Zobrazeno 1 - 10
of 111
pro vyhledávání: '"Reeve, Henry"'
Conditional quantile treatment effect (CQTE) can provide insight into the effect of a treatment beyond the conditional average treatment effect (CATE). This ability to provide information over multiple quantiles of the response makes CQTE especially
Externí odkaz:
http://arxiv.org/abs/2410.12454
Autor:
Reeve, Henry W J
We consider a semi-supervised classification problem with non-stationary label-shift in which we observe a labelled data set followed by a sequence of unlabelled covariate vectors in which the marginal probabilities of the class labels may change ove
Externí odkaz:
http://arxiv.org/abs/2405.18091
Autor:
Reeve, Henry W J
The Dvoretzky--Kiefer--Wolfowitz--Massart inequality gives a sub-Gaussian tail bound on the supremum norm distance between the empirical distribution function of a random sample and its population counterpart. We provide a short proof of a result tha
Externí odkaz:
http://arxiv.org/abs/2403.16651
Given a sample of covariate-response pairs, we consider the subgroup selection problem of identifying a subset of the covariate domain where the regression function exceeds a pre-determined threshold. We introduce a computationally-feasible approach
Externí odkaz:
http://arxiv.org/abs/2305.04852
Density Ratio Estimation (DRE) is an important machine learning technique with many downstream applications. We consider the challenge of DRE with missing not at random (MNAR) data. In this setting, we show that using standard DRE methods leads to bi
Externí odkaz:
http://arxiv.org/abs/2302.10655
Publikováno v:
Journal of Machine Learning Research, 24(359), 2023
We present a theory of ensemble diversity, explaining the nature of diversity for a wide range of supervised learning scenarios. This challenge has been referred to as the holy grail of ensemble learning, an open research issue for over 30 years. Our
Externí odkaz:
http://arxiv.org/abs/2301.03962
We consider a large number of agents collaborating on a multi-armed bandit problem with a large number of arms. The goal is to minimise the regret of each agent in a communication-constrained setting. We present a decentralised algorithm which builds
Externí odkaz:
http://arxiv.org/abs/2109.09427
In clinical trials and other applications, we often see regions of the feature space that appear to exhibit interesting behaviour, but it is unclear whether these observed phenomena are reflected at the population level. Focusing on a regression sett
Externí odkaz:
http://arxiv.org/abs/2109.01077
In transfer learning, we wish to make inference about a target population when we have access to data both from the distribution itself, and from a different but related source distribution. We introduce a flexible framework for transfer learning in
Externí odkaz:
http://arxiv.org/abs/2106.04455
Autor:
Reeve, Henry W. J., Kaban, Ata
We present a framework for the theoretical analysis of ensembles of low-complexity empirical risk minimisers trained on independent random compressions of high-dimensional data. First we introduce a general distribution-dependent upper-bound on the e
Externí odkaz:
http://arxiv.org/abs/2106.01092