# Articles

Publications by years in reversed chronological order

## Published

### 2023

- Computing an empirical Fisher information matrix estimate in latent variable models through stochastic approximationMaud Delattre, and Estelle Kuhn
*Computo*, 2023.The Fisher information matrix (FIM) is a key quantity in statistics. However its exact computation is often not trivial. In particular in many latent variable models, it is intricated due to the presence of unobserved variables. Several methods have been proposed to approximate the FIM when it can not be evaluated analytically. Different estimates have been considered, in particular moment estimates. However some of them require to compute second derivatives of the complete data log-likelihood which leads to some disadvantages. In this paper, we focus on the empirical Fisher information matrix defined as an empirical estimate of the covariance matrix of the score, which only requires to compute the first derivatives of the log-likelihood. Our contribution consists in presenting a new numerical method to evaluate this empirical Fisher information matrix in latent variable model when the proposed estimate can not be directly analytically evaluated. We propose a stochastic approximation estimation algorithm to compute this estimate as a by-product of the parameter estimate. We evaluate the finite sample size properties of the proposed estimate and the convergence properties of the estimation algorithm through simulation studies.

Model-based standard error, moment estimate, Fisher identity, stochastic approximation algorithm

- Inference of Multiscale Gaussian Graphical ModelEdmond Sanou, Christophe Ambroise, and Geneviève Robin
*Computo*, 2023.Gaussian Graphical Models (GGMs) are widely used in high-dimensional data analysis to synthesize the interaction between variables. In many applications, such as genomics or image analysis, graphical models rely on sparsity and clustering to reduce dimensionality and improve performances. This paper explores a slightly different paradigm where clustering is not knowledge-driven but performed simultaneously with the graph inference task. We introduce a novel Multiscale Graphical Lasso (MGLasso) to improve networks interpretability by proposing graphs at different granularity levels. The method estimates clusters through a convex clustering approach — a relaxation of k-means, and hierarchical clustering. The conditional independence graph is simultaneously inferred through a neighborhood selection scheme for undirected graphical models. MGLasso extends and generalizes the sparse group fused lasso problem to undirected graphical models. We use continuation with Nesterov smoothing in a shrinkage-thresholding algorithm (CONESTA) to propose a regularization path of solutions along the group fused Lasso penalty, while the Lasso penalty is kept constant. Extensive experiments on synthetic data compare the performances of our model to state-of-the-art clustering methods and network inference models. Applications to gut microbiome data and poplar’s methylation mixed with transcriptomic data are presented.

Neighborhood selection, Convex hierarchical clustering, Gaussian graphical models

- Macrolitter Video Counting on Riverbanks Using State Space Models and Moving CamerasMathis Chagneux, Sylvain Le Corff, Pierre Gloaguen, Charles Ollion, Océane Lepâtre, and Antoine Bruge
*Computo*, 2023.Litter is a known cause of degradation in marine environments and most of it travels in rivers before reaching the oceans. In this paper, we present a novel algorithm to assist waste monitoring along watercourses. While several attempts have been made to quantify litter using neural object detection in photographs of floating items, we tackle the more challenging task of counting directly in videos using boat-embedded cameras. We rely on multi-object tracking (MOT) but focus on the key pitfalls of false and redundant counts which arise in typical scenarios of poor detection performance. Our system only requires supervision at the image level and performs Bayesian filtering via a state space model based on optical flow. We present a new open image dataset gathered through a crowdsourced campaign and used to train a center-based anchor-free object detector. Realistic video footage assembled by water monitoring experts is annotated and provided for evaluation. Improvements in count quality are demonstrated against systems built from state-of-the-art multi-object trackers sharing the same detection capabilities. A precise error decomposition allows clear analysis and highlights the remaining challenges.

- A Python Package for Sampling from Copulae: claytonAlexis Boulin
*Computo*, 2023.The package clayton is designed to be intuitive, user-friendly, and efficient. It offers a wide range of copula models, including Archimedean, Elliptical, and Extreme. The package is implemented in pure Python, making it easy to install and use. In addition, we provide detailed documentation and examples to help users get started quickly. We also conduct a performance comparison with existing R packages, demonstrating the efficiency of our implementation. The clayton package is a valuable tool for researchers and practitioners working with copulae in Python

Copulae, Random number generation

### 2022

- Trade-off between deep learning for species identification and inference about predator-prey co-occurrence: Reproducible R workflow integrating models in computer vision and ecological statisticsOlivier Gimenez, Maelis Kervellec, Jean-Baptiste Fanjul, Anna Chaine, Lucile Marescot, Yoann Bollet, and Christophe Duchamp
*Computo*, 2022.Deep learning is used in computer vision problems with important applications in several scientific fields. In ecology for example, there is a growing interest in deep learning for automatizing repetitive analyses on large amounts of images, such as animal species identification. However, there are challenging issues toward the wide adoption of deep learning by the community of ecologists. First, there is a programming barrier as most algorithms are written in Python while most ecologists are versed in R. Second, recent applications of deep learning in ecology have focused on computational aspects and simple tasks without addressing the underlying ecological questions or carrying out the statistical data analysis to answer these questions. Here, we showcase a reproducible R workflow integrating both deep learning and statistical models using predator-prey relationships as a case study. We illustrate deep learning for the identification of animal species on images collected with camera traps, and quantify spatial co-occurrence using multispecies occupancy models. Despite average model classification performances, ecological inference was similar whether we analysed the ground truth dataset or the classified dataset. This result calls for further work on the trade-offs between time and resources allocated to train models with deep learning and our ability to properly address key ecological questions with biodiversity monitoring. We hope that our reproducible workflow will be useful to ecologists and applied statisticians.

computer vision, deep-learning, species distribution modeling, ecological statistics

## In the pipeline

Manuscript conditionally accepted, whose editorial and scientific reproducibility is being validated

- Local tree methods for classification: a review and some dead endsAlice Cleynen, Louis Raynal, and Jean-Michel Marin
*Computo*, 2023.Random Forests (RF) [@breiman:2001] are very popular machine learning methods. They perform well even with little or no tuning, and have some theoretical guarantees, especially for sparse problems [@biau:2012;@scornet:etal:2015]. These learning strategies have been used in several contexts, also outside the field of classification and regression. To perform Bayesian model selection in the case of intractable likelihoods, the ABC Random Forests (ABC-RF) strategy of @pudlo:etal:2016 consists in applying Random Forests on training sets composed of simulations coming from the Bayesian generative models. The ABC-RF technique is based on an underlying RF for which the training and prediction phases are separated. The training phase does not take into account the data to be predicted. This seems to be suboptimal as in the ABC framework only one observation is of interest for the prediction. In this paper, we study tree-based methods that are built to predict a specific instance in a classification setting. This type of methods falls within the scope of local (lazy/instance-based/case specific) classification learning. We review some existing strategies and propose two new ones. The first consists in modifying the tree splitting rule by using kernels, the second in using a first RF to compute some local variable importance that is used to train a second, more local, RF. Unfortunately, these approaches, although interesting, do not provide conclusive results.

## Under review

At the moment, 6 manuscripts are under review.

## Example: a mock contribution

This page is a reworking of the original t-SNE article using the Computo template. It aims to help authors submitting to the journal by using some advanced formatting features.

- Visualizing Data using t-SNE: practical Computo exampleLaurens Maaten, and Geoffrey Hinton
*Computo*, 2021.We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding hinton:stochastic that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large data sets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of data sets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualization produced by t-SNE are significantly better than those produced by other techniques on almost all of the data sets.

template, documentation, quarto, R, python