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Author Information
Zakaria Mhammedi (The Australian National University)
Peter Grünwald (CWI and Leiden University)
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 FranceUK 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 PACBayesian theory.
More from the Same Authors

2021 Poster: Risk Monotonicity in Statistical Learning »
Zakaria Mhammedi 
2021 Oral: Risk Monotonicity in Statistical Learning »
Zakaria Mhammedi 
2021 Poster: Learning Stochastic Majority Votes by Minimizing a PACBayes Generalization Bound »
Valentina Zantedeschi · Paul Viallard · Emilie Morvant · Rémi Emonet · Amaury Habrard · Pascal Germain · Benjamin Guedj 
2020 Poster: PACBayesian Bound for the Conditional Value at Risk »
Zakaria Mhammedi · Benjamin Guedj · Robert Williamson 
2020 Poster: Learning the Linear Quadratic Regulator from Nonlinear Observations »
Zakaria Mhammedi · Dylan Foster · Max Simchowitz · Dipendra Misra · Wen Sun · Akshay Krishnamurthy · Alexander Rakhlin · John Langford 
2020 Spotlight: PACBayesian Bound for the Conditional Value at Risk »
Zakaria Mhammedi · Benjamin Guedj · Robert Williamson 
2019 Poster: Dichotomize and Generalize: PACBayesian Binary Activated Deep Neural Networks »
Gaël Letarte · Pascal Germain · Benjamin Guedj · Francois Laviolette 
2018 Poster: Constant Regret, Generalized Mixability, and Mirror Descent »
Zakaria Mhammedi · Robert Williamson 
2018 Spotlight: Constant Regret, Generalized Mixability, and Mirror Descent »
Zakaria Mhammedi · Robert Williamson 
2017 Workshop: (Almost) 50 shades of Bayesian Learning: PACBayesian trends and insights »
Benjamin Guedj · Pascal Germain · Francis Bach 
2016 Poster: Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning »
Wouter Koolen · Peter Grünwald · Tim van Erven 
2014 Workshop: From Bad Models to Good Policies (Sequential Decision Making under Uncertainty) »
OdalricAmbrym Maillard · Timothy A Mann · Shie Mannor · Jeremie Mary · Laurent Orseau · Thomas Dietterich · Ronald Ortner · Peter Grünwald · Joelle Pineau · Raphael Fonteneau · Georgios Theocharous · Esteban D Arcaute · Christos Dimitrakakis · Nan Jiang · Doina Precup · PierreLuc Bacon · Marek Petrik · Aviv Tamar 
2014 Poster: Learning the Learning Rate for Prediction with Expert Advice »
Wouter M Koolen · Tim van Erven · Peter Grünwald 
2013 Workshop: Learning Faster From Easy Data »
Peter Grünwald · Wouter M Koolen · Sasha Rakhlin · Nati Srebro · Alekh Agarwal · Karthik Sridharan · Tim van Erven · Sebastien Bubeck 
2012 Poster: Mixability in Statistical Learning »
Tim van Erven · Peter Grünwald · Mark Reid · Robert Williamson 
2011 Poster: Adaptive Hedge »
Tim van Erven · Peter Grünwald · Wouter M Koolen · Steven D Rooij 
2007 Spotlight: Catching Up Faster in Bayesian Model Selection and Model Averaging »
Tim van Erven · Peter Grünwald · Steven de Rooij 
2007 Poster: Catching Up Faster in Bayesian Model Selection and Model Averaging »
Tim van Erven · Peter Grünwald · Steven de Rooij