David Janz
9 papers · 2019–2025 · 5 conferences · across top CS/AI conferences
Achievements
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🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (5) 🏃 Academic Marathon (6) 🐝 Cross-Pollinator (5)
🌈
Renaissance Researcher
(5)
🗺️
Taxonomy Completionist
(19)
🐣
Hot Topic Early Bird
Conferences
NIPS (3)
AISTATS (2)
ICLR (2)
ALT (1)
ICML (1)
Top co-authors
Keywords
posterior sampling
(2)
bayesian optimization
(2)
uncertainty quantification
(2)
gaussian process
(2)
model uncertainty
(1)
reproducing kernel hilbert space
(1)
thompson sampling
(1)
model evidence
(1)
regret bound
(1)
online algorithm
(1)
bandit optimization
(1)
sublinear regret
(1)
linear bandit
(1)
stochastic bandit
(1)
generalized linear bandit
(1)
normalization layer
(1)
bayesian deep learning
(1)
reward perturbation
(1)
inducing point
(1)
linear perturbation
(1)
Papers
When and why randomised exploration works (in linear bandits)
ALT 2025
Ensemble sampling for linear bandits: small ensembles suffice
NIPS 2024
Stochastic Gradient Descent for Gaussian Processes Done Right
ICLR 2024
Exploration via linearly perturbed loss minimisation
AISTATS 2024
Sampling-based inference for large linear models, with application to linearised Laplace
ICLR 2023
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
NIPS 2023
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
ICML 2022
Bandit optimisation of functions in the Matérn kernel RKHS
AISTATS 2020
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning
NIPS 2019