4 citations · 10 across the 21 of their papers we have counts for
12 papers · 1 filter
Conservative neural posterior estimation via distributionally robust training
William Laplante, Yuga Hikida, Charita Dellaporta +2
Simulation-based inference with neural posterior estimation (NPE) often yields overconfident and unreliable posteriors under limited simulation budgets. To address this, we propose…
Amortised and provably-robust simulation-based inference
Ayush Bharti, Charita Dellaporta, Yuga Hikida +1
Complex simulator-based models are now routinely used to perform inference across the sciences and engineering, but existing inference methods are often unable to account for outli…
BayesSum: Bayesian Quadrature in Discrete Spaces
Sophia Seulkee Kang, François-Xavier Briol, Toni Karvonen +1
This paper addresses the challenging computational problem of estimating intractable expectations over discrete domains. Existing approaches, including Monte Carlo and Russian Roul…
Outlier-Robust Multi-Output 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 (GPs), MOGPs ar…
Multilevel neural simulation-based inference
Yuga Hikida, Ayush Bharti, Niall Jeffrey +1
Neural simulation-based inference (SBI) is a popular set of methods for Bayesian inference when models are only available in the form of a simulator. These methods are widely used…
Stationary MMD Points
Zonghao Chen, Toni Karvonen, Heishiro Kanagawa +2
Approximation of a target probability distribution using a finite set of points is a problem of fundamental importance in numerical integration. Several authors have proposed to se…
A Dictionary of Closed-Form Kernel Mean Embeddings
François-Xavier Briol, Alexandra Gessner, Toni Karvonen +1
Kernel mean embeddings -- integrals of a kernel with respect to a probability distribution -- are essential in Bayesian quadrature, but also widely used in other computational tool…
Nested Expectations with Kernel Quadrature
Zonghao Chen, Masha Naslidnyk, François-Xavier Briol
This paper considers the challenging computational task of estimating nested expectations. Existing algorithms, such as nested Monte Carlo or multilevel Monte Carlo, are known to b…
Cost-aware simulation-based inference
Ayush Bharti, Daolang Huang, Samuel Kaski +1
Simulation-based inference (SBI) is the preferred framework for estimating parameters of intractable models in science and engineering. A significant challenge in this context is t…
Conditional Bayesian Quadrature
Zonghao Chen, Masha Naslidnyk, Arthur Gretton +1
We propose a novel approach for estimating conditional or parametric expectations in the setting where obtaining samples or evaluating integrands is costly. Through the framework o…
Bayesian Numerical Integration with Neural Networks
Katharina Ott, Michael Tiemann, Philipp Hennig +1
Bayesian probabilistic numerical methods for numerical integration offer significant advantages over their non-Bayesian counterparts: they can encode prior information about the in…
Towards Healing the Blindness of Score Matching
Mingtian Zhang, Oscar Key, Peter Hayes +3
Score-based divergences have been widely used in machine learning and statistics applications. Despite their empirical success, a blindness problem has been observed when using the…