Probabilistic structure discovery in time series data. - David Janz; Brooks Paige; Tom Rainforth; Jan-Willem van de Meent; Frank D. Wood (CoRR, 2016)

Actively Learning what makes a Discrete Sequence Valid. - David Janz; Jos van der Westhuizen; José Miguel Hernández-Lobato (CoRR, 2017)

Learning a Generative Model for Validity in Complex Discrete Structures. - David Janz; Jos van der Westhuizen; Brooks Paige; Matt J. Kusner; José Miguel Hernández-Lobato (ICLR (Poster), 2018)

Successor Uncertainties: exploration and uncertainty in temporal difference learning. - David Janz; Jiri Hron; José Miguel Hernández-Lobato; Katja Hofmann; Sebastian Tschiatschek (CoRR, 2018)

The Automatic Statistician. - Christian Steinruecken; Emma Smith; David Janz; James Robert Lloyd; Zoubin Ghahramani (Automated Machine Learning, 2019)

Learning to Drive in a Day. - Alex Kendall; Jeffrey Hawke; David Janz; Przemyslaw Mazur; Daniele Reda; John-Mark Allen; Vinh-Dieu Lam; Alex Bewley; Amar Shah 001 (ICRA, 2019)

Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning. - David Janz; Jiri Hron; Przemyslaw Mazur; Katja Hofmann; José Miguel Hernández-Lobato; Sebastian Tschiatschek (NeurIPS, 2019)

Bandit optimisation of functions in the Matérn kernel RKHS. - David Janz; David R. Burt; Javier Gonzalez (AISTATS, 2020)

Bandit optimisation of functions in the Matérn kernel RKHS. - David Janz; David R. Burt; Javier González 002 (CoRR, 2020)

Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. - Javier Antorán; David Janz; James Urquhart Allingham; Erik A. Daxberger; Riccardo Barbano; Eric T. Nalisnick; José Miguel Hernández-Lobato (ICML, 2022)

Sampling-based inference for large linear models, with application to linearised Laplace. - Javier Antorán; Shreyas Padhy; Riccardo Barbano; Eric T. Nalisnick; David Janz; José Miguel Hernández-Lobato (ICLR, 2023)

Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. - Jihao Andreas Lin; Javier Antorán; Shreyas Padhy; David Janz; José Miguel Hernández-Lobato; Alexander Terenin (NeurIPS, 2023)

Exploration via linearly perturbed loss minimisation. - David Janz; Shuai Liu; Alex Ayoub; Csaba Szepesvári (AISTATS, 2024)

Stochastic Gradient Descent for Gaussian Processes Done Right. - Jihao Andreas Lin; Shreyas Padhy; Javier Antorán; Austin Tripp; Alexander Terenin; Csaba Szepesvári; José Miguel Hernández-Lobato; David Janz (ICLR, 2024)

Ensemble sampling for linear bandits: small ensembles suffice. - David Janz; Alexander E. Litvak; Csaba Szepesvári (NeurIPS, 2024)

When and why randomised exploration works (in linear bandits). - Marc Abeille; David Janz; Ciara Pike-Burke (ALT, 2025)