59 citations · 173 across the 29 of their papers we have counts for
32 papers
On the Optimisation Landscape of Overparameterised Weighted MMD
Tharrshann Jayan Logarajah, Jeremias Knoblauch, Louis Sharrock
We study the optimisation landscape of the maximum mean discrepancy (MMD) over weighted atomic measures. For the one-dimensional Gaussian kernel, we show that, for any compactly su…
Predictively Oriented Gaussian Process Posteriors
Callum Lau, Jeremias Knoblauch, Louis Sharrock
Gaussian Processes (GPs) are a powerful tool for modelling and quantifying uncertainty in functional relationships. However, they require practitioners to make a number of design d…
Markov chain Monte Carlo for predictively oriented posteriors
Yann McLatchie, Louis Sharrock, David T. Frazier +1
The predictively oriented posterior offers principled uncertainty quantification, even under model misspecification. However, it does not admit an explicit density and therefore ca…
Can Bayesian Optimization Efficiently Find a Strong Single Expert in Neural Thickets?
Nigel Bastian Cendra, Abdelhamid Ezzerg, Fernando Julio Cendra +2
Gradient-free post-training has emerged as a compelling alternative to gradient-based optimization for large language models (LLMs), but existing approaches remain costly. We ask w…
Conjugate Generalized Bayesian Inference for Discrete Doubly Intractable Problems
William Laplante, Matias Altamirano, Jeremias Knoblauch +2
Doubly intractable problems occur when both the likelihood and the posterior are available only in unnormalized form, with computationally intractable normalization constants. Baye…
Robust Bayesian Optimisation with Unbounded Corruptions
Abdelhamid Ezzerg, Ilija Bogunovic, Jeremias Knoblauch
Bayesian Optimization is critically vulnerable to extreme outliers. Existing provably robust methods typically assume a bounded cumulative corruption budget, which makes them defen…
Multi-Output Robust and Conjugate Gaussian Processes
Joshua Rooijakkers, Leiv Rønneberg, François-Xavier Briol +2
Multi-output Gaussian process (MOGP) regression allows modelling dependencies among multiple correlated response variables. Similarly to standard Gaussian processes, MOGPs are sens…
TabMGP: Martingale Posterior with TabPFN
Kenyon Ng, Edwin Fong, David T. Frazier +2
Bayesian inference provides principled uncertainty quantification but is often limited by the challenges of prior and likelihood elicitation. The martingale posterior (MGP) (Fong e…
Predictively Oriented Posteriors
Yann McLatchie, Badr-Eddine Cherief-Abdellatif, David T. Frazier +1
We advocate for a new statistical principle that combines the most desirable aspects of both parameter inference and density estimation. This leads us to the predictively oriented…
Near-Optimal Approximations for Bayesian Inference in Function Space
Veit Wild, James Wu, Dino Sejdinovic +1
We propose a scalable inference algorithm for Bayes posteriors defined on a reproducing kernel Hilbert space (RKHS). Given a likelihood function and a Gaussian random element repre…
Robust and Conjugate Spatio-Temporal Gaussian Processes
William Laplante, Matias Altamirano, Andrew Duncan +2
State-space formulations allow for Gaussian process (GP) regression with linear-in-time computational cost in spatio-temporal settings, but performance typically suffers in the pre…
Prediction-Centric Uncertainty Quantification via MMD
Zheyang Shen, Jeremias Knoblauch, Sam Power +1
Deterministic mathematical models, such as those specified via differential equations, are a powerful tool to communicate scientific insight. However, such models are necessarily s…
Predictive performance of power posteriors
Yann McLatchie, Edwin Fong, David T. Frazier +1
We analyse the impact of using tempered likelihoods in the production of posterior predictions. While the choice of temperature has an impact on predictive performance in small sam…
Outlier-robust Kalman Filtering through Generalised Bayes
Gerardo Duran-Martin, Matias Altamirano, Alexander Y. Shestopaloff +5
We derive a novel, provably robust, and closed-form Bayesian update rule for online filtering in state-space models in the presence of outliers and misspecified measurement models.…
Exact Sampling of Gibbs Measures with Estimated Losses
David T. Frazier, Jeremias Knoblauch, Jack Jewson +1
A popular strategy for ameliorating some of the shortcomings of Bayesian posterior inference is to instead target a Gibbs measure based on losses that connect a parameter of intere…
Robust and Conjugate Gaussian Process Regression
Matias Altamirano, François-Xavier Briol, Jeremias Knoblauch
To enable closed form conditioning, a common assumption in Gaussian process (GP) regression is independent and identically distributed Gaussian observation noise. This strong and s…
A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods
Veit David Wild, Sahra Ghalebikesabi, Dino Sejdinovic +1
We establish the first mathematically rigorous link between Bayesian, variational Bayesian, and ensemble methods. A key step towards this it to reformulate the non-convex optimisat…
Robustifying likelihoods by optimistically re-weighting data
Miheer Dewaskar, Christopher Tosh, Jeremias Knoblauch +1
Likelihood-based inferences have been remarkably successful in wide-spanning application areas. However, even after due diligence in selecting a good model for the data at hand, th…
Robust and Scalable Bayesian Online Changepoint Detection
Matias Altamirano, François-Xavier Briol, Jeremias Knoblauch
This paper proposes an online, provably robust, and scalable Bayesian approach for changepoint detection. The resulting algorithm has key advantages over previous work: it provides…
Generalised Bayesian Inference for Discrete Intractable Likelihood
Takuo Matsubara, Jeremias Knoblauch, François-Xavier Briol +1
Discrete state spaces represent a major computational challenge to statistical inference, since the computation of normalisation constants requires summation over large or possibly…
Uncertainty-aware deep learning methods for robust diabetic retinopathy classification
Joel Jaskari, Jaakko Sahlsten, Theodoros Damoulas +5
Automatic classification of diabetic retinopathy from retinal images has been widely studied using deep neural networks with impressive results. However, there is a clinical need f…
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap
Charita Dellaporta, Jeremias Knoblauch, Theodoros Damoulas +1
Simulator-based models are models for which the likelihood is intractable but simulation of synthetic data is possible. They are often used to describe complex real-world phenomena…
Robust Generalised Bayesian Inference for Intractable Likelihoods
Takuo Matsubara, Jeremias Knoblauch, François-Xavier Briol +1
Generalised Bayesian inference updates prior beliefs using a loss function, rather than a likelihood, and can therefore be used to confer robustness against possible mis-specificat…
Generalized Posteriors in Approximate Bayesian Computation
Sebastian M Schmon, Patrick W Cannon, Jeremias Knoblauch
Complex simulators have become a ubiquitous tool in many scientific disciplines, providing high-fidelity, implicit probabilistic models of natural and social phenomena. Unfortunate…