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Author

Lisa Schut

Observed name: Schut, Lisa
Observed institution: University of Oxford
ORCID: 0009-0008-6760-5879
Observed ORCID: 0009-0008-6760-5879
OpenAlex ID: A5064019182
A benchmark of expert-level academic questions to assess AI capabilities
2026 · Long Phan, Alice Gatti, et al. · Nature · 61
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Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties
2021 · Schut, Lisa, Key, Oscar, et al. · arXiv (Cornell University) · 23
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Speedy Performance Estimation for Neural Architecture Search
2020 · Ru, Binxin, Lyle, Clare, et al. · arXiv (Cornell University) · 19
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Bridging the human–AI knowledge gap through concept discovery and transfer in AlphaZero
2025 · Lisa Schut, Nenad Tomašev, et al. · Proceedings of the National Academy of Sciences · 15
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A Bayesian Perspective on Training Speed and Model Selection
2020 · Lyle, Clare, Schut, Lisa, et al. · arXiv (Cornell University) · 11
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Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero
2023 · Schut, Lisa, Tomasev, Nenad, et al. · arXiv (Cornell University) · 9
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Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search
2021 · Binxin Ru, Clare Lyle, et al. · arXiv (Cornell University) · 8
Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs
2024 · Kossen, Jannik, Han, Jiatong, et al. · arXiv (Cornell University) · 6
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Diversifying AI: Towards Creative Chess with AlphaZero
2023 · Zahavy, Tom, Veeriah, Vivek, et al. · arXiv (Cornell University) · 4
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Speedy Performance Estimation for Neural Architecture Search
2021 · Robin Ru, Clare Lyle, et al. · neural information processing systems · 3
Capsule Networks -- A Probabilistic Perspective
2020 · Smith, Lewis, Schut, Lisa, et al. · arXiv (Cornell University) · 3
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Explaining Explainability: Recommendations for Effective Use of Concept Activation Vectors
2024 · Nicolson, Angus, Schut, Lisa, et al. · arXiv (Cornell University) · 2
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DeDUCE: Generating Counterfactual Explanations Efficiently
2021 · Höltgen, Benedikt, Schut, Lisa, et al. · arXiv (Cornell University) · 2
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Time pressure modulates resource-rational planning in online chess
2026 · Ionatan Kuperwajs, Evan Russek, et al.
COrigami: An AI Pipeline for Co-Designing Flat-Foldable Visually Recognisable Origami
2026 · Zahavy, Tom, Hou, Shaobo, et al. · arXiv (Cornell University)
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A benchmark of expert-level academic questions to assess AI capabilities
2026 · Center for AI Safety, Phan, Long, et al. · RWTH Publications (RWTH Aachen)
Learning in online chess increases with more time spent thinking and diversity of experience
2025 · Cognitive Science Society 2025, Gomes Mattar, Marcelo, et al. · eScholarship (California Digital Library)
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Exploring resource-rational planning under time pressure in online chess
2025 · Cognitive Science Society 2025, Gomes Mattar, Marcelo, et al. · eScholarship (California Digital Library)
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How machine learning models encode knowledge – and what we can learn from them
2025 · Schut, Lisa · Oxford University Research Archive (ORA) (University of Oxford)
Model-based Saliency for the Detection of Adversarial Examples
2019 · Lisa Schut, Yarin Gal
Works count: 21
Citation count: 170
H-index: 7
I10-index: 5
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