Search Results for author: Tijl De Bie

Found 34 papers, 20 papers with code

New Perspectives on the Evaluation of Link Prediction Algorithms for Dynamic Graphs

1 code implementation30 Nov 2023 Raphaël Romero, Tijl De Bie, Jefrey Lijffijt

We leverage these visualization tools to investigate the effect of negative sampling on the predictive performance, at the node and edge level.

Dynamic Link Prediction

FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources

1 code implementation8 Nov 2023 Nan Li, Bo Kang, Jefrey Lijffijt, Tijl De Bie

In settings such as e-recruitment and online dating, recommendation involves distributing limited opportunities, calling for novel approaches to quantify and enforce fairness.

Fairness Recommendation Systems

fairret: a Framework for Differentiable Fairness Regularization Terms

no code implementations26 Oct 2023 Maarten Buyl, MaryBeth Defrance, Tijl De Bie

Current tools for machine learning fairness only admit a limited range of fairness definitions and have seen little integration with automatic differentiation libraries, despite the central role these libraries play in modern machine learning pipelines.

Fairness

LLM4Jobs: Unsupervised occupation extraction and standardization leveraging Large Language Models

1 code implementation18 Sep 2023 Nan Li, Bo Kang, Tijl De Bie

Automated occupation extraction and standardization from free-text job postings and resumes are crucial for applications like job recommendation and labor market policy formation.

Natural Language Understanding

ReCon: Reducing Congestion in Job Recommendation using Optimal Transport

1 code implementation18 Aug 2023 Yoosof Mashayekhi, Bo Kang, Jefrey Lijffijt, Tijl De Bie

Recommenders are increasingly used in domains where items have limited availability, such as the job market, where congestion is especially problematic: Recommending a vacancy -- for which typically only one person will be hired -- to a large number of job seekers may lead to frustration for job seekers, as they may be applying for jobs where they are not hired.

Recommendation Systems

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

1 code implementation17 Apr 2023 Nan Li, Bo Kang, Tijl De Bie

We present SkillGPT, a tool for skill extraction and standardization (SES) from free-style job descriptions and user profiles with an open-source Large Language Model (LLM) as backbone.

Feature Engineering Language Modelling +1

Maximal Fairness

no code implementations12 Apr 2023 MaryBeth Defrance, Tijl De Bie

To date, this negative result has not yet been complemented with a positive one: a characterization of which combinations of fairness notions are possible.

Fairness

Topologically Regularized Data Embeddings

1 code implementation9 Jan 2023 Edith Heiter, Robin Vandaele, Tijl De Bie, Yvan Saeys, Jefrey Lijffijt

In some cases, users may have prior topological knowledge about the data, such as a known cluster structure or the fact that the data is known to lie along a tree- or graph-structured topology.

Computational Efficiency Dimensionality Reduction +2

A Systematic Evaluation of Node Embedding Robustness

1 code implementation16 Sep 2022 Alexandru Mara, Jefrey Lijffijt, Stephan Günnemann, Tijl De Bie

We find that node classification results are impacted more than network reconstruction ones, that degree-based and label-based attacks are on average the most damaging and that label heterophily can strongly influence attack performance.

Classification Node Classification

A challenge-based survey of e-recruitment recommendation systems

no code implementations12 Sep 2022 Yoosof Mashayekhi, Nan Li, Bo Kang, Jefrey Lijffijt, Tijl De Bie

The recommendations are generated based on the suitability of the job seekers for the positions as well as the job seekers' and the recruiters' preferences.

Collaborative Filtering Recommendation Systems

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

no code implementations14 Mar 2022 Raphaël Romero, Bo Kang, Tijl De Bie

Continuous time temporal networks are attracting increasing attention due their omnipresence in real-world datasets and they manifold applications.

BIG-bench Machine Learning Link Prediction +2

Evaluating Feature Attribution Methods in the Image Domain

1 code implementation22 Feb 2022 Arne Gevaert, Axel-Jan Rousseau, Thijs Becker, Dirk Valkenborg, Tijl De Bie, Yvan Saeys

Feature attribution maps are a popular approach to highlight the most important pixels in an image for a given prediction of a model.

Benchmarking

Optimal Transport of Classifiers to Fairness

1 code implementation8 Feb 2022 Maarten Buyl, Tijl De Bie

In past work on fairness in machine learning, the focus has been on forcing the prediction of classifiers to have similar statistical properties for people of different demographics.

Fairness

Topologically Regularized Data Embeddings

1 code implementation ICLR 2022 Robin Vandaele, Bo Kang, Jefrey Lijffijt, Tijl De Bie, Yvan Saeys

For tasks for which prior expert topological knowledge is available, incorporating this into the learned representation may lead to higher quality embeddings.

Graph Embedding Representation Learning

The Curse Revisited: When are Distances Informative for the Ground Truth in Noisy High-Dimensional Data?

