David Janz - RankMe

28 papers A* 7A 2B 1Journal 16Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Eluder dimension: localise it! CoRR Alireza Bakhtiari, Alex Ayoub, Samuel Robertson, David Janz, Csaba Szepesvári
2026	J	`jnl`	Sharp analysis of linear ensemble sampling. CoRR Arya Akhavan, David Janz, Csaba Szepesvári
2025	J	`jnl`	High-Probability Analysis of Online and Federated Zero-Order Optimisation. CoRR Arya Akhavan, David Janz, El-Mahdi El-Mhamdi
2025	B	`conf`	When and why randomised exploration works (in linear bandits). ALT Marc Abeille, David Janz, Ciara Pike-Burke
2025	J	`jnl`	When and why randomised exploration works (in linear bandits). CoRR Marc Abeille, David Janz, Ciara Pike-Burke
2024	A*	`conf`	Ensemble sampling for linear bandits: small ensembles suffice. NeurIPS David Janz, Alexander E. Litvak, Csaba Szepesvári
2024	A	`conf`	Exploration via linearly perturbed loss minimisation. AISTATS David Janz, Shuai Liu, Alex Ayoub, Csaba Szepesvári
2024	A*	`conf`	Stochastic Gradient Descent for Gaussian Processes Done Right. ICLR Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
2023	J	`jnl`	Ensemble sampling for linear bandits: small ensembles suffice. CoRR David Janz, Alexander E. Litvak, Csaba Szepesvári
2023	J	`jnl`	Exploration via linearly perturbed loss minimisation. CoRR David Janz, Shuai Liu, Alex Ayoub, Csaba Szepesvári
2023	A*	`conf`	Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. NeurIPS Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin
2023	J	`jnl`	Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. CoRR Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin
2023	A*	`conf`	Sampling-based inference for large linear models, with application to linearised Laplace. ICLR Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato
2023	J	`jnl`	Stochastic Gradient Descent for Gaussian Processes Done Right. CoRR Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
2022	A*	`conf`	Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. ICML Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato
2022	J	`jnl`	Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. CoRR Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato
2022	J	`jnl`	Sampling-based inference for large linear models, with application to linearised Laplace. CoRR Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato
2020	A	`conf`	Bandit optimisation of functions in the Matérn kernel RKHS. AISTATS David Janz, David R. Burt, Javier González
2020	J	`jnl`	Bandit optimisation of functions in the Matérn kernel RKHS. CoRR David Janz, David R. Burt, Javier González
2019	A*	`conf`	Learning to Drive in a Day. ICRA Alex Kendall, Jeffrey Hawke, David Janz, Przemyslaw Mazur, Daniele Reda, John-Mark Allen, Vinh-Dieu Lam, Alex Bewley, Amar Shah
2019	A*	`conf`	Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning. NeurIPS David Janz, Jiri Hron, Przemyslaw Mazur, Katja Hofmann, José Miguel Hernández-Lobato, Sebastian Tschiatschek
2019	—	`ch.`	The Automatic Statistician. Automated Machine Learning Christian Steinruecken, Emma Smith, David Janz, James Robert Lloyd, Zoubin Ghahramani
2018	—	`conf`	Learning a Generative Model for Validity in Complex Discrete Structures. ICLR (Poster) David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato
2018	J	`jnl`	Learning to Drive in a Day. CoRR Alex Kendall, Jeffrey Hawke, David Janz, Przemyslaw Mazur, Daniele Reda, John-Mark Allen, Vinh-Dieu Lam, Alex Bewley, Amar Shah
2018	J	`jnl`	Successor Uncertainties: exploration and uncertainty in temporal difference learning. CoRR David Janz, Jiri Hron, José Miguel Hernández-Lobato, Katja Hofmann, Sebastian Tschiatschek
2017	J	`jnl`	Actively Learning what makes a Discrete Sequence Valid. CoRR David Janz, Jos van der Westhuizen, José Miguel Hernández-Lobato
2017	J	`jnl`	Learning a Generative Model for Validity in Complex Discrete Structures. CoRR David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato
2016	J	`jnl`	Probabilistic structure discovery in time series data. CoRR David Janz, Brooks Paige, Tom Rainforth, Jan-Willem van de Meent, Frank D. Wood