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2020 – today
- 2025
[j15]Haifei Zhang, Benjamin Quost, Marie-Hélène Masson:
Cautious classifier ensembles for set-valued decision-making. Int. J. Approx. Reason. 177: 109328 (2025)- 2024
- [j14]Loïc Adam
, Arthur Van Camp, Sébastien Destercke, Benjamin Quost:
Inferring from an imprecise Plackett-Luce model: Application to label ranking. Fuzzy Sets Syst. 482: 108908 (2024) - [j13]Antoine Plissonneau, Luca Jourdan, Damien Trentesaux, Lotfi Abdi, Mohamed Sallak, Abdelghani Bekrar, Benjamin Quost, Walter Schön:
Deep reinforcement learning with predictive auxiliary task for autonomous train collision avoidance. J. Rail Transp. Plan. Manag. 31: 100453 (2024) - [c27]Soroush Ghandi, Benjamin Quost, Cassio de Campos:
Probabilistic Circuits with Constraints via Convex Optimization. ECML/PKDD (3) 2024: 161-177 - [c26]Wenlong Chen, Cyprien Gilet, Benjamin Quost, Sébastien Destercke:
Robust Discrete Bayesian Classifier Under Covariate and Label Noise. SUM 2024: 100-114 - [i2]Soroush Ghandi, Benjamin Quost, Cassio de Campos:
Probabilistic Circuits with Constraints via Convex Optimization. CoRR abs/2403.13125 (2024) - [i1]Soroush Ghandi, Benjamin Quost, Cassio de Campos:
Soft Learning Probabilistic Circuits. CoRR abs/2403.14504 (2024) - 2023
- [j12]Haifei Zhang, Benjamin Quost, Marie-Hélène Masson:
Cautious weighted random forests. Expert Syst. Appl. 213(Part): 118883 (2023) - [c25]Come Rodriguez, Vitor Martin Bordini, Sébastien Destercke, Benjamin Quost:
Self Learning using Venn-Abers predictors. COPA 2023: 234-250 - [c24]Haifei Zhang, Benjamin Quost, Marie-Hélène Masson:
Cautious Decision-Making for Tree Ensembles. ECSQARU 2023: 3-14 - [c23]Vitor Martin Bordini, Sébastien Destercke, Benjamin Quost:
Learning calibrated belief functions from conformal predictions. ISIPTA 2023: 311-320 - [c22]Mathieu Randon, Benjamin Quost, Dirk von Wissel, Nassim Boudaoud:
Robust Consumption Planning from Uncertain Power Demand Predictions. IV 2023: 1-7 - 2022
- [c21]Tathagata Basu, Sébastien Destercke, Benjamin Quost:
A Robust Bayesian Estimation Approach for the Imprecise Plackett-Luce Model. IPMU (1) 2022: 757-769 - [c20]Mathieu Randon, Benjamin Quost, Nassim Boudaoud, Dirk von Wissel:
Vehicle consumption estimation via calibrated Gaussian Process regression. IV 2022: 71-76 - [c19]Haifei Zhang, Benjamin Quost, Marie-Hélène Masson:
Explications contrefactuelles pour les forêts aléatoires prudentes. LFA 2022 - [c18]Haifei Zhang, Benjamin Quost, Marie-Hélène Masson:
Forêts aléatoires prudentes : une nouvelle stratégie de décision et quelques expériences. LFA 2022 - 2021
- [c17]Benjamin Quost:
Decision-making from Partial Instances by Active Feature Querying. ISIPTA 2021: 264-272 - [c16]Haifei Zhang, Benjamin Quost, Marie-Hélène Masson:
Cautious Random Forests: a New Decision Strategy and some Experiments. ISIPTA 2021: 369-372 - 2020
- [j11]Marie-Hélène Masson, Benjamin Quost, Sébastien Destercke:
Cautious relational clustering: A thresholding approach. Expert Syst. Appl. 139 (2020) - [c15]Loïc Adam, Arthur Van Camp, Sébastien Destercke, Benjamin Quost:
Inferring from an Imprecise Plackett-Luce Model: Application to Label Ranking. SUM 2020: 98-112 - [c14]Benjamin Quost, Marie-Hélène Masson, Sébastien Destercke:
Dealing with Atypical Instances in Evidential Decision-Making. SUM 2020: 217-225
2010 – 2019
- 2019
- [e1]Nahla Ben Amor, Benjamin Quost, Martin Theobald:
Scalable Uncertainty Management - 13th International Conference, SUM 2019, Compiègne, France, December 16-18, 2019, Proceedings. Lecture Notes in Computer Science 11940, Springer 2019, ISBN 978-3-030-35513-5 [contents] - 2018
- [j10]Benjamin Quost, Sébastien Destercke:
Classification by pairwise coupling of imprecise probabilities. Pattern Recognit. 77: 412-425 (2018) - 2017
