David Janz

According to our database, David Janz authored at least 15 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Exploration via linearly perturbed loss minimisation.

David Janz, Shuai Liu, Alex Ayoub, Csaba Szepesvári

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Ensemble sampling for linear bandits: small ensembles suffice.

David Janz, Alexander E. Litvak, Csaba Szepesvári

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

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

Proceedings of the International Conference on Machine Learning, 2022

2020

Bandit optimisation of functions in the Matérn kernel RKHS.

David Janz, David R. Burt, Javier González

CoRR, 2020

Bandit optimisation of functions in the Matérn kernel RKHS.

David Janz, David R. Burt, Javier Gonzalez

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning.

David Janz, Jiri Hron, Przemyslaw Mazur, Katja Hofmann, José Miguel Hernández-Lobato, Sebastian Tschiatschek

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 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

Proceedings of the International Conference on Robotics and Automation, 2019

The Automatic Statistician.

Christian Steinruecken, Emma Smith, David Janz, James Robert Lloyd, Zoubin Ghahramani

Proceedings of the Automated Machine Learning - Methods, Systems, Challenges, 2019

2018

Successor Uncertainties: exploration and uncertainty in temporal difference learning.

David Janz, Jiri Hron, José Miguel Hernández-Lobato, Katja Hofmann, Sebastian Tschiatschek

CoRR, 2018

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

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Actively Learning what makes a Discrete Sequence Valid.

David Janz, Jos van der Westhuizen, José Miguel Hernández-Lobato

CoRR, 2017

2016

Probabilistic structure discovery in time series data.

David Janz, Brooks Paige, Tom Rainforth, Jan-Willem van de Meent, Frank D. Wood

CoRR, 2016