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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) @
Author Information
Neil Lawrence (University of Cambridge)
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)
More from the Same Authors
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2022 Poster: A Non-asymptotic Analysis of Non-parametric Temporal-Difference Learning »
Eloïse Berthier · Ziad Kobeissi · Francis Bach -
2023 Poster: On the impact of activation and normalization in obtaining isometric embeddings at initialization »
Amir Joudaki · Hadi Daneshmand · Francis Bach -
2023 Poster: Differentiable Clustering with Perturbed Spanning Forests »
Lawrence Stewart · Francis Bach · Felipe Llinares-Lopez · Quentin Berthet -
2023 Poster: Regularization properties of adversarially-trained linear regression »
Antonio Ribeiro · Dave Zachariah · Francis Bach · Thomas Schön -
2022 Spotlight: Lightning Talks 1A-4 »
Siwei Wang · Jing Liu · Nianqiao Ju · Shiqian Li · Eloïse Berthier · Muhammad Faaiz Taufiq · Arsene Fansi Tchango · Chen Liang · Chulin Xie · Jordan Awan · Jean-Francois Ton · Ziad Kobeissi · Wenguan Wang · Xinwang Liu · Kewen Wu · Rishab Goel · Jiaxu Miao · Suyuan Liu · Julien Martel · Ruobin Gong · Francis Bach · Chi Zhang · Rob Cornish · Sanmi Koyejo · Zhi Wen · Yee Whye Teh · Yi Yang · Jiaqi Jin · Bo Li · Yixin Zhu · Vinayak Rao · Wenxuan Tu · Gaetan Marceau Caron · Arnaud Doucet · Xinzhong Zhu · Joumana Ghosn · En Zhu -
2022 Spotlight: A Non-asymptotic Analysis of Non-parametric Temporal-Difference Learning »
Eloïse Berthier · Ziad Kobeissi · Francis Bach -
2022 Poster: Variational inference via Wasserstein gradient flows »
Marc Lambert · Sinho Chewi · Francis Bach · Silvère Bonnabel · Philippe Rigollet -
2022 Poster: Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays »
Konstantin Mishchenko · Francis Bach · Mathieu Even · Blake Woodworth -
2022 Poster: On the Theoretical Properties of Noise Correlation in Stochastic Optimization »
Aurelien Lucchi · Frank Proske · Antonio Orvieto · Francis Bach · Hans Kersting -
2022 Poster: Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization »
Benjamin Dubois-Taine · Francis Bach · Quentin Berthet · Adrien Taylor -
2022 Poster: Active Labeling: Streaming Stochastic Gradients »
Vivien Cabannes · Francis Bach · Vianney Perchet · Alessandro Rudi -
2020 : Francis Bach - Where is Machine Learning Going? »
Francis Bach -
2019 Poster: Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks »
Gaël Letarte · Pascal Germain · Benjamin Guedj · Francois Laviolette -
2018 : Research Panel »
Sinead Williamson · Barbara Engelhardt · Tom Griffiths · Neil Lawrence · Hanna Wallach -
2017 : Concluding remarks »
Francis Bach · Benjamin Guedj · Pascal Germain -
2017 : Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance »
Francis Bach -
2017 : Panel Session »
Neil Lawrence · Finale Doshi-Velez · Zoubin Ghahramani · Yann LeCun · Max Welling · Yee Whye Teh · Ole Winther -
2017 : Overture »
Benjamin Guedj · Francis Bach · Pascal Germain -
2017 : François Laviolette - A Tutorial on PAC-Bayesian Theory »
Francois Laviolette -
2017 Workshop: (Almost) 50 shades of Bayesian Learning: PAC-Bayesian trends and insights »
Benjamin Guedj · Pascal Germain · Francis Bach -
2017 Poster: On Structured Prediction Theory with Calibrated Convex Surrogate Losses »
Anton Osokin · Francis Bach · Simon Lacoste-Julien -
2017 Oral: On Structured Prediction Theory with Calibrated Convex Surrogate Losses »
Anton Osokin · Francis Bach · Simon Lacoste-Julien -
2017 Poster: Nonlinear Acceleration of Stochastic Algorithms »
Damien Scieur · Francis Bach · Alexandre d'Aspremont -
2017 Poster: Integration Methods and Optimization Algorithms »
Damien Scieur · Vincent Roulet · Francis Bach · Alexandre d'Aspremont -
2017 Poster: Maximum Margin Interval Trees »
Alexandre Drouin · Toby Hocking · Francois Laviolette -
2016 : Francis Bach. Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression. »
Francis Bach -
2016 : Submodular Functions: from Discrete to Continuous Domains »
Francis Bach -
2016 Tutorial: Large-Scale Optimization: Beyond Stochastic Gradient Descent and Convexity »
Suvrit Sra · Francis Bach -
2015 Workshop: Advances in Approximate Bayesian Inference »
Dustin Tran · Tamara Broderick · Stephan Mandt · James McInerney · Shakir Mohamed · Alp Kucukelbir · Matthew D. Hoffman · Neil Lawrence · David Blei -
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 »
Yevgeny Seldin · Yacov Crammer · Nicolò Cesa-Bianchi · Francois Laviolette · John Shawe-Taylor -
2011 Poster: PAC-Bayesian Analysis of Contextual Bandits »
Yevgeny Seldin · Peter Auer · Francois Laviolette · John Shawe-Taylor · Ronald Ortner -
2009 Poster: From PAC-Bayes Bounds to KL Regularization »
Pascal Germain · Alexandre Lacasse · Francois Laviolette · Mario Marchand · Sara Shanian -
2008 Poster: A Transductive Bound for the Voted Classifier with an Application to Semi-supervised Learning »
Massih R Amini · Nicolas Usunier · Francois Laviolette -
2008 Spotlight: A Transductive Bound for the Voted Classifier with an Application to Semi-supervised Learning »
Massih R Amini · Nicolas Usunier · Francois Laviolette -
2006 Poster: A PAC-Bayes Risk Bound for General Loss Functions »
Pascal Germain · Alexandre Lacasse · Francois Laviolette · Mario Marchand -
2006 Poster: PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier »
Alexandre Lacasse · Francois Laviolette · Mario Marchand · Pascal Germain · Nicolas Usunier -
2006 Spotlight: PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier »
Alexandre Lacasse · Francois Laviolette · Mario Marchand · Pascal Germain · Nicolas Usunier