| Concrete dropout Y Gal, J Hron, A Kendall NeurIPS, 2017 | 736 | 2017 |
| Gaussian process behaviour in wide deep neural networks AGG Matthews, J Hron, M Rowland, RE Turner, Z Ghahramani ICLR, 2018 | 574* | 2018 |
| Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes R Novak, L Xiao, Y Bahri, J Lee, G Yang, J Hron, DA Abolafia, ... ICLR, 2019 | 379 | 2019 |
| Neural Tangents: Fast and Easy Infinite Neural Networks in Python R Novak, L Xiao, J Hron, J Lee, AA Alemi, J Sohl-Dickstein, ... ICLR, 2020 | 255 | 2020 |
| Infinite attention: NNGP and NTK for deep attention networks J Hron, Y Bahri, J Sohl-Dickstein, R Novak ICML, 2020 | 130 | 2020 |
| Variational Bayesian dropout: pitfalls and fixes J Hron, AGG Matthews, Z Ghahramani ICML, 2018 | 83 | 2018 |
| Successor Uncertainties: exploration and uncertainty in temporal difference learning D Janz*, J Hron*, JM Hernández-Lobato, K Hofmann, S Tschiatschek NeurIPS, 2019 | 66 | 2019 |
| Variational Gaussian Dropout is not Bayesian J Hron, AGG Matthews, Z Ghahramani Bayesian Deep Learning (NeurIPS workshop), 2017 | 60 | 2017 |
| Orthogonal Estimation of Wasserstein Distances M Rowland*, J Hron*, Y Tang*, K Choromanski, T Sarlos, A Weller AISTATS, 2019 | 48 | 2019 |
| Beyond human data: Scaling self-training for problem-solving with language models A Singh, JD Co-Reyes, R Agarwal, A Anand, P Patil, PJ Liu, J Harrison, ... arXiv preprint arXiv:2312.06585, 2023 | 44 | 2023 |
| Modeling content creator incentives on algorithm-curated platforms J Hron*, K Krauth*, MI Jordan, N Kilbertus, S Dean ICLR 2023, 2023 | 33 | 2023 |
| On component interactions in two-stage recommender systems J Hron, K Krauth, M Jordan, N Kilbertus NeurIPS, 2021 | 33 | 2021 |
| Exact posterior distributions of wide Bayesian neural networks J Hron, Y Bahri, R Novak, J Pennington, J Sohl-Dickstein Uncertainty in Deep Learning (ICML workshop), 2020 | 31 | 2020 |
| Sample-then-optimize posterior sampling for Bayesian linear models AGG Matthews, J Hron, RE Turner, Z Ghahramani Advances in Approximate Bayesian Inference (NeurIPS workshop), 2017 | 30 | 2017 |
| Exploration in two-stage recommender systems J Hron*, K Krauth*, MI Jordan, N Kilbertus REVEAL (ACM RecSys workshop), 2020 | 11 | 2020 |
| Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling J Hron, R Novak, J Pennington, J Sohl-Dickstein ICML, 2022 | 7 | 2022 |
| The progression of disparities within the criminal justice system: Differential enforcement and risk assessment instruments M Zilka, R Fogliato, J Hron, B Butcher, C Ashurst, A Weller Proceedings of the 2023 ACM Conference on Fairness, Accountability, and …, 2023 | 6 | 2023 |
| Optimising Human-Machine Collaboration for Efficient High-Precision Information Extraction from Text Documents B Butcher, M Zilka, J Hron, D Cook, A Weller ACM Journal on Responsible Computing 1 (2), 1-27, 2024 | 2 | 2024 |
| Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability J Hron, L Culp, G Elsayed, R Liu, B Adlam, M Bileschi, B Bohnet, ... arXiv preprint arXiv:2408.07852, 2024 | 1 | 2024 |
| Beyond Use-Cases: A Participatory Approach to Envisioning Data Science in Law Enforcement C Kearney, J Hron, H Kosc, M Zilka The 2024 ACM Conference on Fairness, Accountability, and Transparency, 1809-1826, 2024 | | 2024 |
Alexander G. D. G. MatthewsDeepMindVerified email at google.com
