Search Results for author: Tijl De Bie

Found 26 papers, 12 papers with code

A Systematic Evaluation of Node Embedding Robustness

no code implementations16 Sep 2022 Alexandru Mara, Jefrey Lijffijt, Stephan Günnemann, Tijl De Bie

Node embedding methods map network nodes to low dimensional vectors that can be subsequently used in a variety of downstream prediction tasks.

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.

Optimal Transport of Classifiers to Fairness

no code implementations8 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.


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

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.

Link Prediction Network Embedding

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.

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

Explainable Subgraphs with Surprising Densities: A Subgroup Discovery Approach

1 code implementation10 Jan 2020 Junning Deng, Bo Kang, Jefrey Lijffijt, Tijl De Bie

The connectivity structure of graphs is typically related to the attributes of the nodes.

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).

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 Link Prediction +1

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.

Subjectively Interesting Subgroup Discovery on Real-valued Targets

no code implementations12 Oct 2017 Jefrey Lijffijt, Bo Kang, Wouter Duivesteijn, Kai Puolamäki, Emilia Oikarinen, Tijl De Bie

The subgroup descriptions are in terms of a succinct set of arbitrarily-typed other attributes.

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.

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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