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View article: Machine Learning Methods for Predicting Cancer Complications Using Smartphone Sensor Data: A Prospective Study
Machine Learning Methods for Predicting Cancer Complications Using Smartphone Sensor Data: A Prospective Study Open
Complications are frequent in cancer patients and contribute to adverse outcomes and higher healthcare costs, underscoring the need for earlier identification and prediction. This study evaluated the feasibility of using passively generate…
View article: SimHawNet: a modified Hawkes process for temporal network simulation
SimHawNet: a modified Hawkes process for temporal network simulation Open
Temporal networks allow representing connections between objects while incorporating the temporal dimension. While static network models can capture unchanging topological regularities, they often fail to model the effects associated with …
View article: Multiresolution Analysis and Statistical Thresholding on Dynamic Networks
Multiresolution Analysis and Statistical Thresholding on Dynamic Networks Open
Detecting structural change in dynamic network data has wide-ranging applications. Existing approaches typically divide the data into time bins, extract network features within each bin, and then compare these features over time. This intr…
View article: BiMi Sheets: Infosheets for bias mitigation methods
BiMi Sheets: Infosheets for bias mitigation methods Open
Over the past 15 years, hundreds of bias mitigation methods have been proposed in the pursuit of fairness in machine learning (ML). However, algorithmic biases are domain-, task-, and model-specific, leading to a `portability trap': bias m…
View article: JobHop: A Large-Scale Dataset of Career Trajectories
JobHop: A Large-Scale Dataset of Career Trajectories Open
Understanding labor market dynamics is essential for policymakers, employers, and job seekers. However, comprehensive datasets that capture real-world career trajectories are scarce. In this paper, we introduce JobHop, a large-scale public…
View article: LLM4Jobs: Unsupervised occupation extraction and standardization leveraging Large Language Models
LLM4Jobs: Unsupervised occupation extraction and standardization leveraging Large Language Models Open
Automated occupation extraction and standardization from free-text job postings and resumes are crucial for applications like job recommendation and labor market policy formation. This paper introduces LLM4Jobs, a novel unsupervised method…
View article: Maximal Combinations of Fairness Definitions
Maximal Combinations of Fairness Definitions Open
The so-called ‘Impossibility Theorem’ for fairness definitions is one of the more striking research results with both theoretical and practical consequences, as it states that satisfying certain combinations of fairness definitions is impo…
View article: Biased Heritage: How Datasets Shape Models in Facial Expression Recognition
Biased Heritage: How Datasets Shape Models in Facial Expression Recognition Open
In recent years, the rapid development of artificial intelligence (AI) systems has raised concerns about our ability to ensure their fairness, that is, how to avoid discrimination based on protected characteristics such as gender, race, or…
View article: Your Next State-of-the-Art Could Come from Another Domain: A Cross-Domain Analysis of Hierarchical Text Classification
Your Next State-of-the-Art Could Come from Another Domain: A Cross-Domain Analysis of Hierarchical Text Classification Open
Text classification with hierarchical labels is a prevalent and challenging task in natural language processing. Examples include assigning ICD codes to patient records, tagging patents into IPC classes, assigning EUROVOC descriptors to Eu…
View article: A Dutch Financial Large Language Model
A Dutch Financial Large Language Model Open
This paper presents FinGEITje, the first Dutch financial Large Language Model (LLM) specifically designed and optimized for various financial tasks. Together with the model, we release a specialized Dutch financial instruction tuning datas…
View article: A Dutch Financial Large Language Model
A Dutch Financial Large Language Model Open
This paper presents FinGEITje, the first Dutch financial Large Language Model\n(LLM) specifically designed and optimized for various financial tasks. Together\nwith the model, we release a specialized Dutch financial instruction tuning\nda…
View article: Persuasion with Large Language Models: a Survey
Persuasion with Large Language Models: a Survey Open
The rapid rise of Large Language Models (LLMs) has created new disruptive possibilities for persuasive communication, by enabling fully-automated personalized and interactive content generation at an unprecedented scale. In this paper, we …
View article: Large Language Models Reflect the Ideology of their Creators
Large Language Models Reflect the Ideology of their Creators Open
