Using supervised machine learning to classify real alerts and artifact in online multisignal vital sign monitoring data L Chen, A Dubrawski, D Wang, M Fiterau, M Guillame-Bert, E Bose, ... Critical care medicine 44 (7), e456-e463, 2016 | 83 | 2016 |
Learning temporal association rules on symbolic time sequences M Guillame-Bert, JL Crowley Asian conference on machine learning, 159-174, 2012 | 41 | 2012 |
Learning temporal rules to forecast instability in continuously monitored patients M Guillame-Bert, A Dubrawski, D Wang, M Hravnak, G Clermont, ... Journal of the American Medical Informatics Association 24 (1), 47-53, 2017 | 30 | 2017 |
Classification of time sequences using graphs of temporal constraints M Guillame-Bert, A Dubrawski Journal of Machine Learning Research 18 (121), 1-34, 2017 | 27 | 2017 |
Data-driven classification of screwdriving operations RM Aronson, A Bhatia, Z Jia, M Guillame-Bert, D Bourne, A Dubrawski, ... 2016 International Symposium on Experimental Robotics, 244-253, 2017 | 16 | 2017 |
Predicting home service demands from appliance usage data K Basu, M Guillame-Bert, H Joumaa, S Ploix, J Crowley International conference on information and communication technologies and …, 2011 | 15 | 2011 |
New approach on temporal data mining for symbolic time sequences: Temporal tree associate rules M Guillame-Bert, JL Crowley 2011 IEEE 23rd International Conference on Tools with Artificial …, 2011 | 14 | 2011 |
First-order logic learning in artificial neural networks M Guillame-Bert, K Broda, AA Garcez The 2010 International Joint Conference on Neural Networks (IJCNN), 1-8, 2010 | 11 | 2010 |
Increasing cardiovascular data sampling frequency and referencing it to baseline improve hemorrhage detection A Wertz, AL Holder, M Guillame-Bert, G Clermont, A Dubrawski, ... Critical Care Explorations 1 (10), e0058, 2019 | 9 | 2019 |
Exact distributed training: Random forest with billions of examples M Guillame-Bert, O Teytaud arXiv preprint arXiv:1804.06755, 2018 | 8 | 2018 |
Artifact patterns in continuous noninvasive monitoring of patients M Hravnak, L Chen, E Bose, M Fiterau, M Guillame-Bert, A Dubrawski, ... Intensive care medicine 39 (Suppl 2), S405, 2013 | 8 | 2013 |
Generative trees: Adversarial and copycat R Nock, M Guillame-Bert arXiv preprint arXiv:2201.11205, 2022 | 7 | 2022 |
Learning temporal rules to forecast events in multivariate time sequences M Guillame-Bert, A Dubrawski 2nd Workshop on Machine Learning for Clinical Data Analysis, Healthcare and …, 2014 | 7 | 2014 |
Yggdrasil decision forests: A fast and extensible decision forests library M Guillame-Bert, S Bruch, R Stotz, J Pfeifer Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 6 | 2023 |
Introducing TensorFlow decision forests M Guillame-Bert, S Bruch, J Gordon, J Pfeifer Online]. URL: https://blog. tensorflow. org/2021/05/introducing-tensorflow …, 2021 | 5 | 2021 |
Learning representations for axis-aligned decision forests through input perturbation S Bruch, J Pfeifer, M Guillame-Bert arXiv preprint arXiv:2007.14761, 2020 | 4 | 2020 |
Systems and Methods for Distributed Generation of Decision Tree-Based Models M Guillame-bert, O Teytaud US Patent App. 16/271,064, 2019 | 4 | 2019 |
Learning temporal rules to forecast instability in intensive care patients M Guillame-Bert, A Dubrawski, L Chen, M Hravnak, M Pinsky, G Clermont Intensive care medicine 39 (Suppl 2), S470, 2013 | 4 | 2013 |
Utility of empirical models of hemorrhage in detecting and quantifying bleeding M Guillame-Bert, A Dubrawski, L Chen, A Holder, MR Pinsky, G Clermont Intensive Care Medicine 40, S287-S287, 2014 | 3 | 2014 |
Planning with inaccurate temporal rules M Guillame-Bert, JL Crowley 2012 IEEE 24th International Conference on Tools with Artificial …, 2012 | 3 | 2012 |