David Janz

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 21

Top Topics

Gaussian Process

Top Venues

Publications

Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
Stochastic Gradient Descent for Gaussian Processes Done Right. ICLR (2024)
David Janz, Shuai Liu, Alex Ayoub, Csaba Szepesvári
Exploration via linearly perturbed loss minimisation. AISTATS (2024)
Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. CoRR (2023)
David Janz, Alexander E. Litvak, Csaba Szepesvári
Ensemble sampling for linear bandits: small ensembles suffice. CoRR (2023)
Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
Stochastic Gradient Descent for Gaussian Processes Done Right. CoRR (2023)
Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. NeurIPS (2023)
Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato
Sampling-based inference for large linear models, with application to linearised Laplace. ICLR (2023)
David Janz, Shuai Liu, Alex Ayoub, Csaba Szepesvári
Exploration via linearly perturbed loss minimisation. CoRR (2023)
Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. CoRR (2022)
Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato
Sampling-based inference for large linear models, with application to linearised Laplace. CoRR (2022)
Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. ICML (2022)
David Janz, David R. Burt, Javier Gonzalez
Bandit optimisation of functions in the Matérn kernel RKHS. AISTATS (2020)
David Janz, David R. Burt, Javier González
Bandit optimisation of functions in the Matérn kernel RKHS. CoRR (2020)
Alex Kendall, Jeffrey Hawke, David Janz, Przemyslaw Mazur, Daniele Reda, John-Mark Allen, Vinh-Dieu Lam, Alex Bewley, Amar Shah
Learning to Drive in a Day. ICRA (2019)
David Janz, Jiri Hron, Przemyslaw Mazur, Katja Hofmann, José Miguel Hernández-Lobato, Sebastian Tschiatschek
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning. NeurIPS (2019)
Alex Kendall, Jeffrey Hawke, David Janz, Przemyslaw Mazur, Daniele Reda, John-Mark Allen, Vinh-Dieu Lam, Alex Bewley, Amar Shah
Learning to Drive in a Day. CoRR (2018)
David Janz, Jiri Hron, José Miguel Hernández-Lobato, Katja Hofmann, Sebastian Tschiatschek
Successor Uncertainties: exploration and uncertainty in temporal difference learning. CoRR (2018)
David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato
Learning a Generative Model for Validity in Complex Discrete Structures. ICLR (Poster) (2018)
David Janz, Jos van der Westhuizen, José Miguel Hernández-Lobato
Actively Learning what makes a Discrete Sequence Valid. CoRR (2017)
David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato
Learning a Generative Model for Validity in Complex Discrete Structures. CoRR (2017)
David Janz, Brooks Paige, Tom Rainforth, Jan-Willem van de Meent, Frank D. Wood
Probabilistic structure discovery in time series data. CoRR (2016)