- [j9]Benjamin Quost, Thierry Denoeux, Shoumei Li:
Parametric classification with soft labels using the evidential EM algorithm: linear discriminant analysis versus logistic regression. Adv. Data Anal. Classif. 11(4): 659-690 (2017) - [j8]Dingfu Zhou, Vincent Frémont, Benjamin Quost, Yuchao Dai, Hongdong Li:
Moving object detection and segmentation in urban environments from a moving platform. Image Vis. Comput. 68: 76-87 (2017) - 2016
- [j7]Benjamin Quost, Thierry Denoeux:
Clustering and classification of fuzzy data using the fuzzy EM algorithm. Fuzzy Sets Syst. 286: 134-156 (2016) - 2015
- [c13]Abdel-Djalil Ourabah, Benjamin Quost, Atef Gayed, Thierry Denoeux:
Estimating energy consumption of a PHEV using vehicle and on-board navigation data. Intelligent Vehicles Symposium 2015: 755-760 - 2014
- [j6]Marie-Hélène Masson, Sébastien Destercke, Benjamin Quost:
Editorial. Int. J. Approx. Reason. 55(1): 275-276 (2014) - [j5]Violaine Antoine, Benjamin Quost, Marie-Hélène Masson, Thierry Denoeux:
CEVCLUS: evidential clustering with instance-level constraints for relational data. Soft Comput. 18(7): 1321-1335 (2014) - [c12]Benjamin Quost:
Logistic Regression of Soft Labeled Instances via the Evidential EM Algorithm. Belief Functions 2014: 77-86 - [c11]Dingfu Zhou, Benjamin Quost, Vincent Frémont:
Soft label based semi-supervised boosting for classification and object recognition. ICARCV 2014: 1062-1067 - [c10]Dingfu Zhou, Vincent Frémont, Benjamin Quost, Bihao Wang:
On modeling ego-motion uncertainty for moving object detection from a mobile platform. Intelligent Vehicles Symposium 2014: 1332-1338 - 2013
- [c9]Xun Wang, Benjamin Quost, Jean-Daniel Chazot, Jérôme Antoni:
Sound Source Localization from Uncertain Information Using the Evidential EM Algorithm. SUM 2013: 162-175 - 2012
- [j4]Violaine Antoine, Benjamin Quost, Marie-Hélène Masson, Thierry Denoeux:
CECM: Constrained evidential C-means algorithm. Comput. Stat. Data Anal. 56(4): 894-914 (2012) - [c8]Sébastien Destercke, Benjamin Quost:
Correcting Binary Imprecise Classifiers: Local vs Global Approach. SUM 2012: 299-310 - 2011
- [j3]Benjamin Quost, Marie-Hélène Masson, Thierry Denoeux:
Classifier fusion in the Dempster-Shafer framework using optimized t-norm based combination rules. Int. J. Approx. Reason. 52(3): 353-374 (2011) - [c7]Violaine Antoine, Benjamin Quost, Mylène Masson, Thierry Denoeux:
CEVCLUS: Constrained evidential clustering of proximity data. EUSFLAT Conf. 2011: 876-882 - [c6]Sébastien Destercke, Benjamin Quost:
Combining Binary Classifiers with Imprecise Probabilities. IUKM 2011: 219-230 - 2010
- [c5]Violaine Antoine, Benjamin Quost, Marie-Hélène Masson, Thierry Denoeux:
CECM: Adding pairwise constraints to evidential clustering. FUZZ-IEEE 2010: 1-8 - [c4]Benjamin Quost, Thierry Denoeux:
Clustering Fuzzy Data Using the Fuzzy EM Algorithm. SUM 2010: 333-346
2000 – 2009
- 2009
- [c3]Benjamin Quost, Thierry Denoeux:
Learning from data with uncertain labels by boosting credal classifiers. KDD Workshop on Knowledge Discovery from Uncertain Data 2009: 38-47 - 2008
- [j2]David Mercier, Benjamin Quost, Thierry Denoeux:
Refined modeling of sensor reliability in the belief function framework using contextual discounting. Inf. Fusion 9(2): 246-258 (2008) - [c2]Benjamin Quost, Marie-Hélène Masson, Thierry Denoeux:
Refined classifier combination using belief functions. FUSION 2008: 1-7 - 2007
- [j1]Benjamin Quost, Thierry Denoeux, Marie-Hélène Masson:
Pairwise classifier combination using belief functions. Pattern Recognit. Lett. 28(5): 644-653 (2007) - 2005
- [c1]David Mercier, Benjamin Quost, Thierry Denoeux:
Contextual Discounting of Belief Functions. ECSQARU 2005: 552-562
Coauthor Index
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