Mehrdad Farajtabar
Mehrdad Farajtabar
Google DeepMind
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Coevolve: A joint point process model for information diffusion and network co-evolution
M Farajtabar, M Gomez-Rodriguez, Y Wang, S Li, H Zha, L Song
Companion Proceedings of the The Web Conference 2018, 473-477, 2018
1932018
Improved knowledge distillation via teacher assistant: Bridging the gap between student and teacher
SI Mirzadeh, M Farajtabar, A Li, H Ghasemzadeh
AAAI 2020, 2020
162*2020
Shaping social activity by incentivizing users
M Farajtabar, N Du, MG Rodriguez, I Valera, H Zha, L Song
Advances in neural information processing systems 27, 2014
1522014
Dirichlet-hawkes processes with applications to clustering continuous-time document streams
N Du, M Farajtabar, A Ahmed, AJ Smola, L Song
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015
1452015
Dyrep: Learning representations over dynamic graphs
R Trivedi, M Farajtabar, P Biswal, H Zha
International Conference on Learning Representations (ICLR), 2019
133*2019
Learning granger causality for hawkes processes
H Xu, M Farajtabar, H Zha
International Conference on Machine Learning, 1717-1726, 2016
1332016
More robust doubly robust off-policy evaluation
M Farajtabar, Y Chow, M Ghavamzadeh
International Conference on Machine Learning (ICML), 1446-1455, 2018
1142018
Fake news mitigation via point process based intervention
M Farajtabar, J Yang, X Ye, H Xu, R Trivedi, E Khalil, S Li, L Song, H Zha
International Conference on Machine Learning, 1097-1106, 2017
1082017
Wasserstein learning of deep generative point process models
S Xiao, M Farajtabar, X Ye, J Yan, L Song, H Zha
arXiv preprint arXiv:1705.08051, 2017
1022017
Back to the past: Source identification in diffusion networks from partially observed cascades
M Farajtabar, MG Rodriguez, M Zamani, N Du, H Zha, L Song
Artificial Intelligence and Statistics, 232-240, 2015
852015
Learning time series associated event sequences with recurrent point process networks
S Xiao, J Yan, M Farajtabar, L Song, X Yang, H Zha
IEEE transactions on neural networks and learning systems 30 (10), 3124-3136, 2019
56*2019
Adapting auxiliary losses using gradient similarity
Y Du, WM Czarnecki, SM Jayakumar, M Farajtabar, R Pascanu, ...
arXiv preprint arXiv:1812.02224, 2018
512018
Multistage Campaigning in Social Networks
M Farajtabar, X Ye, S Harati, L Song, H Zha
Advances in Neural Information Processing Systems, 4718-4726, 2016
502016
Recurrent poisson factorization for temporal recommendation
SA Hosseini, A Khodadadi, K Alizadeh, A Arabzadeh, M Farajtabar, H Zha, ...
IEEE Transactions on Knowledge and Data Engineering 32 (1), 121-134, 2018
482018
Correlated cascades: Compete or cooperate
A Zarezade, A Khodadadi, M Farajtabar, HR Rabiee, H Zha
AAAI 2017, 2017
452017
Self-distillation amplifies regularization in hilbert space
H Mobahi, M Farajtabar, PL Bartlett
arXiv preprint arXiv:2002.05715, 2020
402020
From local similarity to global coding: An application to image classification
A Shaban, HR Rabiee, M Farajtabar, M Ghazvininejad
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2013
392013
Orthogonal Gradient Descent for Continual Learning
M Farajtabar, N Azizan, A Mott, A Li
AISTATS 2020, 2020
382020
NetCodec: Community Detection from Individual Activities
L Tran, M Farajtabar, L Song, H Zha
SIAM International Conference on Data Mining, 2015
362015
Detecting changes in dynamic events over networks
S Li, Y Xie, M Farajtabar, A Verma, L Song
IEEE Transactions on Signal and Information Processing over Networks 3 (2 …, 2017
332017
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Articles 1–20