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2020 – today
- 2025
[c11]Marc Abeille, David Janz, Ciara Pike-Burke:
When and why randomised exploration works (in linear bandits). ALT 2025: 4-22- [i13]Marc Abeille, David Janz, Ciara Pike-Burke:
When and why randomised exploration works (in linear bandits). CoRR abs/2502.08870 (2025) - 2024
- [c10]David Janz, Shuai Liu, Alex Ayoub, Csaba Szepesvári:
Exploration via linearly perturbed loss minimisation. AISTATS 2024: 721-729 - [c9]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 - [c8]David Janz, Alexander E. Litvak, Csaba Szepesvári:
Ensemble sampling for linear bandits: small ensembles suffice. NeurIPS 2024 - 2023
- [c7]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 - [c6]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 - [i12]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 abs/2306.11589 (2023) - [i11]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 abs/2310.20581 (2023) - [i10]David Janz, Shuai Liu, Alex Ayoub, Csaba Szepesvári:
Exploration via linearly perturbed loss minimisation. CoRR abs/2311.07565 (2023) - [i9]David Janz, Alexander E. Litvak, Csaba Szepesvári:
Ensemble sampling for linear bandits: small ensembles suffice. CoRR abs/2311.08376 (2023) - 2022
- [c5]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: 796-821 - [i8]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 abs/2206.08900 (2022) - [i7]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 abs/2210.04994 (2022) - 2020
- [c4]David Janz, David R. Burt, Javier Gonzalez:
Bandit optimisation of functions in the Matérn kernel RKHS. AISTATS 2020: 2486-2495 - [i6]David Janz, David R. Burt, Javier González:
Bandit optimisation of functions in the Matérn kernel RKHS. CoRR abs/2001.10396 (2020)
2010 – 2019
- 2019
- [c3]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: 8248-8254 - [c2]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: 4509-4518 - [p1]Christian Steinruecken, Emma Smith, David Janz, James Robert Lloyd, Zoubin Ghahramani:
The Automatic Statistician. Automated Machine Learning 2019: 161-173 - 2018
- [c1]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 - [i5]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 abs/1807.00412 (2018) - [i4]David Janz, Jiri Hron, José Miguel Hernández-Lobato, Katja Hofmann, Sebastian Tschiatschek:
Successor Uncertainties: exploration and uncertainty in temporal difference learning. CoRR abs/1810.06530 (2018) - 2017
- [i3]David Janz, Jos van der Westhuizen, José Miguel Hernández-Lobato:
Actively Learning what makes a Discrete Sequence Valid. CoRR abs/1708.04465 (2017) - [i2]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 abs/1712.01664 (2017) - 2016
- [i1]David Janz, Brooks Paige, Tom Rainforth, Jan-Willem van de Meent, Frank D. Wood:
Probabilistic structure discovery in time series data. CoRR abs/1611.06863 (2016)
Coauthor Index
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