---
title: "Sham M. Kakade — author profile and papers in our library"
description: "Sham M. Kakade, Harvard University: 3 papers on Elamur Research (2024–2025), h-index 66 — affiliations, topics, co-authors and who cites…"
canonical: "https://research.elamur.ai/free/en/authors/sham-m-kakade/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/sham-m-kakade/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Sham M. Kakade

# Sham M. Kakade

Sham M. Kakade has 3 papers in our library, in 2024–2025, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/); often with [Eran Malach](https://md.research.elamur.ai/free/en/authors/eran-malach/), [Samy Jelassi](https://md.research.elamur.ai/free/en/authors/samy-jelassi/), [Benjamin L. Edelman](https://md.research.elamur.ai/free/en/authors/benjamin-l-edelman/).

3 papers in our library · 2024–2025

[OpenAlex](https://openalex.org/A5108381794) · [**h-index 66**](https://openalex.org/authors/A5108381794) · [275 works](https://openalex.org/works?filter=author.id:A5108381794) · [19,051 citations](https://openalex.org/works?filter=author.id:A5108381794&sort=cited_by_count:desc) OpenAlex, 2026-10-14

[📄 3 works in our library](https://md.research.elamur.ai/free/en/authors/sham-m-kakade/#papers) · [🔗 Cited 335 times in our library](https://md.research.elamur.ai/free/en/authors/sham-m-kakade/cited-by/) · [13 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #8320](https://md.research.elamur.ai/free/en/paper-stars/page/84/#star-8320)

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

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Sham+M.+Kakade) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Sham+M.+Kakade) · [dblp](https://dblp.org/search?q=Sham+M.+Kakade) · [ACL Anthology](https://aclanthology.org/search/?q=Sham+M.+Kakade)

## Affiliations

[🎓**Harvard University** Cambridge · United States 2021–2026 · 51 papers*current*](https://md.research.elamur.ai/free/en/universities/harvard-university/) · [🎓**University of Washington** Seattle · United States 2015–2023 · 62 papers](https://md.research.elamur.ai/free/en/universities/university-of-washington/) · [🏢**Microsoft** Redmond · United States 2009–2022 · 32 papers](https://md.research.elamur.ai/free/en/companies/microsoft/) · [🎓**University of Pennsylvania** Philadelphia · United States 2004–2018 · 25 papers](https://md.research.elamur.ai/free/en/universities/university-of-pennsylvania/) · [🎓**Toyota Technological Institute at Chicago** Chicago · United States 2005–2009 · 23 papers](https://md.research.elamur.ai/free/en/universities/toyota-technological-institute-at-chicago/) · [🏢**Microsoft Research New England** Cambridge · United States 2009–2015 · 18 papers](https://ror.org/010wkqn50) · [🔬**Gatsby Computational Neuroscience Unit** London · United Kingdom 1999–2002 · 9 papers](https://ror.org/0269epf40) · [🎓**University College London** London · United Kingdom 2000–2003 · 7 papers](https://md.research.elamur.ai/free/en/universities/university-college-london/) · [🏢**Amazon** Seattle · United States 2018–2024 · 6 papers](https://md.research.elamur.ai/free/en/companies/amazon/) · [🏢**Microsoft Research New York City** New York · United States 2021–2022 · 3 papers](https://md.research.elamur.ai/free/en/companies/microsoft-research-new-york-city/)

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 15 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 5 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/)

🧭 Topics 1 Research subjects and languages the work is about.[Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/)

## Works by year

### 2020s

[**2025** 1 paper](https://md.research.elamur.ai/free/en/authors/sham-m-kakade/year/2025/) · [**2024** 2 papers](https://md.research.elamur.ai/free/en/authors/sham-m-kakade/year/2024/)

## Papers

The most cited first (OpenAlex and our library).

1. **[Repeat After Me: Transformers are Better than State Space Models at Copying](https://md.research.elamur.ai/free/en/papers/jelassi-2024-repeat-after-me-transformers-better/)** *(2024 · 57 citations)* The paper asks what models with a constant-size memory give up in exchange for cheap inference. The authors call this family "generalized state space models" (GSSMs). It includes S4, Mamba, LSTMs and linear-attention variants. The authors find that the main loss is the ability to reproduce or retrieve arbitrary parts…
2. **[Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining](https://md.research.elamur.ai/free/en/papers/zhao-2025-echo-chamber-rl-post-training/)** *(2025 · 34 citations)* The paper asks why reinforcement-learning fine-tuning improves mathematical reasoning in language models. Pretraining data for popular base models such as Qwen is usually undisclosed, which makes it hard to separate the effect of RL from the effect of pretraining. To get around this, the authors train decoder-only…
3. **[Transcendence: Generative Models Can Outperform The Experts That Train Them](https://md.research.elamur.ai/free/en/papers/zhang-2024-transcendence-generative-models-outperform-experts/)** *(2024 · 12 citations)* Models trained with cross-entropy learn to mimic the distribution of their training data. This suggests they cannot outperform the humans who produced that data. The authors examine this assumption and define "transcendence" as a generative model earning higher reward than every expert behind its training set.

