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Roger Grosse

Roger Grosse has 3 papers in our library, in 2020–2023, most of them on Reasoning, Language models & pretraining, Evaluation & benchmarks; often with Jimmy Lei Ba, Yuhuai Wu, Albert Q. Jiang.

3 papers in our library · 2020–2023

OpenAlex · h-index 31 · 99 works · 7,450 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 964 times in our library · 22 more in our library without a page yet · ⭐ Paper Stars ranking: #6931

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

🎓University of TorontoToronto · Canada2014–2026 · 63 paperscurrent🔬Vector InstituteToronto · Canada2017–2025 · 27 papers🎓Massachusetts Institute of TechnologyCambridge · United States2009–2024 · 6 papers🤝Machine Intelligence Research InstituteBerkeley · United States2022–2023 · 2 papers🔬MIT Computer Science and Artificial Intelligence LaboratoryCambridge · United States2011–2012 · 2 papers🎓Stanford UniversityStanford · United States2007–2009 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 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 5The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArtificial Intelligence · 3 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 worksComputational Theory and Mathematics · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 1 workReasoning · 2 worksEvaluation & benchmarks · 1 work

 Works by year

2020s

20231 paper20211 paper20201 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Studying Large Language Model Generalization with Influence Functions (2023 · 105 citations)
    Knowing which training examples drive a given behavior in a large language model could help researchers anticipate and reduce its risks.
  2. INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving (2020 · 41 citations)
    Learning-based theorem provers struggle with two issues: formal proof corpora are small, and useful target theorems often differ substantially from those seen in training.
  3. LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning (2021 · 39 citations)
    The paper asks whether the inductive bias needed for mathematical reasoning can be taught to a generic transformer through data rather than through specialized architectures.

All works

25 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. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations ↗International Conference on Machine Learning · no summary yetHonglak Lee et al.2009 · 2,412 citations
  2. Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation ↗arXiv (Cornell University) · no summary yetYuhuai Wu et al.2017 · 450 citations
  3. Importance Weighted Autoencoders ↗arXiv (Cornell University) · no summary yetYuri Burda, Roger Grosse, Ruslan Salakhutdinov2015 · 316 citations
  4. Toy Models of Superposition ↗arXiv:2209.10652 · no summary yetNelson Elhage et al.2022 · 277 citations
  5. Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches ↗arXiv:1803.04386 · no summary yetYeming Wen et al.2018 · 189 citations
  6. Studying Large Language Model Generalization with Influence FunctionsarXiv:2308.03296Roger Grosse, Juhan Bae, Cem Anil, et al.2023 · 105 citations
  7. Distributed Second-Order Optimization using Kronecker-Factored Approximations ↗International Conference on Learning Representations · no summary yetJimmy Lei Ba, Roger Grosse, James Martens2017 · 73 citations
  8. Accurate and Conservative Estimates of MRF Log-likelihood using Reverse Annealing ↗arXiv (Cornell University) · no summary yetYuri Burda, Roger Grosse, Ruslan Salakhutdinov2014 · 43 citations
  9. INT: An Inequality Benchmark for Evaluating Generalization in Theorem ProvingarXiv:2007.02924Yuhuai Wu, Albert Qiaochu Jiang, Jimmy Ba, et al.2020 · 41 citations
  10. On the Quantitative Analysis of Decoder-Based Generative Models ↗arXiv (Cornell University) · no summary yetYuhuai Wu et al.2016 · 41 citations
  11. LIME: Learning Inductive Bias for Primitives of Mathematical ReasoningarXiv:2101.06223Yuhuai Wu, Markus Rabe, Wenda Li, et al.2021 · 39 citations
  12. Exploiting compositionality to explore a large space of model structures ↗arXiv (Cornell University) · no summary yetRoger Grosse et al.2012 · 33 citations
  13. Reversible Recurrent Neural Networks ↗arXiv (Cornell University) · no summary yetMatthew Mackay et al.2018 · 30 citations
  14. Adversarial Distillation of Bayesian Neural Network Posteriors ↗arXiv (Cornell University) · no summary yetKuan-Chieh Wang et al.2018 · 29 citations
  15. Learning Wake-Sleep Recurrent Attention Models ↗arXiv (Cornell University) · no summary yetJimmy Lei Ba et al.2015 · 29 citations
  16. The Scattering Compositional Learner: Discovering Objects, Attributes, Relationships in Analogical Reasoning ↗arXiv (Cornell University) · no summary yetYuhuai Wu et al.2020 · 27 citations
  17. Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes ↗arXiv (Cornell University) · no summary yetJames Lucas et al.2021 · 13 citations
  18. When Does Preconditioning Help or Hurt Generalization? ↗arXiv (Cornell University) · no summary yetШун-ичи Амари et al.2020 · 11 citations
  19. Aggregated Momentum: Stability Through Passive Damping ↗arXiv (Cornell University) · no summary yetJames Lucas et al.2018 · 10 citations
  20. Sabotage Evaluations for Frontier Models ↗arXiv (Cornell University) · no summary yetJoe Benton et al.2024 · 7 citations
  21. Second-order Optimization for Deep Reinforcement Learning using Kronecker-factored Approximation ↗neural information processing systems · no summary yetYuhuai Wu et al.2017 · 7 citations
  22. Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve ↗arXiv (Cornell University) · no summary yetJuhan Bae et al.2022 · 4 citations
  23. Forecasting Rare Language Model Behaviors ↗arXiv (Cornell University) · no summary yetErik Jones et al.2025 · 3 citations
  24. On Monotonic Linear Interpolation of Neural Network Parameters ↗International Conference on Machine Learning · no summary yetJames Lucas et al.2021 · 1 citation
  25. Accurate and Conservative Estimates of MRF Log-likelihood using Reverse Annealing. arXiv:1412.8566 ↗no summary yetYuri Burda, Roger Grosse, Ruslan Salakhutdinov2014

 Co-authors here

12 of 15 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 964 times (of the 149,439 papers whose bibliographies we read); 254 citing papers with a page here →.

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