Navid Rekab-saz

Navid Rekab-saz

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.


Peer-Reviewed Journal and Conference Papers

ConGater_EACL_2024 screenshot

Shahed Masoudian, Volaucnik Cornelia, Markus Schedl, Navid Rekab-saz
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

CompVsUserPercep screenshot

Oleg Lesota, Gustavo Escobedo, Yashar Deldjoo, Bruce Ferwerda, Simone Kopeinik, Elisabeth Lex, Navid Rekab-saz, Markus Schedl
Computational Versus Perceived Popularity Miscalibration in Recommender Systems
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

Red_Words_RecSysHR screenshot

Deepak Kumar, Tessa Grosz, Elisabeth Greif, Navid Rekab-saz, Markus Schedl
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

ControlledDA_EUSIPCO_2023 screenshot

Shahed Masoudian, Koutini Khaled, Markus Schedl, Widmer Gerhard, Navid Rekab-saz
Domain Information Control at Inference Time for Acoustic Scene Classification
31st European Signal Processing Conference, (EUSIPCO) 2023, Helsinki,Finland, September 4-8, 2023, 2023

DAM_EACL_2023 screenshot

Deepak Kumar, Oleg Lesota, George Zerveas, Daniel Cohen, Carsten Eickoff, Markus Schedl, Navid Rekab-saz
Parameter-efficient Modularised Bias Mitigation via AdapterFusion
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, 2023

ModularDebiasing_ACLFindings_2023 screenshot

Lukas Hauzenberger, Shahed Masoudian, Deepak Kumar, Markus Schedl, Navid Rekab-saz
Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks
Findings of the Association for Computational Linguistics: ACL 2023, 2023

UnlearningAdversarial screenshot

Christian Ganhör, David Penz, Navid Rekab-saz, Oleg Lesota, Markus Schedl
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

CaliPopExploring screenshot

Oleg Lesota, Stefan Brandl, Matthias Wenzel, Alessandro B. Melchiorre, Elisabeth Lex, Navid Rekab-saz, Markus Schedl
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

CountryBias screenshot

Oleg Lesota, Emilia Parada-Cabaleiro, Stefan Brandl, Elisabeth Lex, Navid Rekab-saz, Markus Schedl
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

PopBiasGender screenshot

Oleg Lesota, Alessandro B. Melchiorre, Navid Rekab-saz, Stefan Brandl, Dominik Kowald, Elisabeth Lex, Markus Schedl
Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?
Proceedings of the 15th ACM Conference on Recommender Systems (RecSys), 2021

DeepGenIR screenshot

Oleg Lesota, Navid Rekab-saz, Daniel Cohen, Klaus Antonius Grasserbauer, Carsten Eickhoff, Markus Schedl
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