b'Tijl De Bie'

research

∙ 09/18/2023

LLM4Jobs: Unsupervised occupation extraction and standardization leveraging Large Language Models

Automated occupation extraction and standardization from free-text job p...

0 Nan Li, et al. ∙

research

∙ 08/18/2023

ReCon: Reducing Congestion in Job Recommendation using Optimal Transport

Recommender systems may suffer from congestion, meaning that there is an...

0 Yoosof Mashayekhi, et al. ∙

research

∙ 04/17/2023

SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language Model

We present SkillGPT, a tool for skill extraction and standardization (SE...

0 Nan Li, et al. ∙

research

∙ 04/12/2023

Maximal Fairness

Fairness in AI has garnered quite some attention in research, and increa...

0 MaryBeth Defrance, et al. ∙

research

∙ 12/13/2022

Inherent Limitations of AI Fairness

As the real-world impact of Artificial Intelligence (AI) systems has bee...

0 Maarten Buyl, et al. ∙

research

∙ 09/16/2022

A Systematic Evaluation of Node Embedding Robustness

Node embedding methods map network nodes to low dimensional vectors that...

0 Alexandru Mara, et al. ∙

research

∙ 09/12/2022

A challenge-based survey of e-recruitment recommendation systems

E-recruitment recommendation systems recommend jobs to job seekers and j...

0 Yoosof Mashayekhi, et al. ∙

research

∙ 03/14/2022

Graph-Survival: A Survival Analysis Framework for Machine Learning on Temporal Networks

Continuous time temporal networks are attracting increasing attention du...

0 Raphaël Romero, et al. ∙

research

∙ 02/22/2022

Evaluating Feature Attribution Methods in the Image Domain

Feature attribution maps are a popular approach to highlight the most im...

0 Arne Gevaert, et al. ∙

research

∙ 02/08/2022

Optimal Transport of Binary Classifiers to Fairness

Much of the past work on fairness in machine learning has focused on for...

0 Maarten Buyl, et al. ∙

research

∙ 12/14/2021

An Earth Mover's Distance Based Graph Distance Metric For Financial Statements

Quantifying the similarity between a group of companies has proven to be...

0 Sander Noels, et al. ∙

research

∙ 10/18/2021

Topologically Regularized Data Embeddings

Unsupervised feature learning often finds low-dimensional embeddings tha...

0 Robin Vandaele, et al. ∙

research

∙ 09/22/2021

The Curse Revisited: a Newly Quantified Concept of Meaningful Distances for Learning from High-Dimensional Noisy Data

Distances between data points are widely used in point cloud representat...

0 Robin Vandaele, et al. ∙

research

∙ 07/05/2021

Adversarial Robustness of Probabilistic Network Embedding for Link Prediction

In today's networked society, many real-world problems can be formalized...

0 Xi Chen, et al. ∙

research

∙ 05/12/2021

Automating Data Science: Prospects and Challenges

Given the complexity of typical data science projects and the associated...

84 Tijl De Bie, et al. ∙

research

∙ 03/02/2021

The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizer

Learning and reasoning over graphs is increasingly done by means of prob...

0 Maarten Buyl, et al. ∙

research

∙ 05/19/2020

CSNE: Conditional Signed Network Embedding

Signed networks are mathematical structures that encode positive and neg...

7 Alexandru Mara, et al. ∙

research

∙ 02/26/2020

DeBayes: a Bayesian method for debiasing network embeddings

As machine learning algorithms are increasingly deployed for high-impact...

64 Maarten Buyl, et al. ∙

research

∙ 02/25/2020

Network Representation Learning for Link Prediction: Are we improving upon simple heuristics?

Network representation learning has become an active research area in re...

51 Alexandru Mara, et al. ∙

research

∙ 02/24/2020

FONDUE: A Framework for Node Disambiguation Using Network Embeddings

Real-world data often presents itself in the form of a network. Examples...

17 Ahmad Mel, et al. ∙

research

∙ 02/14/2020

Scalable Dyadic Independence Models with Local and Global Constraints

An important challenge in the field of exponential random graphs (ERGs) ...

11 Florian Adriaens, et al. ∙

research

∙ 02/04/2020

ALPINE: Active Link Prediction using Network Embedding

Many real-world problems can be formalized as predicting links in a part...

1 Xi Chen, et al. ∙

research

∙ 09/20/2019

FACE: Feasible and Actionable Counterfactual Explanations

Work in Counterfactual Explanations tends to focus on the principle of "...

14 Rafael Poyiadzi, et al. ∙

research

∙ 09/03/2019

Discovering Interesting Cycles in Directed Graphs

Cycles in graphs often signify interesting processes. For example, cycli...

0 Florian Adriaens, et al. ∙

research

∙ 05/24/2019

Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information

Dimensionality reduction and manifold learning methods such as t-Distrib...

0 Bo Kang, et al. ∙

research

∙ 04/22/2019

ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions

Networks are powerful data structures, but are challenging to work with ...

0 Bo Kang, et al. ∙

research

∙ 05/19/2018

Conditional Network Embeddings

Network embeddings map the nodes of a given network into d-dimensional E...

0 Bo Kang, et al. ∙

research

∙ 10/23/2017

Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach

Visual exploration of high-dimensional real-valued datasets is a fundame...

0 Kai Puolamäki, et al. ∙

research

∙ 10/12/2017

Subjectively Interesting Subgroup Discovery on Real-valued Targets

Deriving insights from high-dimensional data is one of the core problems...

0 Jefrey Lijffijt, et al. ∙

research

∙ 07/25/2011

An end-to-end machine learning system for harmonic analysis of music

We present a new system for simultaneous estimation of keys, chords, and...

0 Yizhao Ni, et al. ∙

research

∙ 06/29/2009

Explicit probabilistic models for databases and networks

Recent work in data mining and related areas has highlighted the importa...

0 Tijl De Bie, et al. ∙

Tijl De Bie

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