Generative Adversarial Nets. - Ian J. Goodfellow; Jean Pouget-Abadie; Mehdi Mirza; Bing Xu; David Warde-Farley; Sherjil Ozair; Aaron C. Courville; Yoshua Bengio (NIPS, 2014)

Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation. - Jean Pouget-Abadie; Dzmitry Bahdanau; Bart van Merrienboer; Kyunghyun Cho; Yoshua Bengio (SSST@EMNLP, 2014)

Inferring Graphs from Cascades: A Sparse Recovery Framework. - Jean Pouget-Abadie; Thibaut Horel (WWW (Companion Volume), 2015)

Detecting Network Effects: Randomizing Over Randomized Experiments. - Martin Saveski; Jean Pouget-Abadie; Guillaume Saint-Jacques; Weitao Duan; Souvik Ghosh; Ya Xu; Edoardo M. Airoldi (KDD, 2017)

Optimizing Cluster-based Randomized Experiments under Monotonicity. - Jean Pouget-Abadie; Vahab S. Mirrokni; David C. Parkes; Edoardo M. Airoldi (KDD, 2018)

Randomized Experimental Design via Geographic Clustering. - David Rolnick; Kevin Aydin; Jean Pouget-Abadie; Shahab Kamali; Vahab S. Mirrokni; Amir Najmi (KDD, 2019)

Variance Reduction in Bipartite Experiments through Correlation Clustering. - Jean Pouget-Abadie; Kevin Aydin; Warren Schudy; Kay Brodersen; Vahab S. Mirrokni (NeurIPS, 2019)

Generative adversarial networks. - Ian J. Goodfellow; Jean Pouget-Abadie; Mehdi Mirza; Bing Xu; David Warde-Farley; Sherjil Ozair; Aaron C. Courville; Yoshua Bengio (Commun. ACM, 2020)

Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls. - Nick Doudchenko; Khashayar Khosravi; Jean Pouget-Abadie; Sébastien Lahaie; Miles Lubin; Vahab S. Mirrokni; Jann Spiess; Guido Imbens (NeurIPS, 2021)

Cluster Randomized Designs for One-Sided Bipartite Experiments. - Jennifer Brennan; Vahab Mirrokni; Jean Pouget-Abadie (NeurIPS, 2022)

Design and Analysis of Bipartite Experiments Under a Linear Exposure-response Model. - Christopher Harshaw; Fredrik Sävje; David Eisenstat; Vahab Mirrokni; Jean Pouget-Abadie (EC, 2022)

Modeling Interference Using Experiment Roll-out. - Ariel Boyarsky; Hongseok Namkoong; Jean Pouget-Abadie (EC, 2023)

Causal Estimation of User Learning in Personalized Systems. - Evan Munro; David Jones; Jennifer Brennan; Roland Nelet; Vahab Mirrokni; Jean Pouget-Abadie (EC, 2023)

Causal Inference with Differentially Private (Clustered) Outcomes. - Adel Javanmard; Vahab Mirrokni; Jean Pouget-Abadie (CoRR, 2023)

Cascade-Based Randomization for Inferring Causal Effects under Diffusion Interference. - Zahra Fatemi; Jean Pouget-Abadie; Elena Zheleva (ICWSM, 2024)

Country-diverted experiments for mitigation of network effects. - Lina Lin; Changping Meng; Jennifer Brennan; Jean Pouget-Abadie; Ningren Han; Shuchao Bi; Yajun Peng (RecSys, 2024)

Reducing Symbiosis Bias through Better A/B Tests of Recommendation Algorithms. - Jennifer Brennan; Yahu Cong; Yiwei Yu; Lina Lin; Yajun Peng; Changping Meng; Ningren Han; Jean Pouget-Abadie; David M. Holtz (WWW, 2025)

Gemma 3 Technical Report. - Aishwarya Kamath; Johan Ferret; Shreya Pathak; Nino Vieillard; Ramona Merhej; Sarah Perrin; Tatiana Matejovicova; Alexandre Ramé; Morgane Rivière; Louis Rouillard; Thomas Mesnard; Geoffrey Cideron; et al. (CoRR, 2025)