arXiv subjects

Alexandre Chaussard

Publications and source records attributed to Alexandre Chaussard.

3 recordsLinked to original sources

arXiv2026

Independent Component Discovery in Temporal Count Data

Advances in data collection are producing growing volumes of temporal count observations, making adapted modeling increasingly necessary. In this work, we introduce a generative framework for independent component analysis of temporal count data, combining regime-adaptive dynamics with Poisson log-normal emissions. The model identifies disentangled components with regime-dependent contributions, enabling representation learning and perturbations analysis. Notably, we establish the identifiability of the model, supporting principled interpretation. To learn the parameters, we propose an efficient amortized variational inference procedure. Experiments on simulated data evaluate recovery of the mixing function and latent sources across diverse settings, while real-world applications to gut microbiome and climate datasets reveal co-variation patterns and regime shifts consistent with domain-specific knowledge.

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arXiv2025

TaxaPLN: a taxonomy-aware augmentation strategy for microbiome-trait classification including metadata

The gut microbiome plays a crucial role in human health, making it a corner stone of modern biomedical research. To study its structure and dynamics, machine learning models are increasingly used to identify key microbial patterns associated with disease and environmental factors. However, microbiome data present unique challenges due to their compositionality, high-dimensionality, sparsity, and high variability, which can obscure meaningful signals. Besides, the effectiveness of machine learning models is often constrained by limited sample sizes, as microbiome data collection remains costly and time consuming. In this context, data augmentation has emerged as a promising strategy to enhance model robustness and predictive performance by generating artificial microbiome data. The aim of this study is to improve predictive modeling from microbiome data by introducing a model-based data augmentation approach that incorporates both taxonomic relationships and covariate information. To that end, we propose TaxaPLN, a data augmentation method built on PLN-Tree generative models, which leverages the taxonomy and a data-driven sampler to generate realistic synthetic microbiome compositions. We further introduce a conditional extension based on feature-wise linear modulation, enabling covariate-aware generation. Experiments on high-quality curated microbiome datasets show that TaxaPLN preserves ecological properties and generally improves or maintains predictive performances, particularly with non-linear classifiers, outperforming state-of-the-art baselines. Besides, TaxaPLN conditional augmentation establishes a novel benchmark for covariate-aware microbiome augmentation. The MIT-licensed source code is available at https://github.com/ AlexandreChaussard/PLNTree-package along with the datasets used in our experiments.

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arXiv2024

Tree-based variational inference for Poisson log-normal models

When studying ecosystems, hierarchical trees are often used to organize entities based on proximity criteria, such as the taxonomy in microbiology, social classes in geography, or product types in retail businesses, offering valuable insights into entity relationships. Despite their significance, current count-data models do not leverage this structured information. In particular, the widely used Poisson log-normal (PLN) model, known for its ability to model interactions between entities from count data, lacks the possibility to incorporate such hierarchical tree structures, limiting its applicability in domains characterized by such complexities. To address this matter, we introduce the PLN-Tree model as an extension of the PLN model, specifically designed for modeling hierarchical count data. By integrating structured variational inference techniques, we propose an adapted training procedure and establish identifiability results, enhancing both theoretical foundations and practical interpretability. Experiments on synthetic datasets and human gut microbiome data highlight generative improvements when using PLN-Tree, demonstrating the practical interest of knowledge graphs like the taxonomy in microbiome modeling. Additionally, we present a proof-of-concept implication of the identifiability results by illustrating the practical benefits of using identifiable features for classification tasks, showcasing the versatility of the framework.

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