Search Results for author: Nathan Noiry

Found 13 papers, 5 papers with code

Subgroup analysis methods for time-to-event outcomes in heterogeneous randomized controlled trials

1 code implementation22 Jan 2024 Valentine Perrin, Nathan Noiry, Nicolas Loiseau, Alex Nowak

Identifying such heterogeneous treatment effects is key for precision medicine and many post-hoc analysis methods have been developed for that purpose.

Benchmarking Synthetic Data Generation

Toward Stronger Textual Attack Detectors

1 code implementation21 Oct 2023 Pierre Colombo, Marine Picot, Nathan Noiry, Guillaume Staerman, Pablo Piantanida

The landscape of available textual adversarial attacks keeps growing, posing severe threats and raising concerns regarding the deep NLP system's integrity.

A Novel Information-Theoretic Objective to Disentangle Representations for Fair Classification

no code implementations21 Oct 2023 Pierre Colombo, Nathan Noiry, Guillaume Staerman, Pablo Piantanida

One of the pursued objectives of deep learning is to provide tools that learn abstract representations of reality from the observation of multiple contextual situations.

2k Attribute +1

A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution Detection

1 code implementation6 Jun 2023 Eduardo Dadalto, Pierre Colombo, Guillaume Staerman, Nathan Noiry, Pablo Piantanida

A key feature of out-of-distribution (OOD) detection is to exploit a trained neural network by extracting statistical patterns and relationships through the multi-layer classifier to detect shifts in the expected input data distribution.

Anomaly Detection Out-of-Distribution Detection +1

Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks

no code implementations17 May 2023 Anas Himmi, Ekhine Irurozki, Nathan Noiry, Stephan Clemencon, Pierre Colombo

This paper formalize an existing problem in NLP research: benchmarking when some systems scores are missing on the task, and proposes a novel approach to address it.


Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Model

1 code implementation24 Oct 2022 Jean-Rémy Conti, Nathan Noiry, Vincent Despiegel, Stéphane Gentric, Stéphan Clémençon

In spite of the high performance and reliability of deep learning algorithms in a wide range of everyday applications, many investigations tend to show that a lot of models exhibit biases, discriminating against specific subgroups of the population (e. g. gender, ethnicity).

Face Recognition Face Verification +1

The Glass Ceiling of Automatic Evaluation in Natural Language Generation

no code implementations31 Aug 2022 Pierre Colombo, Maxime Peyrard, Nathan Noiry, Robert West, Pablo Piantanida

Automatic evaluation metrics capable of replacing human judgments are critical to allowing fast development of new methods.

Text Generation

Learning Disentangled Textual Representations via Statistical Measures of Similarity

no code implementations ACL 2022 Pierre Colombo, Guillaume Staerman, Nathan Noiry, Pablo Piantanida

When working with textual data, a natural application of disentangled representations is fair classification where the goal is to make predictions without being biased (or influenced) by sensitive attributes that may be present in the data (e. g., age, gender or race).


What are the best systems? New perspectives on NLP Benchmarking

1 code implementation8 Feb 2022 Pierre Colombo, Nathan Noiry, Ekhine Irurozki, Stephan Clemencon

In Machine Learning, a benchmark refers to an ensemble of datasets associated with one or multiple metrics together with a way to aggregate different systems performances.


Learning an Ethical Module for Bias Mitigation of pre-trained Models

no code implementations29 Sep 2021 Jean-Rémy Conti, Nathan Noiry, Stephan Clemencon, Vincent Despiegel, Stéphane Gentric

In spite of the high performance and reliability of deep learning algorithms in broad range everyday applications, many investigations tend to show that a lot of models exhibit biases, discriminating against some subgroups of the population.

Online Matching in Sparse Random Graphs: Non-Asymptotic Performances of Greedy Algorithm

no code implementations NeurIPS 2021 Nathan Noiry, Flore Sentenac, Vianney Perchet

Motivated by sequential budgeted allocation problems, we investigate online matching problems where connections between vertices are not i. i. d., but they have fixed degree distributions -- the so-called configuration model.

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