRIA and Ecole Normale Supérieure in 2007. He is interested in statistical machine learning, and especially in convex optimization, combinatorial optimization, sparse methods, kernel-based learning, vision and signal processing. He gave numerous courses on optimization in the last few years in summer schools. He has been program co-chair for the International Conference on Machine Learning in 2015.
Francois Laviolette (Université Laval)
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2020 : Francis Bach - Where is Machine Learning Going? »
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2019 Poster: Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks »
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2018 : Research Panel »
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2017 : Concluding remarks »
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2017 : Panel Session »
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2017 : Overture »
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2017 : François Laviolette - A Tutorial on PAC-Bayesian Theory »
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2017 Workshop: (Almost) 50 shades of Bayesian Learning: PAC-Bayesian trends and insights »
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2017 Poster: On Structured Prediction Theory with Calibrated Convex Surrogate Losses »
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2017 Oral: On Structured Prediction Theory with Calibrated Convex Surrogate Losses »
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2017 Poster: Nonlinear Acceleration of Stochastic Algorithms »
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2016 : Francis Bach. Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression. »
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2016 : Submodular Functions: from Discrete to Continuous Domains »
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2016 Tutorial: Large-Scale Optimization: Beyond Stochastic Gradient Descent and Convexity »
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2015 Workshop: Advances in Approximate Bayesian Inference »
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2012 Workshop: Multi-Trade-offs in Machine Learning »
Yevgeny Seldin · Guy Lever · John Shawe-Taylor · Nicolò Cesa-Bianchi · Yacov Crammer · Francois Laviolette · Gabor Lugosi · Peter Bartlett -
2011 Workshop: New Frontiers in Model Order Selection »
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2011 Poster: PAC-Bayesian Analysis of Contextual Bandits »
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2009 Poster: From PAC-Bayes Bounds to KL Regularization »
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2008 Poster: A Transductive Bound for the Voted Classifier with an Application to Semi-supervised Learning »
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2008 Spotlight: A Transductive Bound for the Voted Classifier with an Application to Semi-supervised Learning »
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2006 Poster: A PAC-Bayes Risk Bound for General Loss Functions »
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2006 Poster: PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier »
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