MOTIF-Driven Contrastive Learning of Graph Representations. - Arjun Subramonian (AAAI, 2021)

Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies. - Sunipa Dev; Masoud Monajatipoor; Anaelia Ovalle; Arjun Subramonian; Jeff M. Phillips; Kai-Wei Chang (EMNLP (1), 2021)

On the Discrimination Risk of Mean Aggregation Feature Imputation in Graphs. - Arjun Subramonian; Kai-Wei Chang; Yizhou Sun (NeurIPS, 2022)

Queer in AI. - Hetvi Jethwani; Arjun Subramonian; William Agnew; MaryLena Bleile; Sarthak Arora; Maria Ryskina; Jeffrey Xiong (XRDS, 2022)

It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance. - Arjun Subramonian; Xingdi Yuan; Hal Daumé III; Su Lin Blodgett (ACL (Findings), 2023)

Bound by the Bounty: Collaboratively Shaping Evaluation Processes for Queer AI Harms. - Nathan Dennler; Anaelia Ovalle; Ashwin Singh; Luca Soldaini; Arjun Subramonian; Huy Tu; William Agnew; Avijit Ghosh; Kyra Yee; Irene Font Peradejordi; Zeerak Talat; Mayra Russo; et al. (AIES, 2023)

Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness. - Anaelia Ovalle; Arjun Subramonian; Vagrant Gautam; Gilbert Gee; Kai-Wei Chang (AIES, 2023)

Queer In AI: A Case Study in Community-Led Participatory AI. - Organizers Of QueerInAI; Anaelia Ovalle; Arjun Subramonian; Ashwin Singh; Claas Voelcker; Danica J. Sutherland; Davide Locatelli; Eva Breznik; Filip Klubicka; Hang Yuan; Hetvi Jethwani; Huan Zhang 009; et al. (FAccT, 2023)

Queer In AI: A Case Study in Community-Led Participatory AI. - Anaelia Ovalle; Arjun Subramonian; Ashwin Singh; Claas Voelcker; Danica J. Sutherland; Davide Locatelli; Eva Breznik; Filip Klubicka; Hang Yuan; Hetvi Jethwani; Huan Zhang 009; Jaidev Shriram; et al. (CoRR, 2023)

Weisfeiler and Lehman Go Measurement Modeling: Probing the Validity of the WL Test. - Arjun Subramonian; Adina Williams; Maximilian Nickel; Yizhou Sun; Levent Sagun (CoRR, 2023)

Bound by the Bounty: Collaboratively Shaping Evaluation Processes for Queer AI Harms. - Organizers Of QueerInAI; Nathaniel Dennler; Anaelia Ovalle; Ashwin Singh; Luca Soldaini; Arjun Subramonian; Huy Tu; William Agnew; Avijit Ghosh; Kyra Yee; Irene Font Peradejordi; Zeerak Talat; et al. (CoRR, 2023)

Understanding "Democratization" in NLP and ML Research. - Arjun Subramonian; Vagrant Gautam; Dietrich Klakow; Zeerak Talat (EMNLP, 2024)

Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction. - Arjun Subramonian; Levent Sagun; Yizhou Sun (ICML, 2024)

Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks. - Arjun Subramonian; Jian Kang 008; Yizhou Sun (NeurIPS, 2024)

Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation. - Yixin Wan; Arjun Subramonian; Anaelia Ovalle; Zongyu Lin; Ashima Suvarna; Christina Chance; Hritik Bansal; Rebecca Pattichis; Kai-Wei Chang (CoRR, 2024)

Stop! In the Name of Flaws: Disentangling Personal Names and Sociodemographic Attributes in NLP. - Vagrant Gautam; Arjun Subramonian; Anne Lauscher; Os Keyes (CoRR, 2024)

Motif-Driven Contrastive Learning of Graph Representations. - Shichang Zhang; Ziniu Hu; Arjun Subramonian; Yizhou Sun (IEEE Trans. Knowl. Data Eng., 2024)

Strong Model Collapse. - Elvis Dohmatob; Yunzhen Feng; Arjun Subramonian; Julia Kempe (ICLR, 2025)

An Effective Theory of Bias Amplification. - Arjun Subramonian; Samuel J. Bell; Levent Sagun; Elvis Dohmatob (ICLR, 2025)

SHADES: Towards a Multilingual Assessment of Stereotypes in Large Language Models. - Margaret Mitchell; Giuseppe Attanasio; Ioana Baldini; Miruna Clinciu; Jordan Clive; Pieter Delobelle; Manan Dey; Sil Hamilton; Timm Dill; Jad Doughman; Ritam Dutt; Avijit Ghosh; et al. (NAACL (Long Papers), 2025)

Pairwise Matching of Intermediate Representations for Fine-grained Explainability. - Lauren Shrack; Timm Haucke; Antoine Salaün; Arjun Subramonian; Sara Beery (CoRR, 2025)

auto-fpt: Automating Free Probability Theory Calculations for Machine Learning Theory. - Arjun Subramonian; Elvis Dohmatob (CoRR, 2025)

Agree to Disagree? A Meta-Evaluation of LLM Misgendering. - Arjun Subramonian; Vagrant Gautam; Preethi Seshadri; Dietrich Klakow; Kai-Wei Chang; Yizhou Sun (CoRR, 2025)