11 citations · 18 across the 4 of their papers we have counts for
5 papers
Online (Non-)Convex Learning via Tempered Optimism
Maxime Haddouche, Olivier Wintenberger, Benjamin Guedj
Optimistic Online Learning aims to exploit experts conveying reliable information to predict the future. However, such implicit optimism may be challenged when it comes to practica…
Efficient Aggregated Kernel Tests using Incomplete -statistics
Antonin Schrab, Ilmun Kim, Benjamin Guedj +1
We propose a series of computationally efficient nonparametric tests for the two-sample, independence, and goodness-of-fit problems, using the Maximum Mean Discrepancy (MMD), Hilbe…
Reprint: a randomized extrapolation based on principal components for data augmentation
Le Li, Jiale Wei, Pai Peng +3
Data scarcity and data imbalance have attracted a lot of attention in many fields. Data augmentation, explored as an effective approach to tackle them, can improve the robustness a…
KSD Aggregated Goodness-of-fit Test
Antonin Schrab, Benjamin Guedj, Arthur Gretton
We investigate properties of goodness-of-fit tests based on the Kernel Stein Discrepancy (KSD). We introduce a strategy to construct a test, called KSDAgg, which aggregates multipl…
MMD Aggregated Two-Sample Test
Antonin Schrab, Ilmun Kim, Mélisande Albert +3
We propose two novel nonparametric two-sample kernel tests based on the Maximum Mean Discrepancy (MMD). First, for a fixed kernel, we construct an MMD test using either permutation…