We leverage these balancing scores to perform matching for high-dimensional causal inference and call this procedure neural score matching.
no code implementations • 1 Feb 2022 • Natalia Garcia Martin, Stefano Malacrino, Marta Wojciechowska, Leticia Campo, Helen Jones, David C. Wedge, Chris Holmes, Korsuk Sirinukunwattana, Heba Sailem, Clare Verrill, Jens Rittscher
Multiplexed immunofluorescence provides an unprecedented opportunity for studying specific cell-to-cell and cell microenvironment interactions.
Increasing interest in privacy-preserving machine learning has led to new and evolved approaches for generating private synthetic data from undisclosed real data.
Empirically, we observe that Neighbourhood Shapley values identify meaningful sparse feature relevance attributions that provide insight into local model behaviour, complimenting conventional Shapley analysis.
We propose a variational autoencoder architecture to model both ignorable and nonignorable missing data using pattern-set mixtures as proposed by Little (1993).
In this paper we focus on the so-called `implicit effect' of GNIs, which is the effect of the injected noise on the dynamics of SGD.
There is significant growth and interest in the use of synthetic data as an enabler for machine learning in environments where the release of real data is restricted due to privacy or availability constraints.
Successfully training Variational Autoencoders (VAEs) with a hierarchy of discrete latent variables remains an area of active research.
We make inroads into understanding the robustness of Variational Autoencoders (VAEs) to adversarial attacks and other input perturbations.
The Covid-19 pandemic has resulted in a variety of approaches for managing infection outbreaks in international populations.
On a variety of classification tasks and modern architecture search spaces, we show that the resulting ensembles outperform deep ensembles not only in terms of accuracy but also uncertainty calibration and robustness to dataset shift.
Separating high-dimensional data like images into independent latent factors, i. e independent component analysis (ICA), remains an open research problem.
We make significant advances in addressing this issue by introducing methods for producing adversarially robust VAEs.
In Bayesian statistics, the marginal likelihood, also known as the evidence, is used to evaluate model fit as it quantifies the joint probability of the data under the prior.
no code implementations • 21 Dec 2018 • Sebastian Vollmer, Bilal A. Mateen, Gergo Bohner, Franz J. Király, Rayid Ghani, Pall Jonsson, Sarah Cumbers, Adrian Jonas, Katherine S. L. McAllister, Puja Myles, David Granger, Mark Birse, Richard Branson, Karel GM Moons, Gary S Collins, John P. A. Ioannidis, Chris Holmes, Harry Hemingway
Machine learning (ML), artificial intelligence (AI) and other modern statistical methods are providing new opportunities to operationalize previously untapped and rapidly growing sources of data for patient benefit.
We introduce 'semi-unsupervised learning', a problem regime related to transfer learning and zero-shot learning where, in the training data, some classes are sparsely labelled and others entirely unlabelled.
Boolean tensor decomposition approximates data of multi-way binary relationships as product of interpretable low-rank binary factors, following the rules Boolean algebra.
In this paper we revisit the weighted likelihood bootstrap, a method that generates samples from an approximate Bayesian posterior of a parametric model.
Finally, we have only been able so far to propose subsampling-based methods which display good performance in scenarios where the Bernstein-von Mises approximation of the target posterior distribution is excellent.