164 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University College LondonGB15 papers
- Institut national de recherche en sciences et technologies du numériqueFR13 papers
- Modélisation aléatoire de Paris NanterreFR13 papers
- University of LondonGB8 papers
- Centre Inria de l'Université de LilleFR7 papers
- Centre National de la Recherche ScientifiqueFR5 papers
- Laboratoire de Probabilités et Modèles AléatoiresFR5 papers
- Université Paris NanterreFR5 papers
- Centre de Mathématiques Appliquées de l'École polytechniqueFR3 papers
- Institut de Mathématiques de MarseilleFR3 papers
- Université de LilleFR3 papers
- University of OxfordGB3 papers
6 papers · 1 filter
Upper and Lower Bounds on the Performance of Kernel PCA
Maxime Haddouche, Benjamin Guedj, John Shawe-Taylor
Principal Component Analysis (PCA) is a popular method for dimension reduction and has attracted an unfailing interest for decades. More recently, kernel PCA (KPCA) has emerged as…
Learning Binary Decision Trees by Argmin Differentiation
Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae
We address the problem of learning binary decision trees that partition data for some downstream task. We propose to learn discrete parameters (i.e., for tree traversals and node p…
MAGMA: Inference and Prediction with Multi-Task Gaussian Processes
Arthur Leroy, Pierre Latouche, Benjamin Guedj +1
A novel multi-task Gaussian process (GP) framework is proposed, by using a common mean process for sharing information across tasks. In particular, we investigate the problem of ti…
PAC-Bayesian Bound for the Conditional Value at Risk
Zakaria Mhammedi, Benjamin Guedj, Robert C. Williamson
Conditional Value at Risk (CVaR) is a family of "coherent risk measures" which generalize the traditional mathematical expectation. Widely used in mathematical finance, it is garne…
Differentiable PAC-Bayes Objectives with Partially Aggregated Neural Networks
Felix Biggs, Benjamin Guedj
We make three related contributions motivated by the challenge of training stochastic neural networks, particularly in a PAC-Bayesian setting: (1) we show how averaging over an ens…
From industry-wide parameters to aircraft-centric on-flight inference: improving aeronautics performance prediction with machine learning
Florent Dewez, Benjamin Guedj, Vincent Vandewalle
Aircraft performance models play a key role in airline operations, especially in planning a fuel-efficient flight. In practice, manufacturers provide guidelines which are slightly…