136 citations · 448 across the 41 of their papers we have counts for
16 papers · 1 filter
JEPAMatch: Geometric Representation Shaping for Semi-Supervised Learning
Ali Aghababaei-Harandi, Aude Sportisse, Massih-Reza Amini
Semi-supervised learning has emerged as a powerful paradigm for leveraging large amounts of unlabeled data to improve the performance of machine learning models when labeled data a…
Multi-Label Contrastive Learning : A Comprehensive Study
Alexandre Audibert, Aurélien Gauffre, Massih-Reza Amini
Multi-label classification, which involves assigning multiple labels to a single input, has emerged as a key area in both research and industry due to its wide-ranging applications…
A Unified Contrastive Loss for Self-Training
Aurelien Gauffre, Julien Horvat, Massih-Reza Amini
Self-training methods have proven to be effective in exploiting abundant unlabeled data in semi-supervised learning, particularly when labeled data is scarce. While many of these a…
Unified Framework for Pre-trained Neural Network Compression via Decomposition and Optimized Rank Selection
Ali Aghababaei-Harandi, Massih-Reza Amini
Despite their high accuracy, complex neural networks demand significant computational resources, posing challenges for deployment on resource constrained devices such as mobile pho…
Classification Tree-based Active Learning: A Wrapper Approach
Ashna Jose, Emilie Devijver, Massih-Reza Amini +2
Supervised machine learning often requires large training sets to train accurate models, yet obtaining large amounts of labeled data is not always feasible. Hence, it becomes cruci…
Exploring Contrastive Learning for Long-Tailed Multi-Label Text Classification
Alexandre Audibert, Aurélien Gauffre, Massih-Reza Amini
Learning an effective representation in multi-label text classification (MLTC) is a significant challenge in NLP. This challenge arises from the inherent complexity of the task, wh…
Pool-Based Active Learning with Proper Topological Regions
Lies Hadjadj, Emilie Devijver, Remi Molinier +1
Machine learning methods usually rely on large sample size to have good performance, while it is difficult to provide labeled set in many applications. Pool-based active learning m…
Generalization bounds for learning under graph-dependence: A survey
Rui-Ray Zhang, Massih-Reza Amini
Traditional statistical learning theory relies on the assumption that data are identically and independently distributed (i.i.d.). However, this assumption often does not hold in m…
Self-Training: A Survey
Massih-Reza Amini, Vasilii Feofanov, Loic Pauletto +3
Semi-supervised algorithms aim to learn prediction functions from a small set of labeled observations and a large set of unlabeled observations. Because this framework is relevant…
Self-Training of Halfspaces with Generalization Guarantees under Massart Mislabeling Noise Model
Lies Hadjadj, Massih-Reza Amini, Sana Louhichi +1
We investigate the generalization properties of a self-training algorithm with halfspaces. The approach learns a list of halfspaces iteratively from labeled and unlabeled training…
Multi-class Probabilistic Bounds for Self-learning
Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini
Self-learning is a classical approach for learning with both labeled and unlabeled observations which consists in giving pseudo-labels to unlabeled training instances with a confid…
Self-Learning for Received Signal Strength Map Reconstruction with Neural Architecture Search
Aleksandra Malkova, Loic Pauletto, Christophe Villien +2
In this paper, we present a Neural Network (NN) model based on Neural Architecture Search (NAS) and self-learning for received signal strength (RSS) map reconstruction out of spars…
Treatment Targeting by AUUC Maximization with Generalization Guarantees
Artem Betlei, Eustache Diemert, Massih-Reza Amini
We consider the task of optimizing treatment assignment based on individual treatment effect prediction. This task is found in many applications such as personalized medicine or ta…
Biconditional Generative Adversarial Networks for Multiview Learning with Missing Views
Anastasiia Doinychko, Massih-Reza Amini
In this paper, we present a conditional GAN with two generators and a common discriminator for multiview learning problems where observations have two views, but one of them may be…
Semi-supervised Wrapper Feature Selection by Modeling Imperfect Labels
Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini
In this paper, we propose a new wrapper feature selection approach with partially labeled training examples where unlabeled observations are pseudo-labeled using the predictions of…
Algorithmic Robustness for Learning via -Good Similarity Functions
Maria-Irina Nicolae, Marc Sebban, Amaury Habrard +2
The notion of metric plays a key role in machine learning problems such as classification, clustering or ranking. However, it is worth noting that there is a severe lack of theoret…