26 citations · 41 across the 12 of their papers we have counts for
14 papers
Improved Confidence Estimates for Black-Box Large Language Models
Sokhna Diarra Mbacke, Mouloud Belbahri, Gabriel Loaiza-Ganem
Uncertainty quantification (UQ) is essential for the safe deployment of large language models (LLMs). Existing methods, from verbalized confidence to ones requiring multiple genera…
On the Burden of Achieving Fairness in Conformal Prediction
Ziang Gao, Pengqi Liu, Archer Yi Yang +3
Conformal prediction is often calibrated with a single pooled threshold, but this can hide cross-group heterogeneity in score distributions and distort group-wise coverage. We stud…
Beyond Procedure: Substantive Fairness in Conformal Prediction
Pengqi Liu, Zijun Yu, Mouloud Belbahri +3
Conformal prediction (CP) offers distribution-free uncertainty quantification for machine learning models, yet its interplay with fairness in downstream decision-making remains und…
Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systems
Kin Kwan Leung, Mouloud Belbahri, Yi Sui +4
Retrieval-augmented generation (RAG) is a prevalent approach for building LLM-based question-answering systems that can take advantage of external knowledge databases. Due to the c…
Conformal Prediction Sets Can Cause Disparate Impact
Jesse C. Cresswell, Bhargava Kumar, Yi Sui +1
Conformal prediction is a statistically rigorous method for quantifying uncertainty in models by having them output sets of predictions, with larger sets indicating more uncertaint…
Distilled ChatGPT Topic & Sentiment Modeling with Applications in Finance
Olivier Gandouet, Mouloud Belbahri, Armelle Jezequel +1
In this study, ChatGPT is utilized to create streamlined models that generate easily interpretable features. These features are then used to evaluate financial outcomes from earnin…
A Twin Neural Model for Uplift
Mouloud Belbahri, Olivier Gandouet, Alejandro Murua +1
Uplift is a particular case of conditional treatment effect modeling. Such models deal with cause-and-effect inference for a specific factor, such as a marketing intervention or a…
Adapting Neural Networks for Uplift Models
Belbahri Mouloud, Gandouet Olivier, Kazma Ghaith
Uplift is a particular case of individual treatment effect modeling. Such models deal with cause-and-effect inference for a specific factor, such as a marketing intervention. In pr…
Qini-based Uplift Regression
Mouloud Belbahri, Alejandro Murua, Olivier Gandouet +1
Uplift models provide a solution to the problem of isolating the marketing effect of a campaign. For customer churn reduction, uplift models are used to identify the customers who…
How Does Batch Normalization Help Binary Training?
Eyyüb Sari, Mouloud Belbahri, Vahid Partovi Nia
Binary Neural Networks (BNNs) are difficult to train, and suffer from drop of accuracy. It appears in practice that BNNs fail to train in the absence of Batch Normalization (BatchN…
Active Learning for High-Dimensional Binary Features
Ali Vahdat, Mouloud Belbahri, Vahid Partovi Nia
Erbium-doped fiber amplifier (EDFA) is an optical amplifier/repeater device used to boost the intensity of optical signals being carried through a fiber optic communication system.…
Uplift Regression: The R Package tools4uplift
Mouloud Belbahri, Alejandro Murua, Olivier Gandouet +1
Uplift modeling aims at predicting the causal effect of an action such as a medical treatment or a marketing campaign on a particular individual, by taking into consideration the r…
Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks
Mouloud Belbahri, Eyyüb Sari, Sajad Darabi +1
Deep neural networks (DNNs) have demonstrated success for many supervised learning tasks, ranging from voice recognition, object detection, to image classification. However, their…
Regularized Binary Network Training
Sajad Darabi, Mouloud Belbahri, Matthieu Courbariaux +1
There is a significant performance gap between Binary Neural Networks (BNNs) and floating point Deep Neural Networks (DNNs). We propose to improve the binary training method, by in…