🧑‍🔬 Andrew Gordon Wilson

🏛️ Affiliations

🎓New York UniversityNew York · United States2018–2026 · 54 paperscurrent🎓Cornell UniversityIthaca · United States2016–2025 · 13 papers🎓Carnegie Mellon UniversityPittsburgh · United States2014–2016 · 7 papers🎓Courant Institute of Mathematical SciencesNew York · United States2019–2022 · 5 papers🎓University of CambridgeCambridge · United Kingdom2011–2015 · 4 papers🎓Pennsylvania State UniversityState College · United States2015 · 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.

📅 Works by year

2020s

2010s

📄 Papers

The most cited first (OpenAlex and our library).

1. Averaging Weights Leads to Wider Optima and Better Generalization (2018 · 366 citations)
   Deep networks are usually trained with SGD and a decaying learning rate, and the final iterate tends to sit near the edge of a broad region of good solutions rather than in its middle.
2. Large Language Models Are Zero-Shot Time Series Forecasters (2023 · 268 citations)
   Time series forecasting has lacked large-scale pretraining. Data come from very different sources and scales, values are often missing, and no standard self-supervised objective exists. As a result, simple methods such as ARIMA frequently beat deep networks. Gruver, Finzi, Qiu and Wilson propose LLMTime, which writes…

All works

9 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. Averaging Weights Leads to Wider Optima and Better GeneralizationarXiv:1803.05407Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, et al.2018 · 366 citations
2. Large Language Models Are Zero-Shot Time Series ForecastersConference on Neural Information Processing Systems (NeurIPS)Nate Gruver, Marc Finzi, Shikai Qiu, et al.2023 · 268 citations
3. Probabilistic FastText for Multi-Sense Word Embeddings ↗Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetBen Athiwaratkun, Andrew Wilson, Anima Anandkumar2018 · 182 citations
4. Multimodal Word Distributions ↗Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetBen Athiwaratkun, Andrew Wilson2017 · 123 citations
5. Stochastic Variational Deep Kernel Learning ↗arXiv (Cornell University) · no summary yetAndrew Gordon Wilson et al.2016 · 105 citations
6. Deep Kernel Learning ↗arXiv (Cornell University) · no summary yetAndrew Gordon Wilson et al.2015 · 42 citations
7. A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups ↗arXiv (Cornell University) · no summary yetMarc Finzi, Max Welling, Andrew Gordon Wilson2021 · 35 citations
8. Perspectives on the State and Future of Deep Learning - 2023 ↗arXiv (Cornell University) · no summary yetMicah Goldblum et al.2023 · 5 citations
9. Response to Promises and Pitfalls of Deep Kernel Learning ↗arXiv (Cornell University) · no summary yetAndrew Gordon Wilson et al.2025

🤝 Co-authors here

1 co-author with a page, the most papers together first.

• Pavel Izmailov

Venues

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

• Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) ×2
• Conference on Neural Information Processing Systems (NeurIPS): NeurIPS 2023

🔗 Cited by

Papers of our library cite their works 325 times (of the 149,439 papers whose bibliographies we read); 36 citing papers with a page here →.

Updated 2 November 2026

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