Search Results for author: Louis Abraham

Found 7 papers, 3 papers with code

Prompt Selection Matters: Enhancing Text Annotations for Social Sciences with Large Language Models

no code implementations15 Jul 2024 Louis Abraham, Charles Arnal, Antoine Marie

Large Language Models have recently been applied to text annotation tasks from social sciences, equalling or surpassing the performance of human workers at a fraction of the cost.

text annotation

A Game of Competition for Risk

1 code implementation30 May 2023 Louis Abraham

We also experimentally validate that the Nash equilibrium in our model also serves as a correlated equilibrium.

FastCPH: Efficient Survival Analysis for Neural Networks

2 code implementations21 Aug 2022 Xuelin Yang, Louis Abraham, Sejin Kim, Petr Smirnov, Feng Ruan, Benjamin Haibe-Kains, Robert Tibshirani

The Cox proportional hazards model is a canonical method in survival analysis for prediction of the life expectancy of a patient given clinical or genetic covariates -- it is a linear model in its original form.

Survival Analysis

Competition analysis on the over-the-counter credit default swap market

no code implementations3 Dec 2020 Louis Abraham

We present our methodology as part of the literature on model interpretability before arguing for the use of conditional entropy as the metric of interest to derive knowledge from data through a model-agnostic approach.

Bloom Origami Assays: Practical Group Testing

no code implementations21 Jul 2020 Louis Abraham, Gary Becigneul, Benjamin Coleman, Bernhard Scholkopf, Anshumali Shrivastava, Alexander Smola

Group testing is a well-studied problem with several appealing solutions, but recent biological studies impose practical constraints for COVID-19 that are incompatible with traditional methods.

Crackovid: Optimizing Group Testing

no code implementations13 May 2020 Louis Abraham, Gary Bécigneul, Bernhard Schölkopf

We study the problem usually referred to as group testing in the context of COVID-19.

LassoNet: A Neural Network with Feature Sparsity

2 code implementations29 Jul 2019 Ismael Lemhadri, Feng Ruan, Louis Abraham, Robert Tibshirani

Unlike other approaches to feature selection for neural nets, our method uses a modified objective function with constraints, and so integrates feature selection with the parameter learning directly.

feature selection regression

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