Zobrazeno 1 - 10
of 338
pro vyhledávání: '"CHAIBUB, A. A."'
Autor:
Neto, Elias Chaibub
Non-parametric two-sample tests based on energy distance or maximum mean discrepancy are widely used statistical tests for comparing multivariate data from two populations. While these tests enjoy desirable statistical properties, their test statisti
Externí odkaz:
http://arxiv.org/abs/2406.06488
Autor:
Neto, Elias Chaibub
Traditional perturbative statistical disclosure control (SDC) approaches such as microaggregation, noise addition, rank swapping, etc, perturb the data in an ``ad-hoc" way in the sense that while they manage to preserve some particular aspects of the
Externí odkaz:
http://arxiv.org/abs/2311.06422
Autor:
Neto, Elias Chaibub
In the field of statistical disclosure control, the tradeoff between data confidentiality and data utility is measured by comparing disclosure risk and information loss metrics. Distance based metrics such as the mean absolute error (MAE), mean squar
Externí odkaz:
http://arxiv.org/abs/2305.07846
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-17 (2024)
Abstract Background The two-way partial AUC has been recently proposed as a way to directly quantify partial area under the ROC curve with simultaneous restrictions on the sensitivity and specificity ranges of diagnostic tests or classifiers. The met
Externí odkaz:
https://doaj.org/article/7607bf5da99d4d4fb9b41cc133e6c970
Autor:
Ramzi Halabi, Rahavi Selvarajan, Zixiong Lin, Calvin Herd, Xueying Li, Jana Kabrit, Meghasyam Tummalacherla, Elias Chaibub Neto, Abhishek Pratap
Publikováno v:
Sensors, Vol 24, Iss 19, p 6246 (2024)
Healthcare researchers are increasingly utilizing smartphone sensor data as a scalable and cost-effective approach to studying individualized health-related behaviors in real-world settings. However, to develop reliable and robust digital behavioral
Externí odkaz:
https://doaj.org/article/8c4df3a9bf554dab8daffda47c0d9a98
Autor:
Neto, Elias Chaibub
Linear residualization is a common practice for confounding adjustment in machine learning (ML) applications. Recently, causality-aware predictive modeling has been proposed as an alternative causality-inspired approach for adjusting for confounders.
Externí odkaz:
http://arxiv.org/abs/2011.04605
In health related machine learning applications, the training data often corresponds to a non-representative sample from the target populations where the learners will be deployed. In anticausal prediction tasks, selection biases often make the assoc
Externí odkaz:
http://arxiv.org/abs/2011.04128
Autor:
Neto, Elias Chaibub
Causal modeling has been recognized as a potential solution to many challenging problems in machine learning (ML). Here, we describe how a recently proposed counterfactual approach developed to deconfound linear structural causal models can still be
Externí odkaz:
http://arxiv.org/abs/2004.09466
Autor:
Neto, Elias Chaibub
We propose a counterfactual approach to train ``causality-aware" predictive models that are able to leverage causal information in static anticausal machine learning tasks (i.e., prediction tasks where the outcome influences the features). In applica
Externí odkaz:
http://arxiv.org/abs/2001.03998
Machine learning practice is often impacted by confounders. Confounding can be particularly severe in remote digital health studies where the participants self-select to enter the study. While many different confounding adjustment approaches have bee
Externí odkaz:
http://arxiv.org/abs/1911.05139