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Publications
Improving clustering quality evaluation in noisy Gaussian mixtures
Clustering is a fundamental technique in machine learning and data analysis, widely used across various domains. Internal clustering validat… (voir plus)ion measures, such as the Average Silhouette Width, Calinski-Harabasz, and Davies-Bouldin indices, play a crucial role in assessing clustering quality when external ground truth labels are unavailable. However, these measures can be affected by feature relevance, potentially leading to unreliable evaluations in high-dimensional or noisy data sets. In this paper, we introduce a Feature Importance Rescaling (FIR) method designed to enhance internal clustering validation by adjusting feature contributions based on their dispersion. Our method systematically attenuates noise features making clustering compactness and separation clearer, and by consequence aligning internal validation measures more closely with the ground truth. Through extensive experiments on synthetic data sets under different configurations, we demonstrate that FIR consistently improves the correlation between internal validation indices and the ground truth, particularly in settings with noisy or irrelevant features. The results show that FIR increases the robustness of clustering evaluation, reduces variability in performance across different data sets, and remains effective even when clusters exhibit significant overlap. These findings highlight the potential of FIR as a valuable enhancement for internal clustering validation, making it a practical tool for unsupervised learning tasks where labelled data is not available.
Clustering is a fundamental technique in machine learning and data analysis, widely used across various domains. Internal clustering validat… (voir plus)ion measures, such as the Average Silhouette Width, Calinski-Harabasz, and Davies-Bouldin indices, play a crucial role in assessing clustering quality when external ground truth labels are unavailable. However, these measures can be affected by feature relevance, potentially leading to unreliable evaluations in high-dimensional or noisy data sets. In this paper, we introduce a Feature Importance Rescaling (FIR) method designed to enhance internal clustering validation by adjusting feature contributions based on their dispersion. Our method systematically attenuates noise features making clustering compactness and separation clearer, and by consequence aligning internal validation measures more closely with the ground truth. Through extensive experiments on synthetic data sets under different configurations, we demonstrate that FIR consistently improves the correlation between internal validation indices and the ground truth, particularly in settings with noisy or irrelevant features. The results show that FIR increases the robustness of clustering evaluation, reduces variability in performance across different data sets, and remains effective even when clusters exhibit significant overlap. These findings highlight the potential of FIR as a valuable enhancement for internal clustering validation, making it a practical tool for unsupervised learning tasks where labelled data is not available.
Regression models are essential for a wide range of real-world applications. However, in practice, target values are not always precisely kn… (voir plus)own; instead, they may be represented as intervals of acceptable values. This challenge has led to the development of Interval Regression models. In this study, we provide a comprehensive review of existing Interval Regression models and introduce alternative models for comparative analysis. Experiments are conducted on both real-world and synthetic datasets to offer a broad perspective on model performance. The results demonstrate that no single model is universally optimal, highlighting the importance of selecting the most suitable model for each specific scenario.
In this paper, we argue that current safety alignment research efforts for large language models are hindered by many intertwined sources of… (voir plus) noise, such as small datasets, methodological inconsistencies, and unreliable evaluation setups. This can, at times, make it impossible to evaluate and compare attacks and defenses fairly, thereby slowing progress. We systematically analyze the LLM safety evaluation pipeline, covering dataset curation, optimization strategies for automated red-teaming, response generation, and response evaluation using LLM judges. At each stage, we identify key issues and highlight their practical impact. We also propose a set of guidelines for reducing noise and bias in evaluations of future attack and defense papers. Lastly, we offer an opposing perspective, highlighting practical reasons for existing limitations. We believe that addressing the outlined problems in future research will improve the field's ability to generate easily comparable results and make measurable progress.