1 code implementation22 Sep 2021 Robin Vandaele, Bo Kang, Tijl De Bie, Yvan Saeys

Previously, it has been argued that neighborhood queries become meaningless and unstable when distance concentration occurs, which means that there is a poor relative discrimination between the furthest and closest neighbors in the data.

Dimensionality Reduction Representation Learning

Adversarial Robustness of Probabilistic Network Embedding for Link Prediction

no code implementations5 Jul 2021 Xi Chen, Bo Kang, Jefrey Lijffijt, Tijl De Bie

However, these methods lack transparency when compared to simpler baselines, and as a result their robustness against adversarial attacks is a possible point of concern: could one or a few small adversarial modifications to the network have a large impact on the link prediction performance when using a network embedding model?

Adversarial Robustness Link Prediction +1

Automating Data Science: Prospects and Challenges

no code implementations12 May 2021 Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams

Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process.

AutoML BIG-bench Machine Learning

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

1 code implementation2 Mar 2021 Maarten Buyl, Tijl De Bie

Given this, we propose a fairness regularizer defined as the KL-divergence between the graph model and its I-projection onto the set of fair models.

Fairness Graph Embedding

CSNE: Conditional Signed Network Embedding

2 code implementations19 May 2020 Alexandru Mara, Yoosof Mashayekhi, Jefrey Lijffijt, Tijl De Bie

Experiments on a variety of real-world networks confirm that CSNE outperforms the state-of-the-art on the task of sign prediction.

Network Embedding

DeBayes: a Bayesian Method for Debiasing Network Embeddings

1 code implementation ICML 2020 Maarten Buyl, Tijl De Bie

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, gender, race or other sensitive traits.

BIG-bench Machine Learning Decision Making +4

Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress?

2 code implementations25 Feb 2020 Alexandru Mara, Jefrey Lijffijt, Tijl De Bie

Network embedding methods map a network's nodes to vectors in an embedding space, in such a way that these representations are useful for estimating some notion of similarity or proximity between pairs of nodes in the network.

Benchmarking Link Prediction +1

Block-Approximated Exponential Random Graphs

1 code implementation14 Feb 2020 Florian Adriaens, Alexandru Mara, Jefrey Lijffijt, Tijl De Bie

An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large graphs.

Clustering Link Prediction

ALPINE: Active Link Prediction using Network Embedding

no code implementations4 Feb 2020 Xi Chen, Bo Kang, Jefrey Lijffijt, Tijl De Bie

Often, the link status of a node pair can be queried, which can be used as additional information by the link prediction algorithm.

Active Learning Experimental Design +2

FACE: Feasible and Actionable Counterfactual Explanations

1 code implementation20 Sep 2019 Rafael Poyiadzi, Kacper Sokol, Raul Santos-Rodriguez, Tijl De Bie, Peter Flach

First, a counterfactual example generated by the state-of-the-art systems is not necessarily representative of the underlying data distribution, and may therefore prescribe unachievable goals(e. g., an unsuccessful life insurance applicant with severe disability may be advised to do more sports).

counterfactual

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

no code implementations24 May 2019 Bo Kang, Darío García García, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie

Dimensionality reduction and manifold learning methods such as t-Distributed Stochastic Neighbor Embedding (t-SNE) are routinely used to map high-dimensional data into a 2-dimensional space to visualize and explore the data.

Dimensionality Reduction

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

no code implementations22 Apr 2019 Bo Kang, Jefrey Lijffijt, Tijl De Bie

Networks are powerful data structures, but are challenging to work with for conventional machine learning methods.

BIG-bench Machine Learning counterfactual +2

EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction

1 code implementation22 Jan 2019 Alexandru Mara, Jefrey Lijffijt, Tijl De Bie

In this paper we present EvalNE, a Python toolbox for evaluating network embedding methods on link prediction tasks.

Social and Information Networks

Conditional Network Embeddings

no code implementations ICLR 2019 Bo Kang, Jefrey Lijffijt, Tijl De Bie

Network Embeddings (NEs) map the nodes of a given network into $d$-dimensional Euclidean space $\mathbb{R}^d$.

General Classification Link Prediction +1

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

1 code implementation23 Oct 2017 Kai Puolamäki, Emilia Oikarinen, Bo Kang, Jefrey Lijffijt, Tijl De Bie

We conclude that the information theoretic approach to exploratory data analysis where patterns observed by a user are formalized as constraints provides a principled, intuitive, and efficient basis for constructing an EDA system.

Informative Data Projections: A Framework and Two Examples

no code implementations27 Nov 2015 Tijl De Bie, Jefrey Lijffijt, Raul Santos-Rodriguez, Bo Kang

Methods for Projection Pursuit aim to facilitate the visual exploration of high-dimensional data by identifying interesting low-dimensional projections.

Vocal Bursts Valence Prediction

Meta-song evaluation for chord recognition

no code implementations TBD 2011 Yizhao Ni, Matt Mcvicar, Raul Santos-Rodriguez, Tijl De Bie

We present a new approach to evaluate chord recognition systems on songs which do not have full annotations.

Chord Recognition

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