| The malicious use of artificial intelligence: Forecasting, prevention, and mitigation M Brundage, S Avin, J Clark, H Toner, P Eckersley, B Garfinkel, A Dafoe, ... arXiv preprint arXiv:1802.07228, 2018 | 663 | 2018 |
| Towards Robust Evaluations of Continual Learning S Farquhar, Y Gal arXiv preprint arXiv:1805.09733, 2018 | 217 | 2018 |
| Benchmarking Bayesian Deep Learning with Diabetic Retinopathy Diagnosis A Filos, S Farquhar, AN Gomez, TGJ Rudner, Z Kenton, L Smith, ... Preprint, 2019 | 84* | 2019 |
| Radial Bayesian Neural Networks: Robust Variational Inference In Big Models S Farquhar, M Osborne, Y Gal Proceedings of the International Conference on Artificial Intelligence and …, 2020 | 54* | 2020 |
| A Unifying Bayesian View of Continual Learning S Farquhar, Y Gal Bayesian Deep Learning Workshop at NeurIPS arXiv:1902.06494, 2018 | 53 | 2018 |
| Global Catastrophic Risks O Cotton-Barratt, S Farquhar, J Halstead, S Schubert, A Snyder-Beattie Global Challenges Foundation, 2016 | 48* | 2016 |
| Uncertainty Baselines: Benchmarks for uncertainty & robustness in deep learning Z Nado, N Band, M Collier, J Djolonga, MW Dusenberry, S Farquhar, ... arXiv preprint arXiv:2106.04015, 2021 | 43 | 2021 |
| On Statistical Bias In Active Learning: How and When To Fix It S Farquhar, Y Gal, T Rainforth International Conference on Learning Representations, 2021 | 40 | 2021 |
| Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations S Farquhar, L Smith, Y Gal Advances in Neural Information Processing Systems, 2020 | 33 | 2020 |
| Existential Risk: Diplomacy and Governance S Farquhar, J Halstead, O Cotton-Barratt, S Schubert, H Belfield, ... Global Priorities Project, 2017 | 30* | 2017 |
| Active Testing: Sample-Efficient Model Evaluation J Kossen, S Farquhar, Y Gal, T Rainforth International Conference on Machine Learning, 2021 | 17 | 2021 |
| Single Shot Structured Pruning Before Training J van Amersfoort, M Alizadeh, S Farquhar, N Lane, Y Gal arXiv preprint arXiv:2007.00389, 2020 | 15 | 2020 |
| Pricing externalities to balance public risks and benefits of research S Farquhar, O Cotton-Barratt, A Snyder-Beattie Health security 15 (4), 401-408, 2017 | 12* | 2017 |
| Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt S Mindermann, JM Brauner, MT Razzak, M Sharma, A Kirsch, W Xu, ... International Conference on Machine Learning, 15630-15649, 2022 | 10 | 2022 |
| Try Depth Instead of Weight Correlations: Mean-field is a Less Restrictive Assumption for Variational Inference in Deep Networks S Farquhar, L Smith, Y Gal Bayesian Deep Learning Workshop at NeurIPS, 2019 | 9* | 2019 |
| Differentially Private Continual Learning S Farquhar, Y Gal Privacy in Machine Learning and AI workshop at the International Conference …, 2018 | 9 | 2018 |
| A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions A Kirsch, S Farquhar, Y Gal arXiv preprint arXiv:2106.12059, 2021 | 8 | 2021 |
| Path-Specific Objectives for Safer Agent Incentives S Farquhar, R Carey, T Everitt AAAI, 2022 | 7 | 2022 |
| Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients M Alizadeh, SA Tailor, LM Zintgraf, J van Amersfoort, S Farquhar, ... International Conference on Learning Representations, 2022 | 7 | 2022 |
| CLAM: Selective Clarification for Ambiguous Questions with Large Language Models L Kuhn, Y Gal, S Farquhar arXiv preprint arXiv:2212.07769, 2022 | 1 | 2022 |
Yarin GalAssociate Professor, University of OxfordZweryfikowany adres z cs.ox.ac.uk
