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ORCIDSouhaib Ben Taieb
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2020 – today
- 2024
[j7]Victor Dheur, Tanguy Bosser, Rafael Izbicki, Souhaib Ben Taieb:
Distribution-free conformal joint prediction regions for neural marked temporal point processes. Mach. Learn. 113(9): 7055-7102 (2024)- [j6]Le Thi Khanh Hien, Sukanya Patra, Souhaib Ben Taieb:
Anomaly detection with semi-supervised classification based on risk estimators. Trans. Mach. Learn. Res. 2024 (2024) - [c16]Victor Dheur, Souhaib Ben Taieb:
Probabilistic Calibration by Design for Neural Network Regression. AISTATS 2024: 3133-3141 - [c15]Sukanya Patra
, Nicolas Sournac, Souhaib Ben Taieb:
Detecting Abnormal Operations in Concentrated Solar Power Plants from Irregular Sequences of Thermal Images. KDD 2024: 5578-5589 - [i8]Victor Dheur, Tanguy Bosser, Rafael Izbicki, Souhaib Ben Taieb:
Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes. CoRR abs/2401.04612 (2024) - [i7]Victor Dheur, Souhaib Ben Taieb:
Probabilistic Calibration by Design for Neural Network Regression. CoRR abs/2403.11964 (2024) - [i6]Sukanya Patra, Nicolas Sournac, Souhaib Ben Taieb:
Detecting Abnormal Operations in Concentrated Solar Power Plants from Irregular Sequences of Thermal Images. CoRR abs/2406.16077 (2024) - 2023
- [j5]Tanguy Bosser, Souhaib Ben Taieb:
On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data. Trans. Mach. Learn. Res. 2023 (2023) - [c14]Tanguy Bosser, Souhaib Ben Taieb:
Revisiting the Mark Conditional Independence Assumption in Neural Marked Temporal Point Processes. ESANN 2023 - [c13]Sukanya Patra, Le Thi Khanh Hien, Souhaib Ben Taieb:
Anomaly detection in irregular image sequences for concentrated solar power plants. ESANN 2023 - [c12]Victor Dheur, Souhaib Ben Taieb:
A Large-Scale Study of Probabilistic Calibration in Neural Network Regression. ICML 2023: 7813-7836 - [i5]Victor Dheur, Souhaib Ben Taieb:
A Large-Scale Study of Probabilistic Calibration in Neural Network Regression. CoRR abs/2306.02738 (2023) - [i4]Tanguy Bosser, Souhaib Ben Taieb:
On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data. CoRR abs/2306.17066 (2023) - [i3]Le Thi Khanh Hien, Sukanya Patra, Souhaib Ben Taieb:
Anomaly detection with semi-supervised classification based on risk estimators. CoRR abs/2309.00379 (2023) - 2022
- [c11]Souhaib Ben Taieb:
Learning Quantile Functions for Temporal Point Processes with Recurrent Neural Splines. AISTATS 2022: 3219-3241 - 2020
- [i2]Fotios Petropoulos, Daniele Apiletti, Vassilios Assimakopoulos, Mohamed Zied Babai, Devon K. Barrow, Souhaib Ben Taieb, Christoph Bergmeir, Ricardo J. Bessa, Jakub Bijak, John E. Boylan, Jethro Browell, Claudio Carnevale, Jennifer L. Castle, Pasquale Cirillo, Michael P. Clements, Clara Cordeiro, Fernando Luiz Cyrino Oliveira, Shari De Baets, Alexander Dokumentov, Joanne Ellison, Piotr Fiszeder, Philip Hans Franses, David T. Frazier, Michael Gilliland, Mustafa Sinan Gönül, Paul Goodwin, Luigi Grossi, Yael Grushka-Cockayne, Mariangela Guidolin, Massimo Guidolin, Ulrich Gunter, Xiaojia Guo, Renato Guseo, Nigel Harvey, David F. Hendry, Ross Hollyman, Tim Januschowski, Jooyoung Jeon, Victor Richmond R. Jose, Yanfei Kang, Anne B. Koehler, Stephan Kolassa, Nikolaos Kourentzes, Sonia Leva, Feng Li:
Forecasting: theory and practice. CoRR abs/2012.03854 (2020)
2010 – 2019
- 2019
- [c10]Souhaib Ben Taieb, Bonsoo Koo:
Regularized Regression for Hierarchical Forecasting Without Unbiasedness Conditions. KDD 2019: 1337-1347 - 2017
- [c9]Souhaib Ben Taieb, Jiafan Yu, Mateus Neves Barreto, Ram Rajagopal:
Regularization in Hierarchical Time Series Forecasting with Application to Electricity Smart Meter Data. AAAI 2017: 4474-4480 - [c8]Souhaib Ben Taieb, James W. Taylor, Rob J. Hyndman:
Coherent Probabilistic Forecasts for Hierarchical Time Series. ICML 2017: 3348-3357 - 2016
- [j4]Souhaib Ben Taieb, Amir F. Atiya:
A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting. IEEE Trans. Neural Networks Learn. Syst. 27(1): 62-76 (2016) - [j3]Souhaib Ben Taieb, Raphaël Huser, Rob J. Hyndman, Marc G. Genton:
Forecasting Uncertainty in Electricity Smart Meter Data by Boosting Additive Quantile Regression. IEEE Trans. Smart Grid 7(5): 2448-2455 (2016) - [c7]Souhaib Ben Taieb:
Sparse and Smooth Adjustments for Coherent Forecasts in Temporal Aggregation of Time Series. NIPS Time Series Workshop 2016: 16-26 - 2015
- [c6]Ahmad H. Dehwah, Souhaib Ben Taieb, Jeff S. Shamma, Christian G. Claudel:
Decentralized Energy and Power Estimation in Solar-Powered Wireless Sensor Networks. DCOSS 2015: 199-200 - 2014
- [c5]Souhaib Ben Taieb, Rob J. Hyndman:
Boosting multi-step autoregressive forecasts. ICML 2014: 109-117 - 2013
- [c4]Liran Lerman, Gianluca Bontempi, Souhaib Ben Taieb, Olivier Markowitch:
A Time Series Approach for Profiling Attack. SPACE 2013: 75-94 - 2012
- [j2]Souhaib Ben Taieb, Gianluca Bontempi, Amir F. Atiya, Antti Sorjamaa:
A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. Expert Syst. Appl. 39(8): 7067-7083 (2012) - [c3]Gianluca Bontempi, Souhaib Ben Taieb, Yann-Aël Le Borgne:
Machine Learning Strategies for Time Series Forecasting. eBISS 2012: 62-77 - 2011
- [c2]Souhaib Ben Taieb, Gianluca Bontempi:
Recursive Multi-step Time Series Forecasting by Perturbing Data. ICDM 2011: 695-704 - [i1]Souhaib Ben Taieb, Gianluca Bontempi, Amir F. Atiya, Antti Sorjamaa:
A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. CoRR abs/1108.3259 (2011) - 2010
- [j1]Souhaib Ben Taieb, Antti Sorjamaa, Gianluca Bontempi:
Multiple-output modeling for multi-step-ahead time series forecasting. Neurocomputing 73(10-12): 1950-1957 (2010)
2000 – 2009
- 2009
- [c1]Souhaib Ben Taieb, Gianluca Bontempi, Antti Sorjamaa, Amaury Lendasse:
Long-term prediction of time series by combining direct and MIMO strategies. IJCNN 2009: 3054-3061
Coauthor Index
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