Large language models (LLMs) are trained on vast amounts of data to generate natural language, enabling them to perform tasks like text summarization and question answering. These models have become popular in artificial intelligence (AI) …
View article: ABCFair: an Adaptable Benchmark approach for Comparing Fairness Methods
ABCFair: an Adaptable Benchmark approach for Comparing Fairness Methods Open
Numerous methods have been implemented that pursue fairness with respect to sensitive features by mitigating biases in machine learning. Yet, the problem settings that each method tackles vary significantly, including the stage of interven…
View article: Pattern or Artifact? Interactively Exploring Embedding Quality with TRACE
Pattern or Artifact? Interactively Exploring Embedding Quality with TRACE Open
This paper presents TRACE, a tool to analyze the quality of 2D embeddings generated through dimensionality reduction techniques. Dimensionality reduction methods often prioritize preserving either local neighborhoods or global distances, b…
View article: KamerRaad: Enhancing Information Retrieval in Belgian National Politics through Hierarchical Summarization and Conversational Interfaces
KamerRaad: Enhancing Information Retrieval in Belgian National Politics through Hierarchical Summarization and Conversational Interfaces Open
KamerRaad is an AI tool that leverages large language models to help citizens interactively engage with Belgian political information. The tool extracts and concisely summarizes key excerpts from parliamentary proceedings, followed by the …
View article: Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms
Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms Open
Dynamic Link Prediction (DLP) addresses the prediction of future links in evolving networks. However, accurately portraying the performance of DLP algorithms poses challenges that might impede progress in the field. Importantly, common eva…
View article: TopoLedgerBERT: Topological Learning of Ledger Description Embeddings using Siamese BERT-Networks
TopoLedgerBERT: Topological Learning of Ledger Description Embeddings using Siamese BERT-Networks Open
This paper addresses a long-standing problem in the field of accounting: mapping company-specific ledger accounts to a standardized chart of accounts. We propose a novel solution, TopoLedgerBERT, a unique sentence embedding method devised …
View article: A Challenge-based Survey of E-recruitment Recommendation Systems
A Challenge-based Survey of E-recruitment Recommendation Systems Open
E-recruitment recommendation systems recommend jobs to job seekers and job seekers to recruiters. The recommendations are generated based on the suitability of job seekers for positions and on job seekers’ and recruiters’ preferences. Ther…
View article: Evaluating Feature Attribution Methods in the Image Domain: Benchmark results and model parameters
Evaluating Feature Attribution Methods in the Image Domain: Benchmark results and model parameters Open
This dataset contains the experimental results, adversarial patches and model parameters used in the paper Evaluating Feature Attribution Methods in the Image Domain.
View article: FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources
FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources Open
Recommendation in settings such as e-recruitment and online dating involves distributing limited opportunities, which differs from recommending practically unlimited goods such as in e-commerce or music recommendation. This setting calls f…
View article: Inherent Limitations of AI Fairness
Inherent Limitations of AI Fairness Open
AI fairness should not be considered a panacea: It may have the potential to make society more fair than ever, but it needs critical thought and outside help to make it happen.
View article: DeBayes: a Bayesian Method for Debiasing Network Embeddings
DeBayes: a Bayesian Method for Debiasing Network Embeddings Open
As machine learning algorithms are increasingly deployed for high-impact automated decision making, ethical and increasingly also legal standards demand that they treat all individuals fairly, without discrimination based on their age, gen…
View article: Scalable Job Recommendation With Lower Congestion Using Optimal Transport
Scalable Job Recommendation With Lower Congestion Using Optimal Transport Open
Recommender systems often face congestion, characterized by an uneven distribution in the frequency of item recommendations. The presence of congestion in recommendations is especially problematic in domains where users or items have limit…
View article: Incorporating Topological Priors Into Low-Dimensional Visualizations Through Topological Regularization
Incorporating Topological Priors Into Low-Dimensional Visualizations Through Topological Regularization Open
Unsupervised representation learning techniques are commonly employed to analyze high-dimensional or unstructured data. In some cases, users may have prior knowledge of the topology of the data, such as a known cluster structure or the fac…