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Akshay Krishnamurthy

Akshay Krishnamurthy has 2 papers in our library, in 2019–2024, most of them on Language models & pretraining; often with Alekh Agarwal, John C. Langford.

2 papers in our library · 2019–2024

OpenAlex · ORCID · h-index 24 · 125 works · 2,403 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 108 times in our library · ⭐ Paper Stars ranking: #5118

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

🏢MicrosoftRedmond · United States2016–2026 · 28 paperscurrent🏢Microsoft Research New York CityNew York · United States2016–2026 · 28 paperscurrent🎓University of California, BerkeleyBerkeley · United States2020–2026 · 2 paperscurrent🎓University of Massachusetts AmherstAmherst Center · United States2014–2021 · 23 papers🎓Carnegie Mellon UniversityPittsburgh · United States2012–2015 · 8 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 1 work

 Works by year

2020s

20241 paper

2010s

20191 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds (2019 · 362 citations)
    The paper addresses batch active learning for deep neural networks. The task is to select a whole group of unlabeled examples for labeling at once while spending as little annotator effort as possible. Methods that consider only model uncertainty often fill a batch with nearly identical examples. Methods that seek…
  2. Self-Improvement in Language Models: The Sharpening Mechanism (2024 · 20 citations)
    The paper asks why language models can improve themselves without external feedback, given that self-training cannot add information the model lacks.

 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 108 times (of the 149,439 papers whose bibliographies we read); 10 citing papers with a page here →.

Updated

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