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Gilles Louppe - Publications ORBI
Louppe, G. (2015, April 03). Tree models with Scikit-Learn: Great models with little assumptions. Paper presented at PyData Paris 2015, Paris, France.
This talk gives an introduction to tree-based methods, both from a theoretical and practical point of view. It covers decision trees, random forests and boosting estimators, along with concrete examples based ...
McGovern, A., Gagne II, D. J., Eustaquio, L., Titericz, G., Lazorthes, B., Zhang, O., Louppe, G., Prettenhofer, P., Basara, J., Hamill, T. M., & Margolin, D. (2015). Solar Energy Prediction: An International Contest to Initiate Interdisciplinary Research on Compelling Meteorological Problems. Bulletin of the American Meteorological Society.
Peer reviewed (verified by ORBi)
As meteorological observing systems and models grow in complexity and number, the size of the data becomes overwhelming for humans to analyze using traditional techniques. Computer scientists, and specifically ...
Louppe, G. (2014). Understanding Random Forests: From Theory to Practice. Unpublished doctoral thesis, Université de Liège, ​Liège, ​​Belgique.
Data analysis and machine learning have become an integrative part of the modern scientific methodology, offering automated procedures for the prediction of a phenomenon based on past observations, unraveling ...
Louppe, G. (2014, August 29). Accelerating Random Forests in Scikit-Learn. Paper presented at EuroScipy 2014, Cambridge, UK.
Peer reviewed
Random Forests are without contest one of the most robust, accurate and versatile tools for solving machine learning tasks. Implementing this algorithm properly and efficiently remains however a challenging ...
Sutera, A., Joly, A., François-Lavet, V., Qiu, Z., Louppe, G., Ernst, D., & Geurts, P. (2014). Simple connectome inference from partial correlation statistics in calcium imaging. In J., Soriano, D., Battaglia, I., Guyon, V., Lemaire, J., Orlandi, & B., Ray (Eds.), Neural Connectomics Challenge. Springer.
Peer reviewed
In this work, we propose a simple yet effective solution to the problem of connectome inference in calcium imaging data. The proposed algorithm consists of two steps. First, processing the raw signals to ...
Marée, R., Rollus, L., Stevens, B., Louppe, G., Caubo, O., Rocks, N., Bekaert, S., Cataldo, D., & Wehenkel, L. (2014). A hybrid human-computer approach for large-scale image-based measurements using web services and machine learning. Proceedings IEEE International Symposium on Biomedical Imaging. IEEE.
Peer reviewed
We present a novel methodology combining web-based software development practices, machine learning, and spatial databases for computer-aided quantification of regions of interest (ROIs) in large-scale ...
Botta, V., Louppe, G., Geurts, P., & Wehenkel, L. (2014, April 02). Exploiting SNP Correlations within Random Forest for Genome-Wide Association Studies. PLoS ONE.
Peer reviewed (verified by ORBi)
The primary goal of genome-wide association studies (GWAS) is to discover variants that could lead, in isolation or in combination, to a particular trait or disease. Standard approaches to GWAS, however, are ...
Prettenhofer, P., & Louppe, G. (2014, February 23). Gradient Boosted Regression Trees in Scikit-Learn. Paper presented at PyData 2014, London, UK.
This talk describes Gradient Boosted Regression Trees (GBRT), a powerful statistical learning technique with applications in a variety of areas, ranging from web page ranking to environmental niche modeling ...
Louppe, G., & Prettenhofer, P. (2014, February 05). Forecasting Daily Solar Energy Production Using Robust Regression Techniques. Paper presented at 94th American Meteorological Society Annual Meeting, Atlanta, USA.
We describe a novel approach to forecast daily solar energy production based on the output of a numerical weather prediction (NWP) model using non-parametric robust regression techniques. Our approach ...
Joly, A., & Louppe, G. (2014, January 27). Scikit-Learn: Machine Learning in the Python ecosystem. Poster session presented at GIGA DAY 2014, Liège, Belgium.
The scikit-learn project is an increasingly popular machine learning library written in Python. It is designed to be simple and efficient, useful to both experts and non-experts, and reusable in a variety of ...
Louppe, G., & Varoquaux, G. (2013, December 10). Scikit-Learn: Machine Learning in the Python ecosystem. Paper presented at NIPS 2013 Workshop on Machine Learning Open Source Software.
Peer reviewed
The scikit-learn project is an increasingly popular machine learning library written in Python. It is designed to be simple and efficient, useful to both experts and non-experts, and reusable in a variety of ...
Louppe, G., Wehenkel, L., Sutera, A., & Geurts, P. (2013). Understanding variable importances in forests of randomized trees. Advances in Neural Information Processing Systems 26.
Peer reviewed
Despite growing interest and practical use in various scientific areas, variable importances derived from tree-based ensemble methods are not well understood from a theoretical point of view. In this work we ...
Louppe, G., & Geurts, P. (2012). Ensembles on Random Patches. Machine Learning and Knowledge Discovery in Databases. Berlin: Springer-Verlag.
Peer reviewed
In this paper, we consider supervised learning under the assumption that the available memory is small compared to the dataset size. This general framework is relevant in the context of big data, distributed ...
Geurts, P., & Louppe, G. (2011). Learning to rank with extremely randomized trees. JMLR: Workshop and Conference Proceedings, 14, 49-61.
Peer reviewed
In this paper, we report on our experiments on the Yahoo! Labs Learning to Rank challenge organized in the context of the 23rd International Conference of Machine Learning (ICML 2010). We competed in both the ...
Louppe, G., & Geurts, P. (2010, December 11). A zealous parallel gradient descent algorithm. Poster session presented at NIPS 2010 Workshop on Learning on Cores, Clusters and Clouds, Whistler, Canada.
Peer reviewed
Parallel and distributed algorithms have become a necessity in modern machine learning tasks. In this work, we focus on parallel asynchronous gradient descent and propose a zealous variant that minimizes the ...
Louppe, G. (2010). Collaborative filtering: Scalable approaches using restricted Boltzmann machines. Unpublished master thesis, Université de Liège, ​Liège, ​​Belgique.
Parallel to the growth of electronic commerce, recommender systems have become a very active area of research, both in the industry and in the academic world. The goal of these systems is to make automatic but ...