Academic on João Fonsecahttps://jfonseca.eu/tags/academic/Recent content in Academic on João FonsecaHugo -- 0.147.2enThu, 01 Jan 2026 00:00:00 +0000ExplainerPFN: Towards tabular foundation models for model-free zero-shot feature importance estimationshttps://jfonseca.eu/paper/explainerpfn-towards-tabular-foundation-models-fo/Thu, 01 Jan 2026 00:00:00 +0000https://jfonseca.eu/paper/explainerpfn-towards-tabular-foundation-models-fo/Computing the importance of features in supervised classification tasks is critical for model interpretability. Shapley values are a widely used approach fMixture-of-Expert Blocks Contain Strong Hallucination Detection Signalshttps://jfonseca.eu/paper/mixture-of-expert-blocks-contain-strong-hallucinat/Thu, 01 Jan 2026 00:00:00 +0000https://jfonseca.eu/paper/mixture-of-expert-blocks-contain-strong-hallucinat/Despite their widespread use, Large Language Models (LLMs) remain limited by a fundamental problem: the generation of plausible but false content, known asRelShap: Relationally Consistent Shapley Explanationshttps://jfonseca.eu/paper/relshap-relationally-consistent-shapley-explanati/Thu, 01 Jan 2026 00:00:00 +0000https://jfonseca.eu/paper/relshap-relationally-consistent-shapley-explanati/Machine learning pipelines commonly flatten relational data into single-table representations, discarding structural constraints. Widely used Shapley valueHow Much Effort Is Enough? Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunityhttps://jfonseca.eu/paper/how-much-effort-is-enough-fairness-in-algorithmic/Wed, 01 Jan 2025 00:00:00 +0000https://jfonseca.eu/paper/how-much-effort-is-enough-fairness-in-algorithmic/Algorithmic recourse, or enabling individuals to reverse a negative outcome, has gained attention as a means of supporting human agency in interactions witSAFENUDGE: Safeguarding Large Language Models in Real-time with Tunable Safety-Performance Trade-offshttps://jfonseca.eu/paper/safenudge-safeguarding-large-language-models-in-r/Wed, 01 Jan 2025 00:00:00 +0000https://jfonseca.eu/paper/safenudge-safeguarding-large-language-models-in-r/Large Language Models (LLMs) have been shown to be susceptible to jailbreak attacks, or adversarial attacks used to illicit high risk behavior from a modelSHAP-based Explanations are Sensitive to Feature Representationhttps://jfonseca.eu/paper/shap-based-explanations-are-sensitive-to-feature-r/Wed, 01 Jan 2025 00:00:00 +0000https://jfonseca.eu/paper/shap-based-explanations-are-sensitive-to-feature-r/Local feature-based explanations are a key component of the XAI toolkit. These explanations compute feature importance values relative to an “interpretableShaRP: Explaining Rankings and Preferences with Shapley Valueshttps://jfonseca.eu/paper/sharp-explaining-rankings-and-preferences-with-sh/Wed, 01 Jan 2025 00:00:00 +0000https://jfonseca.eu/paper/sharp-explaining-rankings-and-preferences-with-sh/Algorithmic decisions in critical domains such as hiring, college admissions, and lending are often based on rankings. Given the impact of these decisionsThe Game Of Recourse: Simulating Algorithmic Recourse over Time to Improve Its Reliability and Fairnesshttps://jfonseca.eu/paper/the-game-of-recourse-simulating-algorithmic-recou/Mon, 01 Jan 2024 00:00:00 +0000https://jfonseca.eu/paper/the-game-of-recourse-simulating-algorithmic-recou/Algorithmic recourse, or providing recommendations to individuals who receive an unfavorable outcome from an algorithmic system on how they can take actionGeometric SMOTE for imbalanced datasets with nominal and continuous featureshttps://jfonseca.eu/paper/geometric-smote-for-imbalanced-datasets-with-nomin/Sun, 01 Jan 2023 00:00:00 +0000https://jfonseca.eu/paper/geometric-smote-for-imbalanced-datasets-with-nomin/Imbalanced learning can be addressed in 3 different ways: Resampling, algorithmic modifications and cost-sensitive solutions. Resampling, and specificallyImproving Active Learning Performance through the Use of Data