## All works

16 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. **[Cover trees for nearest neighbor ↗](https://doi.org/10.1145/1143844.1143857) International Conference on Machine Learning · no summary yet**Alina Beygelzimer, Sham M. Kakade, John C. Langford*2006 · 825 citations*
2. **[Multi-view clustering via canonical correlation analysis ↗](https://doi.org/10.1145/1553374.1553391) International Conference on Machine Learning · no summary yet**Kamalika Chaudhuri et al.*2009 · 791 citations*
3. **[Multi-Label Prediction via Compressed Sensing ↗](https://openalex.org/W2115119296) neural information processing systems · no summary yet**John C. Langford et al.*2009 · 255 citations*
4. [**Repeat After Me: Transformers are Better than State Space Models at Copying arXiv:2402.01032**Samy Jelassi, David Brandfonbrener, Sham M. Kakade, et al.*2024 · 57 citations*](https://md.research.elamur.ai/free/en/papers/jelassi-2024-repeat-after-me-transformers-better/)
5. **[Identifiability and Unmixing of Latent Parse Trees ↗](https://arxiv.org/abs/1206.3137) arXiv (Cornell University) · no summary yet**Daniel Hsu, Sham M. Kakade, Percy Liang*2012 · 38 citations*
6. [**Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining arXiv:2504.07912**Rosie Zhao, Alexandru Meterez, Sham Kakade, et al.*2025 · 34 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2025-echo-chamber-rl-post-training/)
7. **[Prediction with a short memory ↗](https://doi.org/10.1145/3188745.3188954) ACM SIGACT Symposium on Theory of Computing (STOC) · no summary yet**Vatsal Sharan et al.*2016 · 29 citations*
8. **[LoRA Soups: Merging LoRAs for Practical Skill Composition Tasks ↗](https://aclanthology.org/2025.coling-industry.55/) Proceedings of the 31st International Conference on Computational Linguistics: Industry Track · no summary yet**Akshara Prabhakar et al.*2025 · 14 citations*
9. **[Transcendence: Generative Models Can Outperform The Experts That Train Them](https://md.research.elamur.ai/free/en/papers/zhang-2024-transcendence-generative-models-outperform-experts/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Edwin Zhang, Vincent Zhu, Naomi Saphra, et al.*2024 · 12 citations*
10. **[Learning Overcomplete HMMs ↗](https://arxiv.org/abs/1711.02309) arXiv (Cornell University) · no summary yet**Vatsal Sharan et al.*2017 · 11 citations*
11. **[Instabilities of Offline RL with Pre-Trained Neural Representation ↗](https://arxiv.org/abs/2103.04947) arXiv (Cornell University) · no summary yet**Ruosong Wang et al.*2021 · 9 citations*
12. **[Mixture of Parrots: Experts improve memorization more than reasoning ↗](https://arxiv.org/abs/2410.19034) arXiv (Cornell University) · no summary yet**Samy Jelassi et al.*2024 · 4 citations*
13. **[Matching Features, Not Tokens: Energy-Based Fine-Tuning of Language Models ↗](https://arxiv.org/abs/2603.12248) arXiv (Cornell University) · no summary yet**Samy Jelassi et al.*2026 · 1 citation*
14. **[Superposed Decoding: Multiple Generations from a Single Autoregressive Inference Pass ↗](https://doi.org/10.52202/079017-3774) neural information processing systems · no summary yet**Ethan Shen et al.*2024 · 1 citation*
15. **[Domain Adaptation: Overfitting and Small Sample Statistics ↗](https://arxiv.org/abs/1105.0857) arXiv (Cornell University) · no summary yet**Dean P. Foster, Sham M. Kakade, Ruslan Salakhutdinov*2011 · 1 citation*
16. **[Domain Adaptation: A Small Sample Statistical Approach ↗](https://openalex.org/W2185249064) International Conference on Artificial Intelligence and Statistics · no summary yet**Ruslan Salakhutdinov, Sham M. Kakade, Dean P. Foster*2012*

## Co-authors here

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

- [Eran Malach](https://md.research.elamur.ai/free/en/authors/eran-malach/) ×3
- [Samy Jelassi](https://md.research.elamur.ai/free/en/authors/samy-jelassi/) ×2
- [Benjamin L. Edelman](https://md.research.elamur.ai/free/en/authors/benjamin-l-edelman/)
- [David Brandfonbrener](https://md.research.elamur.ai/free/en/authors/david-brandfonbrener/)
- [Naomi Saphra](https://md.research.elamur.ai/free/en/authors/naomi-saphra/)

## Venues

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

- [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/) ×2: [NeurIPS 2024](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2024/)
- International Conference on Machine Learning ×2
- neural information processing systems ×2
- ACM SIGACT Symposium on Theory of Computing (STOC)
- International Conference on Artificial Intelligence and Statistics
- Proceedings of the 31st International Conference on Computational Linguistics: Industry Track

## Cited by

Papers of our library cite their works 335 times (of the 149,439 papers whose bibliographies we read); [97 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/sham-m-kakade/cited-by/).

Updated 3 November 2026