3.4k citations · 4.9k across the 135 of their papers we have counts for
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A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption
Peter L. Bartlett, Victor Gabillon, Michal Valko
We study the problem of optimizing a function under a \emph{budgeted number of evaluations}. We only assume that the function is \emph{locally} smooth around one of its global opti…
Compressing the Input for CNNs with the First-Order Scattering Transform
Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko +1
We study the first-order scattering transform as a candidate for reducing the signal processed by a convolutional neural network (CNN). We show theoretical and empirical evidence t…
DPPy: Sampling DPPs with Python
Guillaume Gautier, Guillermo Polito, Rémi Bardenet +1
Determinantal point processes (DPPs) are specific probability distributions over clouds of points that are used as models and computational tools across physics, probability, stati…
Finding the bandit in a graph: Sequential search-and-stop
Pierre Perrault, Vianney Perchet, Michal Valko
We consider the problem where an agent wants to find a hidden object that is randomly located in some vertex of a directed acyclic graph (DAG) according to a fixed but possibly unk…
Distributed Adaptive Sampling for Kernel Matrix Approximation
Daniele Calandriello, Alessandro Lazaric, Michal Valko
Most kernel-based methods, such as kernel or Gaussian process regression, kernel PCA, ICA, or -means clustering, do not scale to large datasets, because constructing and storing…