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Author Information
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.
Benjamin Guedj (Inria & University College London)
Benjamin Guedj is a tenured research scientist at Inria since 2014, affiliated to the Lille - Nord Europe research centre in France. He is also affiliated with the mathematics department of the University of Lille. Since 2018, he is a Principal Research Fellow at the Centre for Artificial Intelligence and Department of Computer Science at University College London. He is also a visiting researcher at The Alan Turing Institute. Since 2020, he is the founder and scientific director of The Inria London Programme, a strategic partnership between Inria and UCL as part of a France-UK scientific initiative. He obtained his Ph.D. in mathematics in 2013 from UPMC (Université Pierre & Marie Curie, France) under the supervision of Gérard Biau and Éric Moulines. Prior to that, he was a research assistant at DTU Compute (Denmark). His main line of research is in statistical machine learning, both from theoretical and algorithmic perspectives. He is primarily interested in the design, analysis and implementation of statistical machine learning methods for high dimensional problems, mainly using the PAC-Bayesian theory.
Pascal Germain (INRIA)
More from the Same Authors
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2021 : Progress in Self-Certified Neural Networks »
Maria Perez-Ortiz · Omar Rivasplata · Emilio Parrado-Hernández · Benjamin Guedj · John Shawe-Taylor -
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: Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory »
Sokhna Diarra Mbacke · Florence Clerc · Pascal Germain -
2023 Poster: Regularization properties of adversarially-trained linear regression »
Antonio Ribeiro · Dave Zachariah · Francis Bach · Thomas Schön -
2023 Poster: Learning via Wasserstein-Based High Probability Generalization Bounds »
Paul Viallard · Maxime Haddouche · Umut Simsekli · Benjamin Guedj -
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: KSD Aggregated Goodness-of-fit Test »
Antonin Schrab · Benjamin Guedj · Arthur Gretton -
2022 Poster: Efficient Aggregated Kernel Tests using Incomplete $U$-statistics »
Antonin Schrab · Ilmun Kim · Benjamin Guedj · Arthur Gretton -
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: On Margins and Generalisation for Voting Classifiers »
Felix Biggs · Valentina Zantedeschi · Benjamin Guedj -
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: Online PAC-Bayes Learning »
Maxime Haddouche · Benjamin Guedj -
2022 Poster: Active Labeling: Streaming Stochastic Gradients »
Vivien Cabannes · Francis Bach · Vianney Perchet · Alessandro Rudi -
2021 Poster: Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound »
Valentina Zantedeschi · Paul Viallard · Emilie Morvant · Rémi Emonet · Amaury Habrard · Pascal Germain · Benjamin Guedj -
2020 : Francis Bach - Where is Machine Learning Going? »
Francis Bach -
2020 Poster: PAC-Bayesian Bound for the Conditional Value at Risk »
Zakaria Mhammedi · Benjamin Guedj · Robert Williamson -
2020 Spotlight: PAC-Bayesian Bound for the Conditional Value at Risk »
Zakaria Mhammedi · Benjamin Guedj · Robert Williamson -
2019 Poster: PAC-Bayes Un-Expected Bernstein Inequality »
Zakaria Mhammedi · Peter Grünwald · Benjamin Guedj -
2019 Poster: Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks »
Gaël Letarte · Pascal Germain · Benjamin Guedj · Francois Laviolette -
2017 : Neil Lawrence, Francis Bach and François Laviolette »
Neil Lawrence · Francis Bach · Francois Laviolette -
2017 : Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance »
Francis Bach -
2017 : Overture »
Benjamin Guedj · Francis Bach · Pascal Germain -
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 -
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 -
2014 Workshop: Second Workshop on Transfer and Multi-Task Learning: Theory meets Practice »
Urun Dogan · Tatiana Tommasi · Yoshua Bengio · Francesco Orabona · Marius Kloft · Andres Munoz · Gunnar Rätsch · Hal Daumé III · Mehryar Mohri · Xuezhi Wang · Daniel Hernández-lobato · Song Liu · Thomas Unterthiner · Pascal Germain · Vinay P Namboodiri · Michael Goetz · Christopher Berlind · Sigurd Spieckermann · Marta Soare · Yujia Li · Vitaly Kuznetsov · Wenzhao Lian · Daniele Calandriello · Emilie Morvant -
2009 Poster: From PAC-Bayes Bounds to KL Regularization »
Pascal Germain · Alexandre Lacasse · Francois Laviolette · Mario Marchand · Sara Shanian