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Université de Toulouse

Academic institutioneurope · fr
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Research library319linked papers
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Selected work

Representative Papers

Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design

Apr 11, 2025

In multidisciplinary design optimization—particularly aircraft design—the presence of high-dimensional mixed variables (continuous, integer, and categorical) causes combinatorial explosion in the hyperparameter space of Bayesian optimization surrogate models. To address this, we propose a Partial Least Squares (PLS)-based adaptive dimensionality reduction framework. It dynamically learns variable coupling structures to significantly compress the surrogate hyperparameter space, while integrating mixed-variable encoding with an adaptive hyperparameter selection mechanism to balance modeling accuracy and tuning efficiency. Evaluated on analytical benchmarks and two real-world aircraft design cases, our method achieves >30% faster convergence and improves optimal solution quality by 12–18% compared to genetic algorithms, while reducing hyperparameter count by ~60%. Our core contribution is the first application of PLS for hyperparameter pruning in Bayesian optimization, enabling efficient and robust optimization under high-dimensional mixed-variable settings.

8 citations3 influentialRead paper

Constrained Multi-Objective Bayesian Optimization with Application to Aircraft Design

Jun 20, 2022AIAA AVIATION 2022 Forum

Bayesian optimization (BO) methods for computationally expensive, nonlinearly constrained multi-objective optimization problems—such as aircraft conceptual design—often suffer from ill-conditioning in multi-objective acquisition functions, leading to unstable surrogate updates and poor convergence. Method: This paper extends the SEGOMOE framework by introducing a novel regularization mechanism directly into the multi-objective acquisition function, synergistically integrating Kriging surrogates, a Mixture-of-Experts (MoE) architecture, and an enhanced SEGO algorithm. Contribution/Results: The proposed approach systematically alleviates the trade-off between ill-conditioning and convergence in constrained multi-objective BO. Empirical evaluation on aircraft design tasks demonstrates that it achieves high-quality Pareto fronts using only 5% of the function evaluations required by NSGA-II, significantly improving the efficiency of identifying high-fidelity, low-cost compromise solutions.

8 citationsRead paper

Revisiting the MIMIC-IV Benchmark: Experiments Using Language Models for Electronic Health Records

Apr 29, 2025CL4HEALTH

The absence of standardized, text-based electronic health record (EHR) evaluation benchmarks impedes fair comparison and deployment of large language models (LLMs) in clinical downstream tasks. To address this, we introduce the first open-source, Hugging Face–native textual EHR benchmark—systematically transforming MIMIC-IV’s structured clinical data into natural-language sequences via template engineering. The benchmark supports zero-shot prompting, supervised fine-tuning (e.g., Llama-3, Phi-3), and comparison with traditional models. Our key contributions include the first standardized, open-access textual reconstruction of MIMIC-IV and its integration into the Hugging Face ecosystem. Empirical results show that fine-tuned textual models achieve an AUC of 0.86 on in-hospital mortality prediction—matching state-of-the-art tabular models (XGBoost, logistic regression)—thereby validating the viability of the textual pathway. In contrast, zero-shot LLMs underperform markedly (AUC < 0.6), underscoring the critical impact of domain-specific adaptation and data representation on LLM efficacy in healthcare.

2 citationsRead paper

Augmenting the availability of historical GDP per capita estimates through machine learning

Sep 16, 2024Proceedings of the National Academy of Sciences of the United States of America

This study addresses the longstanding gap in long-term per capita GDP estimates for hundreds of regions across Europe and North America over the past 700 years. Method: It innovatively employs large-scale biographical data—encoding birthplace, occupation, and social status—as proxy variables to train a supervised machine learning regression model. The approach integrates high-dimensional text feature engineering with cross-regional extrapolation techniques. Contribution/Results: The model achieves an out-of-sample R² of 0.90 and generates high-accuracy, regionally granular, multi-century per capita GDP series. Relative to existing datasets, it quadruples the volume of historical GDP estimates. Validated against multiple economic proxies—including urbanization rates, average stature, and subjective well-being—the estimates robustly replicate well-documented macrohistorical patterns, such as the North–South European economic reversal and the growth-enhancing role of Atlantic port cities. This work substantially expands both the empirical foundation and methodological toolkit for long-run macroeconomic analysis.

2 citationsRead paper

Surrogate-Based Optimization of System Architectures Subject to Hidden Constraints

Jul 27, 2024AIAA AVIATION FORUM AND ASCEND 2024

This work addresses the challenge of implicit constraints—manifested as evaluation failures—arising from unreliable physics-based simulations in system architecture optimization. To tackle this, we propose a surrogate modeling framework that integrates probabilistic feasibility prediction with Bayesian optimization. Methodologically, we introduce a novel hybrid discrete Gaussian process to model the Probability of Validity (PoV), coupled with an interior-point selection strategy based on a minimum PoV threshold; the framework natively supports hierarchical design variables and multi-objective optimization. Our approach achieves the first successful solution for a jet engine architecture optimization task with a 50% simulation failure rate. Across multiple synthetic benchmarks and real-world case studies, it significantly improves convergence robustness and optimization success rate. The implementation is publicly available as the SBArchOpt Python library.

2 citationsRead paper
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