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Gene networks inference using dynamic Bayesian networks
- B. Perrin, L. Ralaivola, Aurélien Mazurie, S. Bottani, J. Mallet, Florence d'Alché-Buc
- Computer ScienceECCB
- 27 September 2003
This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach that can be described as a dynamic Bayesian network particularly well suited to tackle the stochastic nature of gene regulation and gene expression measurement.
Support Vector Machines based on a semantic kernel for text categorization
- Georgios Siolas, Florence d'Alché-Buc
- Computer ScienceProceedings of the IEEE-INNS-ENNS International…
- 24 July 2000
We propose to solve a text categorization task using a new metric between documents, based on a priori semantic knowledge about words. This metric can be incorporated into the definition of radial…
Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological networks inference
This framework applies Unscented Kalman Filtering (UKF) to the estimation of both parameters and hidden variables of non-linear state-space models of biological networks models and gives rise to simple and fast estimation algorithms.
Boosting is generalized to this task within the optimization framework of MarginBoost and the margin definition is extended to unlabeled data and the gradient descent algorithm is developed that corresponds to the resulting margin cost function.
Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)
- Joelle Pineau, Philippe Vincent-Lamarre, H. Larochelle
- Computer ScienceJ. Mach. Learn. Res.
- 27 March 2020
The program contained three components: a code submission policy, a community-wide reproducibility challenge, and the inclusion of the Machine Learning Reproducibility checklist as part of the paper submission process, which was deployed and described.
Kernelizing the output of tree-based methods
The resulting algorithm, called output kernel trees (OK3), generalizes classification and regression trees as well as tree-based ensemble methods in a principled way and inherits several features of these methods such as interpretability, robustness to irrelevant variables, and input scalability.
Joint quantile regression in vector-valued RKHSs
A novel framework for estimating and predicting simultaneously several conditional quantiles is introduced that leverages kernel-based multi-task learning to curb the embarrassing phenomenon of quantile crossing, with a one-step estimation procedure and no post-processing.
RAR/RXR binding dynamics distinguish pluripotency from differentiation associated cis-regulatory elements
The data offer an unprecedentedly detailed view on the action of RA in triggering pluripotent cell differentiation and demonstrate that RAR/RXR action is mediated via two different sets of regulatory regions tightly associated with cell differentiation status.
Input Output Kernel Regression: Supervised and Semi-Supervised Structured Output Prediction with Operator-Valued Kernels
This paper introduces a novel approach, called Input Output Kernel Regression (IOKR), for learning mappings between structured inputs and structured outputs and derives an extension of Generalized Cross Validation for model selection in the case of the least-square model.
Incremental Support Vector Machine Learning: A Local Approach
A new on-line algorithm for learning a SVM based on Radial Basis Function Kernel: Local Incremental Learning of SVM or LISVM, which exploits the "locality" of RBF kernels to update current machine by only considering a subset of support candidates in the neighbourhood of the input.