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ORCIDMathieu Guillame-Bert
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2020 – today
- 2024
[i9]Mathieu Guillame-Bert, Richard Nock:
Boosting gets full Attention for Relational Learning. CoRR abs/2402.14926 (2024)- 2023
- [c7]Mathieu Guillame-Bert
, Sebastian Bruch, Richard Stotz, Jan Pfeifer:
Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library. KDD 2023: 4068-4077 - [i8]Richard Nock, Mathieu Guillame-Bert:
Generative Forests. CoRR abs/2308.03648 (2023) - 2022
- [c6]Richard Nock, Mathieu Guillame-Bert:
Generative Trees: Adversarial and Copycat. ICML 2022: 16906-16951 - [i7]Richard Nock, Mathieu Guillame-Bert:
Generative Trees: Adversarial and Copycat. CoRR abs/2201.11205 (2022) - [i6]Mathieu Guillame-Bert, Sebastian Bruch, Richard Stotz, Jan Pfeifer:
Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library. CoRR abs/2212.02934 (2022) - 2020
- [i5]Sebastian Bruch, Jan Pfeifer, Mathieu Guillame-Bert:
Learning Representations for Axis-Aligned Decision Forests through Input Perturbation. CoRR abs/2007.14761 (2020) - [i4]Mathieu Guillame-Bert, Sebastian Bruch, Petr Mitrichev, Petr Mikheev, Jan Pfeifer:
Modeling Text with Decision Forests using Categorical-Set Splits. CoRR abs/2009.09991 (2020)
2010 – 2019
- 2018
- [i3]Mathieu Guillame-Bert, Olivier Teytaud:
Exact Distributed Training: Random Forest with Billions of Examples. CoRR abs/1804.06755 (2018) - 2017
- [j2]Mathieu Guillame-Bert, Artur Dubrawski, Donghan Wang, Marilyn Hravnak, Gilles Clermont, Michael R. Pinsky:
Learning temporal rules to forecast instability in continuously monitored patients. J. Am. Medical Informatics Assoc. 24(1): 47-53 (2017) - [j1]Mathieu Guillame-Bert, Artur Dubrawski:
Classification of Time Sequences using Graphs of Temporal Constraints. J. Mach. Learn. Res. 18: 121:1-121:34 (2017) - 2016
- [c5]Reuben M. Aronson, Ankit Bhatia, Zhenzhong Jia, Mathieu Guillame-Bert, David A. Bourne, Artur Dubrawski, Matthew T. Mason:
Data-Driven Classification of Screwdriving Operations. ISER 2016: 244-253 - [i2]Mathieu Guillame-Bert, Artur Dubrawski:
Batched Lazy Decision Trees. CoRR abs/1603.02578 (2016) - [i1]Mathieu Guillame-Bert:
Honey: A dataflow programming language for the processing, featurization and analysis of multivariate, asynchronous and non-uniformly sampled scalar symbolic time sequences. CoRR abs/1609.03146 (2016) - 2012
- [b1]Mathieu Guillame-Bert:
Apprentissage de règles associatives temporelles pour les séquences temporelles de symboles. (Learning temporal association rules on Symbolic time sequences). Grenoble Alpes University, France, 2012 - [c4]Mathieu Guillame-Bert, James L. Crowley:
Planning with Inaccurate Temporal Rules. ICTAI 2012: 492-499 - [c3]Mathieu Guillame-Bert, James L. Crowley:
Learning Temporal Association Rules on Symbolic Time Sequences. ACML 2012: 159-174 - 2011
- [c2]Mathieu Guillame-Bert, James L. Crowley:
New Approach on Temporal Data Mining for Symbolic Time Sequences: Temporal Tree Associate Rules. ICTAI 2011: 748-752 - 2010
- [c1]Mathieu Guillame-Bert, Krysia Broda, Artur S. d'Avila Garcez:
First-order logic learning in Artificial Neural Networks. IJCNN 2010: 1-8
Coauthor Index
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