| Learning to Drive in a Day A Kendall, J Hawke, D Janz, P Mazur, D Reda, JM Allen, VD Lam, ... International Conference on Robotics and Automation (ICRA), 2019 | 839 | 2019 |
| The automatic statistician C Steinruecken, E Smith, D Janz, J Lloyd, Z Ghahramani Automated machine learning: Methods, systems, challenges, 161-173, 2019 | 90 | 2019 |
| Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning D Janz, J Hron, JM Hernández-Lobato, K Hofmann, S Tschiatschek Conference on Neural Information Processing Systems (NeurIPS), 2019 | 69 | 2019 |
| Bandit optimisation of functions in the Matérn kernel RKHS D Janz, DR Burt, J González International Conference on Artificial Intelligence and Statistics (AISTATS), 2020 | 50 | 2020 |
| Adapting the Linearised Laplace Model Evidence for Modern Deep Learning J Antorán, D Janz, JU Allingham, E Daxberger, R Barbano, E Nalisnick, ... International Conference on Machine Learning (ICML), 2022 | 32 | 2022 |
| Actively learning what makes a discrete sequence valid D Janz, J van der Westhuizen, JM Hernández-Lobato Principled Approaches to Deep Learning, 2017 | 29 | 2017 |
| Learning a Generative Model for Validity in Complex Discrete Structures D Janz, J van der Westhuizen, B Paige, MJ Kusner, ... International Conference on Learning Representations (ICLR), 2018 | 25 | 2018 |
| Sampling-based inference for large linear models with application to linearised Laplace J Antorán, S Padhy, R Barbano, E Nalisnick, D Janz, ... International Conference on Representation Learning (ICLR), 2023 | 21 | 2023 |
| Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent JA Lin, J Antorán, S Padhy, D Janz, JM Hernández-Lobato, A Terenin NeurIPS, 2023 | 18 | 2023 |
| Linearised Laplace Inference in Networks with Normalisation Layers and the Neural g-Prior J Antoran, JU Allingham, D Janz, E Daxberger, E Nalisnick, ... Fourth Symposium on Advances in Approximate Bayesian Inference (AABI), 2021 | 10 | 2021 |
| Probabilistic structure discovery in time series data D Janz, B Paige, T Rainforth, JW van de Meent, F Wood Artificial Intelligence for Data Science, NIPS Workshop, 2016 | 9 | 2016 |
| Stochastic Gradient Descent for Gaussian Processes Done Right JA Lin, S Padhy, J Antorán, A Tripp, A Terenin, C Szepesvári, ... ICLR, 2024 | 5 | 2024 |
| Exploration via linearly perturbed loss minimisation D Janz, S Liu, A Ayoub, C Szepesvári AISTATS, 2024 | 3 | 2024 |
| Ensemble sampling for linear bandits: small ensembles suffice D Janz, AE Litvak, C Szepesvári NeurIPS, 2024 | 2 | 2024 |
| Sequential decision making with feature-linear models D Janz University of Cambridge, 2022 | 2 | 2022 |
José Miguel Hernández-LobatoProfessor of Machine Learning, University of Cambridge確認したメール アドレス: cam.ac.uk
