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Alekh Agarwal

Alekh Agarwal has 2 papers in our library, in 2019–2024, most of them on Language models & pretraining, Reasoning; often with Adam Fisch, Amrith Setlur, Aviral Kumar.

2 papers in our library · 2019–2024

OpenAlex · ORCID · h-index 40 · 152 works · 5,228 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 211 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #4484

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

🏢GoogleMountain View · United States2021–2026 · 24 paperscurrent🏢MicrosoftRedmond · United States2014–2021 · 41 papers🎓University of California, BerkeleyBerkeley · United States2008–2013 · 24 papers🏢Microsoft Research New York CityNew York · United States2011–2018 · 20 papers🎓Indian Institute of Technology BombayMumbai · India2006–2007 · 4 papers🎓University of Southern CaliforniaLos Angeles · United States2023–2024 · 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 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 2Research subjects and languages the work is about.Language models & pretraining · 1 workReasoning · 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. Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning (2024 · 93 citations)
    Process reward models (PRMs) score every step of a reasoning trace instead of only the final answer. Step labels written by humans are expensive, though, and earlier automatically labelled PRMs gave only marginal gains over outcome reward models (ORMs). This work asks what a step-level reward should actually measure.…

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Deep Batch Active Learning by Diverse, Uncertain Gradient Lower BoundsInternational Conference on Learning Representations (ICLR)Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, et al.2019 · 362 citations
  2. Rewarding Progress: Scaling Automated Process Verifiers for LLM ReasoningarXiv:2410.08146Amrith Setlur, Chirag Nagpal, Adam Fisch, et al.2024 · 93 citations
  3. Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting ↗arXiv (Cornell University) · no summary yetAditya Grover et al.2019 · 80 citations
  4. Peer Reviews of Peer Reviews: A Randomized Controlled Trial and Other Experiments ↗arXiv:2311.09497 · no summary yetAlexander Goldberg et al.2023 · 32 citations

 Co-authors here

8 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 211 times (of the 149,439 papers whose bibliographies we read); 29 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/alekh-agarwal/