Deepak Kumar, M.Sc.

How Far Can Pretrained LLMs Go in Symbolic Music? Controlled Comparisons of Supervised and Preference-based Adaptation

Deepak Kumar, Emmanouil Karystinaios, Gerhard Widmer, Markus Schedl

Proceedings of the 4th Workshop on NLP for Music and Audio

Mitigating Gender Bias in Job Ranking Systems Using Job Advertisement Neutrality

Deepak Kumar, Shahed Masoudian, Alessandro B. Melchiorre, Markus Schedl

Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI)

Modular Debiasing of Latent User Representations in Prototype-based Recommender Systems

Alessandro B. Melchiorre, Shahed Masoudian, Deepak Kumar, Markus Schedl

Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2024)

Award · Best Student Paper Award

Fairness of Recommender Systems in the Recruitment Domain: An Analysis from Technical and Legal Perspectives

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

Frontiers in Big Data - Recommender Systems

Identifying Words in Job Advertisements Responsible for Gender Bias in Candidate Ranking Systems via Counterfactual Learning

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

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)

Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks

Lukas Hauzenberger, Shahed Masoudian, Deepak Kumar, Markus Schedl, Navid Rekab-saz

Findings of the Association for Computational Linguistics: ACL 2023

Parameter-efficient Modularised Bias Mitigation via AdapterFusion

Deepak Kumar, Oleg Lesota, George Zerveas, Daniel Cohen, Carsten Eickoff, Markus Schedl, Navid Rekab-saz

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics