David Janz
10 papers · 2019–2026 · 6 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
Jump to papers ↓+2 more ↓ Show less ↑
🗺️ Taxonomy Completionist (21) 🐣 Hot Topic Early Bird 🏃 Academic Marathon · Sprint 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Silver
🔥
Unstoppable · Bronze
💎
Century Club · Bronze
Top co-authors
Research topics
Machine Learning
(10)
Optimization & Theory · Machine Learning
(9)
Artificial Intelligence
(5)
Mathematics & Optimization
(4)
Learning Types
(4)
Optimization
(4)
Bayesian & Probabilistic · Artificial Intelligence
(3)
Reinforcement Learning
(2)
Core AI
(2)
Core Methods
(2)
Methods
(2)
Deep Learning
(1)
Bayesian & Probabilistic · Machine Learning
(1)
Mathematics
(1)
Optimization & Theory · Deep Learning
(1)
Keywords
linear bandit
(3)
thompson sampling
(3)
uncertainty quantification
(3)
regret bound
(3)
gaussian process
(3)
stochastic gradient descent
(2)
bayesian optimization
(2)
posterior sampling
(2)
model evidence
(1)
strongly convex
(1)
reproducing kernel hilbert space
(1)
bayesian inference
(1)
laplace approximation
(1)
online algorithm
(1)
model uncertainty
(1)
temporal difference learning
(1)
bandit optimization
(1)
sublinear regret
(1)
reinforcement learning
(1)
stochastic optimization
(1)
Papers
Sharp analysis of linear ensemble sampling
COLT 2026
When and why randomised exploration works (in linear bandits)
ALT 2025
Ensemble sampling for linear bandits: small ensembles suffice
NeurIPS 2024
Exploration via linearly perturbed loss minimisation
AISTATS 2024
Stochastic Gradient Descent for Gaussian Processes Done Right
ICLR 2024
Sampling-based inference for large linear models, with application to linearised Laplace
ICLR 2023
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
NeurIPS 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
NeurIPS 2019