Gal, Yarin

Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Gal, Y., & Ghahramani, Z. (2015) · arXiv

Gaussian Processes and Bayesian InferenceAdversarial Robustness in Machine Learning

Cited by 6,471

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

Kendall, A., & Gal, Y. (2017) · arXiv

Advanced Neural Network ApplicationsAdversarial Robustness in Machine Learning

Cited by 3,088

Detecting hallucinations in large language models using semantic entropy

Farquhar, S., Kossen, J., Kuhn, L., & Gal, Y. (2024) · Nature

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 934

Deep Bayesian Active Learning with Image Data

Gal, Y., Islam, R., & Ghahramani, Z. (2017) · arXiv

Machine Learning and AlgorithmsAdvanced Bandit Algorithms Research

Cited by 900

AI models collapse when trained on recursively generated data

Shumailov, I., Shumaylov, Z., Zhao, Y., Papernot, N., Anderson, R., & Gal, Y. (2024) · Nature

Topic ModelingEthics and Social Impacts of AI

Cited by 793

Disease variant prediction with deep generative models of evolutionary data

Frazer, J., Notin, P., Dias, M., Gomez, A., Min, J. K., Brock, K., Gal, Y., & Marks, D. S. (2021) · Nature

Genomics and Rare DiseasesGenetic Associations and Epidemiology

Cited by 791

Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation

Kuhn, L., Gal, Y., & Farquhar, S. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 700

Towards global flood mapping onboard low cost satellites with machine learning

Mateo-Garcia, G., Veitch-Michaelis, J., Smith, L., Oprea, S. V., Schumann, G., Gal, Y., Baydin, A. G., & Backes, D. (2021) · Scientific Reports

Flood Risk Assessment and ManagementRemote-Sensing Image Classification

Cited by 202

Learning Invariant Representations for Reinforcement Learning without Reconstruction

Zhang, A., McAllister, R., Calandra, R., Gal, Y., & Levine, S. (2020) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 165

Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning

Walmsley, M., Smith, L., Lintott, C., Gal, Y., Bamford, S., Dickinson, H., Fortson, L., Kruk, S., Masters, K., Scarlata, C., et al. (2020) · Monthly Notices of the Royal Astronomical Society

Galaxies: Formation, Evolution, PhenomenaAdvanced Neural Network Applications

Cited by 160

The Curse of Recursion: Training on Generated Data Makes Models Forget

Shumailov, I., Shumaylov, Z., Zhao, Y., Gal, Y., Papernot, N., & Anderson, R. (2023) · arXiv

Reinforcement Learning in RoboticsScientific Computing and Data Management

Cited by 151

Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval

Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A., Marks, D. S., & Gal, Y. (2022) · arXiv

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 118

Deep Deterministic Uncertainty: A New Simple Baseline

Mukhoti, J., Kirsch, A., van Amersfoort, J., Torr, P. H. S., & Gal, Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Adversarial Robustness in Machine LearningAdvanced Neural Network Applications

Cited by 116

VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning

Zintgraf, L., Shiarlis, K., Igl, M., Schulze, S., Gal, Y., Hofmann, K., & Whiteson, S. (2019) · arXiv

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 112

Measuring what Matters: Construct Validity in Large Language Model Benchmarks

Bean, A. M., Kearns, R. O., Romanou, A., Hafner, F. S., Mayne, H., Batzner, J., Foroutan Eghlidi, N., Schmitz, C., Korgul, K., Batra, H., et al. (2025) · Advances in Neural Information Processing Systems 38

Topic ModelingNatural Language Processing Techniques

Cited by 110

Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning

Farquhar, S., Osborne, M., & Gal, Y. (2019) · arXiv

Gaussian Processes and Bayesian InferenceAdversarial Robustness in Machine Learning

Cited by 43

Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt

Mindermann, S., Brauner, J., Razzak, M., Sharma, M., Kirsch, A., Xu, W., Höltgen, B., Gomez, A. N., Morisot, A., Farquhar, S., et al. (2022) · arXiv

Machine Learning and Data ClassificationStochastic Gradient Optimization Techniques

Cited by 28

AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents

Andriushchenko, M., Souly, A., Dziemian, M., Duenas, D., Lin, M., Wang, J., Hendrycks, D., Zou, A., Kolter, Z., Fredrikson, M., et al. (2024) · arXiv

Adversarial Robustness in Machine LearningAccess Control and Trust

Cited by 11

Prediction-Oriented Bayesian Active Learning

Smith, F. B., Kirsch, A., Farquhar, S., Gal, Y., Foster, A., & Rainforth, T. (2023) · arXiv

Machine Learning and AlgorithmsGaussian Processes and Bayesian Inference

Cited by 10

In-Context Learning Learns Label Relationships but Is Not Conventional Learning

Kossen, J., Gal, Y., & Rainforth, T. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 5

Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control

Gupta, G., Yadav, K., Gal, Y., Kira, Z., Batra, D., Lu, C., & Rudner, T. (2024) · Advances in Neural Information Processing Systems 37

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 5

Open technical problems in open-weight ai model risk management

Casper, S., O'Brien, K., Longpre, S., Seger, E., Klyman, K., Bommasani, R., Nrusimha, A., Shumailov, I., Mindermann, S., Basart, S., et al. (2025) · SSRN Electronic Journal

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 3

A Definition of AGI

Hendrycks, D., Song, D., Szegedy, C., Lee, H., Gal, Y., Brynjolfsson, E., Li, S., Zou, A., Levine, L., Han, B., et al. (2025) · arXiv

Psychiatry, Mental Health, NeuroscienceArtificial Intelligence Applications

Cited by 3

FindingDory: A Benchmark to Evaluate Memory in Embodied Agents

Yadav, K., Ali, Y., Gupta, G., Gal, Y., & Kira, Z. (2025) · arXiv

Multimodal Machine Learning ApplicationsRobot Manipulation and Learning

Cited by 1