Janz, David

10 publications

NeurIPS 2025 Eluder Dimension: Localise It! Alireza Bakhtiari, Alex Ayoub, Samuel McLaughlin Robertson, David Janz, Csaba Szepesvari

ALT 2025 When and Why Randomised Exploration Works (in Linear Bandits) Marc Abeille, David Janz, Ciara Pike-Burke

NeurIPS 2024 Ensemble Sampling for Linear Bandits: Small Ensembles Suffice David Janz, Alexander E. Litvak, Csaba Szepesvári

AISTATS 2024 Exploration via Linearly Perturbed Loss Minimisation David Janz, Shuai Liu, Alex Ayoub, Csaba Szepesvári

ICLR 2024 Stochastic Gradient Descent for Gaussian Processes Done Right Jihao Andreas Lin, Shreyas Padhy, Javier Antoran, Austin Tripp, Alexander Terenin, Csaba Szepesvari, José Miguel Hernández-Lobato, David Janz

NeurIPS 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

ICLR 2023 Sampling-Based Inference for Large Linear Models, with Application to Linearised Laplace Javier Antoran, Shreyas Padhy, Riccardo Barbano, Eric Nalisnick, David Janz, José Miguel Hernández-Lobato

ICML 2022 Adapting the Linearised Laplace Model Evidence for Modern Deep Learning Javier Antoran, David Janz, James U Allingham, Erik Daxberger, Riccardo Rb Barbano, Eric Nalisnick, Jose Miguel Hernandez-Lobato

AISTATS 2020 Bandit Optimisation of Functions in the Matérn Kernel RKHS David Janz, David Burt, Javier Gonzalez

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