Augmentationhttps://jfonseca.eu/paper/improving-active-learning-performance-through-the/Sun, 01 Jan 2023 00:00:00 +0000https://jfonseca.eu/paper/improving-active-learning-performance-through-the/Active learning (AL) is a well-known technique to optimize data usage in training, through the interactive selection of unlabeled observations, out of a laSetting the Right Expectations: Algorithmic Recourse Over Timehttps://jfonseca.eu/paper/setting-the-right-expectations-algorithmic-recour/Sun, 01 Jan 2023 00:00:00 +0000https://jfonseca.eu/paper/setting-the-right-expectations-algorithmic-recour/Algorithmic systems are often called upon to assist in high-stakes decision making. In light of this, algorithmic recourse, the principle wherein individuaTabular and latent space synthetic data generation: a literature reviewhttps://jfonseca.eu/paper/tabular-and-latent-space-synthetic-data-generation/Sun, 01 Jan 2023 00:00:00 +0000https://jfonseca.eu/paper/tabular-and-latent-space-synthetic-data-generation/The generation of synthetic data can be used for anonymization, regularization, oversampling, semi-supervised learning, self-supervised learning, and severThe Role of Synthetic Data in Improving Supervised Learning Methods: The Case of Land Use/Land Cover Classificationhttps://jfonseca.eu/paper/the-role-of-synthetic-data-in-improving-supervised/Sun, 01 Jan 2023 00:00:00 +0000https://jfonseca.eu/paper/the-role-of-synthetic-data-in-improving-supervised/Paper about The Role of Synthetic Data in Improving SupervisedResearch trends and applications of data augmentation algorithmshttps://jfonseca.eu/paper/research-trends-and-applications-of-data-augmentat/Sat, 01 Jan 2022 00:00:00 +0000https://jfonseca.eu/paper/research-trends-and-applications-of-data-augmentat/In the Machine Learning research community, there is a consensus regarding the relationship between model complexity and the required amount of data and coImproving Imbalanced Land Cover Classification with K-Means SMOTE: Detecting and Oversampling Distinctive Minority Spectral Signatureshttps://jfonseca.eu/paper/improving-imbalanced-land-cover-classification-wit/Fri, 01 Jan 2021 00:00:00 +0000https://jfonseca.eu/paper/improving-imbalanced-land-cover-classification-wit/Land cover maps are a critical tool to support informed policy development, planning, and resource management decisions. With significant upsides, the autoIncreasing the Effectiveness of Active Learning: Introducing Artificial Data Generation in Active Learning for Land Use/Land Cover Classificationhttps://jfonseca.eu/paper/increasing-the-effectiveness-of-active-learning-i/Fri, 01 Jan 2021 00:00:00 +0000https://jfonseca.eu/paper/increasing-the-effectiveness-of-active-learning-i/In remote sensing, Active Learning (AL) has become an important technique to collect informative ground truth data “on-demand” for supervised classificatioNarratives and Needs: Analyzing Experiences of Cyclone Amphan Using Twitter Discoursehttps://jfonseca.eu/paper/narratives-and-needs-analyzing-experiences-of-cyc/Wed, 01 Jan 2020 00:00:00 +0000https://jfonseca.eu/paper/narratives-and-needs-analyzing-experiences-of-cyc/Paper about Narratives and Needs: Analyzing Experiences of CycImbalanced Learning in Land Cover Classification: Improving Minority Classes’ Prediction Accuracy Using the Geometric SMOTE Algorithmhttps://jfonseca.eu/paper/imbalanced-learning-in-land-cover-classification/Tue, 01 Jan 2019 00:00:00 +0000https://jfonseca.eu/paper/imbalanced-learning-in-land-cover-classification/the automatic production of land use/land cover maps continues to be a challenging problem, with important impacts on the ability to promote sustainabilityHarnessing Big Data to Inform Tourism Destination Management Organizationshttps://jfonseca.eu/paper/harnessing-big-data-to-inform-tourism-destination/Mon, 01 Jan 2018 00:00:00 +0000https://jfonseca.eu/paper/harnessing-big-data-to-inform-tourism-destination/Paper about Harnessing Big Data to Inform Tourism Destination