231 citations
- Télécom ParisFR7 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- CentraleSupélecFR3 papers
- EURECOMFR3 papers
- Laboratoire des signaux et systèmesFR3 papers
- Laboratoire d'Informatique Gaspard-MongeFR3 papers
- Laboratoire Traitement et Communication de l’InformationFR3 papers
- Université Paris-SaclayFR3 papers
- Aristotle University of ThessalonikiGR2 papers
- CEA GrenobleFR2 papers
- École Normale Supérieure - PSLFR2 papers
- Huawei Technologies (United Kingdom)GB2 papers
6 papers · 1 filter
Differentially Private Policy Gradient
Alexandre Rio, Merwan Barlier, Igor Colin
Motivated by the increasing deployment of reinforcement learning in the real world, involving a large consumption of personal data, we introduce a differentially private (DP) polic…
MANO: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts
Renchunzi Xie, Ambroise Odonnat, Vasilii Feofanov +3
Leveraging the models' outputs, specifically the logits, is a common approach to estimating the test accuracy of a pre-trained neural network on out-of-distribution (OOD) samples w…
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input Representation
Alessandro Finamore, Chao Wang, Jonatan Krolikowski +3
Over the last years we witnessed a renewed interest toward Traffic Classification (TC) captivated by the rise of Deep Learning (DL). Yet, the vast majority of TC literature lacks c…
Local Evaluation of Time Series Anomaly Detection Algorithms
Alexis Huet, Jose Manuel Navarro, Dario Rossi
In recent years, specific evaluation metrics for time series anomaly detection algorithms have been developed to handle the limitations of the classical precision and recall. Howev…
Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?
Balázs Kégl, Gabriel Hurtado, Albert Thomas
We contribute to micro-data model-based reinforcement learning (MBRL) by rigorously comparing popular generative models using a fixed (random shooting) control agent. We find that…
Statistical learning of geometric characteristics of wireless networks
Antoine Brochard, Bartłomiej Błaszczyszyn, Stéphane Mallat +1
Motivated by the prediction of cell loads in cellular networks, we formulate the following new, fundamental problem of statistical learning of geometric marks of point processes: A…