I am an assistant professor at Johannes Kepler University - JKU. Prior to it, I was a post doctoral researcher at Idiap Research Institute (affiliated with EPFL), and a PhD candidate at TU Wien. I explore deep learning methods in natural language processing and information retrieval, with a focus on fairness and algorithmic bias mitigation.
Univ. Ass., Dr. Navid Rekab-saz
Assistant Professor
Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024
ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to Scale
Findings of the Association for Computational Linguistics ACL 2024, 2024
What the Weight?! A Unified Framework for Zero-Shot Knowledge Composition
Findings of the Association for Computational Linguistics: EACL 2024, 2024
Effective Controllable Bias Mitigation for Classification and Retrieval using Gate Adapters
The 18th Conference of the European Chapter of the Association for Computational Linguistics March 17-22, 2024, 2024
Computational Versus Perceived Popularity Miscalibration in Recommender Systems
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023
Identifying Words in Job Advertisements Responsible for Gender Bias in Candidate Ranking Systems via Counterfactual Learning
Proceedings of the 3rd Workshop on Recommender Systems for Human Resources (RecSys in HR 2023) co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023), 2023
Fairness of Recommender Systems in the Recruitment Domain: An Analysis from Technical and Legal Perspectives
Frontiers in Big Data - Recommender Systems, 2023
Domain Information Control at Inference Time for Acoustic Scene Classification
31st European Signal Processing Conference, (EUSIPCO) 2023, Helsinki,Finland, September 4-8, 2023, 2023
Parameter-efficient Modularised Bias Mitigation via AdapterFusion
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, 2023
Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks
Findings of the Association for Computational Linguistics: ACL 2023, 2023
Unlearning Protected User Attributes in Recommendations with Adversarial Training
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022
ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations
Proceedings of the 16th ACM Conference on Recommender Systems (RecSys), 2022
Exploring Cross-group Discrepancies in Calibrated Popularity for Accuracy/Fairness Trade-off Optimization
Proceedings of the 2nd Workshop on Multi-Objective Recommender Systems co-located with 16th ACM Conference on Recommender Systems (RecSys 2022), Seattle, WA, USA, 18th-23rd September, 2022
Traces of Globalization in Online Music Consumption Patterns and Results of Recommendation Algorithms
Proceedings of the 23rd International Society for Music Information Retrieval Conference, ISMIR 2022, Bengaluru, India, December 4-8, 2022
Investigating Gender Fairness of Recommendation Algorithms in the Music Domain
Information Processing & Management, 2021
Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?
Proceedings of the 15th ACM Conference on Recommender Systems (RecSys), 2021
A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models
Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval, 2021
