J
Jean Pouget-Abadie
Researcher at Université de Montréal
Publications - 25
Citations - 43672
Jean Pouget-Abadie is an academic researcher from Université de Montréal. The author has contributed to research in topics: Graph (abstract data type) & Computer science. The author has an hindex of 10, co-authored 21 publications receiving 32708 citations. Previous affiliations of Jean Pouget-Abadie include Google & Harvard University.
Papers
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Proceedings ArticleDOI
Inferring Graphs from Cascades: A Sparse Recovery Framework
Jean Pouget-Abadie,Thibaut Horel +1 more
TL;DR: This work provides the first algorithm which recovers the graph's edges with high probability and O(s log m) measurements, and shows that this algorithm also recovers the edge weights (the parameters of the diffusion process) and is robust in the context of approximate sparsity.
Journal ArticleDOI
Testing for arbitrary interference on experimentation platforms
Jean Pouget-Abadie,Guillaume Saint-Jacques,Martin Saveski,Weitao Duan,Souvik Ghosh,Ya Xu,Edoardo M. Airoldi +6 more
TL;DR: An experimental design strategy for testing whether the classic assumption of no interference among users, under which the outcome of one user does not depend on the treatment assigned to other users, is rarely tenable on such platforms is introduced.
Proceedings Article
Variance Reduction in Bipartite Experiments through Correlation Clustering
TL;DR: A novel clustering objective and a corresponding algorithm that partitions a bipartite graph so as to maximize the statistical power of a bipartsite experiment on that graph are introduced.
Posted Content
Inferring Graphs from Cascades: A Sparse Recovery Framework
Jean Pouget-Abadie,Thibaut Horel +1 more
TL;DR: In this article, a general model of cascades, including the voter model and the independent cascade model, is introduced and the first algorithm which recovers the graph's edges with high probability and O(s ∆ log m) measurements where ∆ is the maximum degree of the graph and m is the number of nodes.
Posted Content
Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation
Jean Pouget-Abadie,Dzmitry Bahdanau,Bart van Merriënboer,Kyunghyun Cho,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio +6 more
TL;DR: The authors propose to segment an input sentence into phrases that can be easily translated by the NMT model and concatenate the translated clauses to form a final translation, which shows a significant improvement in translation quality for long sentences.