output
20202025
most citedTransductive Information Maximization For Few-Shot Learning

49 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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Showing cs.LGShow all

6 papers · 1 filter

cs.LG2025

A Theoretical Justification for Asymmetric Actor-Critic Algorithms

Gaspard Lambrechts, Damien Ernst, Aditya Mahajan

In reinforcement learning for partially observable environments, many successful algorithms have been developed within the asymmetric learning paradigm. This paradigm leverages add…

cs.LG2024

Exposing the Vulnerability of Decentralized Learning to Membership Inference Attacks Through the Lens of Graph Mixing

Ousmane Touat, Jezekael Brunon, Yacine Belal +4

The primary promise of decentralized learning is to allow users to engage in the training of machine learning models in a collaborative manner while keeping their data on their pre…

cs.LG2024

Beyond the Norms: Detecting Prediction Errors in Regression Models

Andres Altieri, Marco Romanelli, Georg Pichler +2

This paper tackles the challenge of detecting unreliable behavior in regression algorithms, which may arise from intrinsic variability (e.g., aleatoric uncertainty) or modeling err…

cs.LG2022★ 15 cited

Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning

Georg Pichler, Marco Romanelli, Leonardo Rey Vega +1

Federated Learning is expected to provide strong privacy guarantees, as only gradients or model parameters but no plain text training data is ever exchanged either between the clie…

cs.LG2021★ 27 cited

Adversarial Robustness via Fisher-Rao Regularization

Marine Picot, Francisco Messina, Malik Boudiaf +3

Adversarial robustness has become a topic of growing interest in machine learning since it was observed that neural networks tend to be brittle. We propose an information-geometric…

cs.LG2020★ 49 cited

Transductive Information Maximization For Few-Shot Learning

Malik Boudiaf, Ziko Imtiaz Masud, Jérôme Rony +3

We introduce Transductive Infomation Maximization (TIM) for few-shot learning. Our method maximizes the mutual information between the query features and their label predictions fo…