David Janz
David Janz
University of Alberta
Verified email at - Homepage
Cited by
Cited by
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
The automatic statistician
C Steinruecken, E Smith, D Janz, J Lloyd, Z Ghahramani
Automated machine learning: Methods, systems, challenges, 161-173, 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
Bandit optimisation of functions in the Matérn kernel RKHS
D Janz, DR Burt, J González
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Actively learning what makes a discrete sequence valid
D Janz, J van der Westhuizen, JM Hernández-Lobato
Principled Approaches to Deep Learning, 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
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
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
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
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
JA Lin, J Antorán, S Padhy, D Janz, JM Hernández-Lobato, A Terenin
NeurIPS, 2023
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
Sequential decision making with feature-linear models
D Janz
University of Cambridge, 2022
Ensemble sampling for linear bandits: small ensembles suffice
D Janz, AE Litvak, C Szepesvári
arXiv preprint arXiv:2311.08376, 2023
Exploration via linearly perturbed loss minimisation
D Janz, S Liu, A Ayoub, C Szepesvári
arXiv preprint arXiv:2311.07565, 2023
Stochastic Gradient Descent for Gaussian Processes Done Right
JA Lin, S Padhy, J Antorán, A Tripp, A Terenin, C Szepesvári, ...
arXiv preprint arXiv:2310.20581, 2023
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