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Tarun Kumar

Tarun Kumar has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Text generation, Vision & language; often with Martin Foltin, Suparna Bhattacharya.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 6 · 15 works · 87 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 4 times in our library · ⭐ Paper Stars ranking: #10602

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢Hewlett Packard EnterprisePalo Alto · United States2026 · 1 papercurrent🎓Indian Institute of Technology MadrasChennai · India2019–2023 · 4 papers🎓Indian Institute of Science BangaloreBengaluru · India2024–2025 · 2 papers🏢Robert BoschBengaluru · India2019 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 1 workText generation · 1 workVision & language · 1 work

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. SubLIME: Subset Selection via Rank Correlation Prediction for Data-Efficient LLM Evaluation (2025 · 5 citations)
    Running every new LLM on every benchmark has become very expensive. Existing data-efficient evaluation methods usually rely on many prior model results, which a newly added benchmark does not have. SubLIME, from Hewlett Packard Labs, addresses this cold-start case. It picks a small subset, 1–20% of a benchmark's…
  2. CEBC: Conformal Evidence-Bounded Control for Low-Hallucination Vision–Language Generation (2026)
    Vision–language models often mention objects that are not in the image. Existing remedies tend either to make outputs vaguer or to require costly retraining. The Hewlett Packard Labs authors propose CEBC, a post-generation layer that works with any VLM and needs no training. It first produces a small pool of…

 Co-authors here

2 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 4 times (of the 149,439 papers whose bibliographies we read).

Updated

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