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- Ian J. Goodfellow, Jean Pouget-Abadie, +5 authors Yoshua Bengio
- NIPS
- 2014

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the… (More)

- Ian J. Goodfellow, Jean Pouget-Abadie, +5 authors Yoshua Bengio
- ArXiv
- 2014

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the… (More)

- Jean Pouget-Abadie, Thibaut Horel
- ICML
- 2015

In the Graph Inference problem, one seeks to recover the edges of an unknown graph from the observations of cascades propagating over this graph. We approach this problem from the sparse recovery perspective. We introduce a general model of cascades, including the voter model and the independent cascade model, for which we provide the first algorithm which… (More)

- Jean Pouget-Abadie, Dzmitry Bahdanau, Bart van Merrienboer, Kyunghyun Cho, Yoshua Bengio
- SSST@EMNLP
- 2014

The authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems. In this paper, we propose a way to address this issue by automatically segmenting an input sentence into phrases… (More)

- Jean Pouget-Abadie, Martin Saveski, +4 authors Edoardo M. Airoldi
- 2017

Experimentation platforms are essential components of modern large IT companies, as they are used to carry out a large number of randomized experiments daily. On such platforms, the classic assumption of no interference among users—that is, the fact that the outcome of a user does not depend on the treatment assigned to other users—is rarely tenable. Here,… (More)

- Martin Saveski, Jean Pouget-Abadie, +4 authors Edoardo M. Airoldi
- KDD
- 2017

Randomized experiments, or A/B tests, are the standard approach for evaluating the causal effects of new product features, i.e., treatments. The validity of these tests rests on the "stable unit treatment value assumption" (SUTVA), which implies that the treatment only affects the behavior of treated users, and does not affect the behavior of their… (More)

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