136 citations · 448 across the 41 of their papers we have counts for
8 papers · 1 filter
A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling
Eustache Diemert, Artem Betlei, Christophe Renaudin +3
Individual Treatment Effect (ITE) prediction is an important area of research in machine learning which aims at explaining and estimating the causal impact of an action at the gran…
Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
Anil Goyal, Emilie Morvant, Pascal Germain +1
In this paper we propose a boosting based multiview learning algorithm, referred to as PB-MVBoost, which iteratively learns i) weights over view-specific voters capturing view-spec…
Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization
Anil Goyal, Emilie Morvant, Massih-Reza Amini
We tackle the issue of classifier combinations when observations have multiple views. Our method jointly learns view-specific weighted majority vote classifiers (i.e. for each view…
An Asynchronous Distributed Framework for Large-scale Learning Based on Parameter Exchanges
Bikash Joshi, Franck Iutzeler, Massih-Reza Amini
In many distributed learning problems, the heterogeneous loading of computing machines may harm the overall performance of synchronous strategies. In this paper, we propose an effe…
Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems
Sumit Sidana, Mikhail Trofimov, Oleg Horodnitskii +3
In this paper, we propose a novel ranking framework for collaborative filtering with the overall aim of learning user preferences over items by minimizing a pairwise ranking loss.…
Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification
Bikash Joshi, Massih-Reza Amini, Ioannis Partalas +2
We address the problem of multi-class classification in the case where the number of classes is very large. We propose a double sampling strategy on top of a multi-class to binary…
Rademacher Complexity Bounds for a Penalized Multiclass Semi-Supervised Algorithm
Yury Maximov, Massih-Reza Amini, Zaid Harchaoui
We propose Rademacher complexity bounds for multiclass classifiers trained with a two-step semi-supervised model. In the first step, the algorithm partitions the partially labeled…
PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach
Anil Goyal, Emilie Morvant, Pascal Germain +1
We study a two-level multiview learning with more than two views under the PAC-Bayesian framework. This approach, sometimes referred as late fusion, consists in learning sequential…