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Gabriele Oliaro

Gabriele Oliaro has 2 papers in our library, in 2023, most of them on Language models & pretraining; often with Xinhao Cheng, Xupeng Miao, Zhihao Zhang.

2 papers in our library · 2023

OpenAlex · ORCID · Wikidata · h-index 11 · 50 works · 912 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 155 times in our library · ⭐ Paper Stars ranking: #5312

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

🎓Carnegie Mellon UniversityPittsburgh · United States2023–2026 · 12 paperscurrent🔬Princeton Plasma Physics LaboratoryPrinceton · United States1989–2005 · 22 papers🎓Princeton UniversityPrinceton · United States1989–2005 · 12 papers🎓Tsinghua UniversityBeijing · China2024 · 3 papers🎓Stanford UniversityStanford · United States2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 8 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 worksHardware and Architecture · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 2 works

 Works by year

2020s

20232 papers

 Papers

The most cited first (OpenAlex and our library).

  1. SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification (2023 · 220 citations)
    Serving generative LLMs is slow and expensive because autoregressive decoding emits one token per step, which leaves GPUs underused and makes key-value caches costly in memory.
  2. Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems (2023 · 110 citations)
    Generative LLMs are expensive to deploy. Their size and token-by-token generation make low-latency, high-throughput serving difficult, especially for organizations without large compute budgets. This survey by researchers at Purdue and Carnegie Mellon reviews the field from a machine-learning-systems perspective. It…

 Co-authors here

6 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 155 times (of the 149,439 papers whose bibliographies we read); 26 citing papers with a page here →.

Updated

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