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researcher

Arjun Subramonian

Meta FAIR

29 papers hereh-index 154.2k citations39 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author9
  • middle author15

Across the 24 of 29 papers where every author was matched, so the position is known.

fields
  • cs.CL9
  • cs.LG7
  • cs.CY6
  • cs.AI3
  • cs.CV2
  • q-bio.PE1
affiliations
  • Meta FAIR
Homepage
same name
  • Arjun Subramonian — 1 paper
  • Arjun Subramonian — 1 paper, h 0

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20202026
most citedQueer In AI: A Case Study in Community-Led Participatory AI

65 citations · 145 across the 28 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.AI2025

OpenApps: Simulating Environment Variations to Measure UI-Agent Reliability

Karen Ullrich, Jingtong Su, Claudia Shi +7

Reliability is key to realizing the promise of autonomous UI-Agents, multimodal agents that directly interact with apps in the same manner as humans, as users must be able to trust…

cs.LG2025

auto-fpt: Automating Free Probability Theory Calculations for Machine Learning Theory

Arjun Subramonian, Elvis Dohmatob

A large part of modern machine learning theory often involves computing the high-dimensional expected trace of a rational expression of large rectangular random matrices. To symbol…

cs.CL2025

Agree to Disagree? A Meta-Evaluation of LLM Misgendering

Arjun Subramonian, Vagrant Gautam, Preethi Seshadri +3

Numerous methods have been proposed to measure LLM misgendering, including probability-based evaluations (e.g., automatically with templatic sentences) and generation-based evaluat…

cs.CV2025

Pairwise Matching of Intermediate Representations for Fine-grained Explainability

Lauren Shrack, Timm Haucke, Antoine Salaün +2

The differences between images belonging to fine-grained categories are often subtle and highly localized, and existing explainability techniques for deep learning models are often…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.