Ghent University Academic Bibliography

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Your filters: cql: author="De Bie, T*" or (type exact bookEditor and editor="De Bie, T*")

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Inherent limitations of AI fairness

Maarten Buyl (UGent) and Tijl De Bie (UGent)

(2024) COMMUNICATIONS OF THE ACM. 67(2). p.48-55
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- Journal Article
FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources

Nan Li (UGent) , Bo Kang (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)

(2024) ACM Transactions on Intelligent Systems and Technology.
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- Journal Article
Scalable Job Recommendation with Lower Congestion using Optimal Transport

Yoosof Mashayekhi (UGent) , Bo Kang (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)

(2024) IEEE Access. p.1-1
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An empirical evaluation of network representation learning methods

Alexandru-Cristian Mara (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)

(2023) BIG DATA.
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- Conference Paper
- P1
Maximal fairness

Marybeth Defrance (UGent) and Tijl De Bie (UGent)

(2023) PROCEEDINGS OF THE 6TH ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2023. p.851-880
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- A2
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An efficient graph-based peer selection method for financial statements

Sander Noels (UGent) , Simon De Ridder, Sebastien Viaene and Tijl De Bie (UGent)

(2023) INTELLIGENT SYSTEMS IN ACCOUNTING FINANCE & MANAGEMENT. 30(3). p.120-136
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- Miscellaneous
LLM4Jobs : unsupervised occupation extraction and standardization leveraging Large Language Models

Nan Li (UGent) , Bo Kang (UGent) and Tijl De Bie (UGent)

(2023)
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SkillGPT : a RESTful API service for skill extraction and standardization using a Large Language Model

Nan Li (UGent) , Bo Kang (UGent) and Tijl De Bie (UGent)

(2023)
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FEIR : quantifying and reducing envy and inferiority for fair recommendation of limited resources

Nan Li (UGent) , Bo Kang (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)

(2023) RECSYS in HR 2023 : the 3rd Workshop on Recommender Systems for Human Resources (RecSys in HR 2023), Proceedings. In CEUR workshop proceedings 3490.
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Gaussian embedding of temporal networks

Raphaël Romero (UGent) , Jefrey Lijffijt (UGent) , Riccardo Rastelli, Marco Corneli and Tijl De Bie (UGent)

(2023) IEEE ACCESS. 11. p.117971-117983
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GREASE : graph imbalance reduction by adding sets of edges

Yoosof Mashayekhi (UGent) , Bo Kang (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)

(2023) IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING. p.1-14
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- Miscellaneous
Fairret : a framework for differentiable fairness regularization terms

Maarten Buyl (UGent) , Marybeth Defrance (UGent) and Tijl De Bie (UGent)

(2023)
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Automated financial analysis using GPT-4

Sander Noels (UGent) , Adriaan Merlevede, Andrew Fecheyr, Maarten Vanhalst, Nick Meerlaen, Sébastien Viaene and Tijl De Bie (UGent)

(2023) MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES : APPLIED DATA SCIENCE AND DEMO TRACK, ECML PKDD 2023, PT VII. In Lecture notes in computer science 14175. p.345-349
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ReCon : reducing congestion in job recommendation using optimal transport

Yoosof Mashayekhi (UGent) , Bo Kang (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)

(2023) Proceedings of the 17th ACM Conference on Recommender Systems (RecSys 2023). p.696-701
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- Miscellaneous
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Topologically Regularized Data Embeddings

Edith Heiter (UGent) , Robin Vandaele, Tijl De Bie (UGent) , Yvan Saeys (UGent) and Jefrey Lijffijt (UGent)

(2023)
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Graph-survival : a survival analysis framework for machine learning on temporal networks

Raphaël Romero (UGent) , Bo Kang (UGent) and Tijl De Bie (UGent)

(2022) ECML-PKDD 2022 PhD Forum, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Proceedings.
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Automating data science

Tijl De Bie (UGent) , Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth and Christopher K. I. Williams

(2022) COMMUNICATIONS OF THE ACM. 65(3). p.76-87
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EvalNE : a framework for network embedding evaluation

Alexandru-Cristian Mara (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)

(2022) SOFTWAREX. 17.
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Evaluating representation learning and graph layout methods for visualization

Edith Heiter (UGent) , Bo Kang (UGent) , Tijl De Bie (UGent) and Jefrey Lijffijt (UGent)

(2022) IEEE COMPUTER GRAPHICS AND APPLICATIONS. 42(3). p.19-28
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Topologically regularized data embeddings

Robin Vandaele, Bo Kang (UGent) , Jefrey Lijffijt (UGent) , Tijl De Bie (UGent) and Yvan Saeys (UGent)

(2022) International Conference on Learning Representations (ICLR 2022), Proceedings.

Academic Bibliography

Inherent limitations of AI fairness

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

Scalable Job Recommendation with Lower Congestion using Optimal Transport

An empirical evaluation of network representation learning methods

Maximal fairness

An efficient graph-based peer selection method for financial statements

LLM4Jobs : unsupervised occupation extraction and standardization leveraging Large Language Models

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

FEIR : quantifying and reducing envy and inferiority for fair recommendation of limited resources

Gaussian embedding of temporal networks

GREASE : graph imbalance reduction by adding sets of edges

Fairret : a framework for differentiable fairness regularization terms

Automated financial analysis using GPT-4

ReCon : reducing congestion in job recommendation using optimal transport

Topologically Regularized Data Embeddings

Graph-survival : a survival analysis framework for machine learning on temporal networks

Automating data science

EvalNE : a framework for network embedding evaluation

Evaluating representation learning and graph layout methods for visualization

Topologically regularized data embeddings

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