Search · four archives
Search · four archives
20 papers · ranked by Valyu relevance
Annabelle Redelmeier, Martin Jullum, Kjersti Aas
It is becoming increasingly important to explain complex, black-box machine learning models. Although there is an expanding literature on this topic, Shapley values stand out as a sound method to explain predictions from any type of machine learning model. The original development of Shapley values for prediction…
Ashwini Venkatasubramaniam, Julian Wolfson, Nathan Mitchell, Timothy Barnes + 2 more
'Timothy Barnes' 'Meghan JaKa' 'Simone French'] Background In many studies, it is of interest to identify population subgroups that are relatively homogeneous with respect to an outcome. The nature of these subgroups can provide insight into effect mechanisms and suggest targets for tailored interventions. However…
Ke Shen, Mayank Kejriwal, Sarah Elizabeth Brewer
COVID-19 vaccine hesitancy has become a major issue in the U.S. as vaccine supply has outstripped demand and vaccination rates slow down. At least one recent global survey has sought to study the covariates of vaccine acceptance, but an inferential model that makes simultaneous use of several socio-demographic…
Stefano Nembrini
The journal published a review of the literature on recursive partition in epidemiological research comparing two decision tree methods: classification and regression trees (CARTs) and conditional inference trees (CITs). There are two sources of potential confusion in the paper for readers: one lies in the definition…
Scott Davies, Andrew Moore
Joint distributions over many variables are frequently modeled by decomposing them into products of simpler, lower-dimensional conditional distributions, such as in sparsely connected Bayesian networks. However, automatically learning such models can be very computationally expensive when there are many datapoints and…
Jinxiong Zhang
Based on decision trees, many fields have arguably made tremendous progress in recent years. In simple words, decision trees use the strategy of "divide-and-conquer" to divide the complex problem on the dependency between input features and labels into smaller ones. While decision trees have a long history, recent…
Julia Braun, Sebastian D. Sahli, Donat R. Spahn, Daniel Röder + 6 more
Background: Despite increasing use and understanding of the process, veno-arterial extracorporeal membrane oxygenation (VA-ECMO) therapy is still associated with considerable mortality. Personalized and quick survival predictions using machine learning methods can assist in clinical decision making before ECMO…
Zijing Yang, Jonathan Klawitter, Remco Bouckaert, Alexei J. Drummond
Bayesian phylogenetic inference uses Markov chain Monte Carlo sampling to estimate the posterior distribution of phylogenetic trees. However, the complex geometry of treespace makes these distributions difficult to characterise. Traditional approaches often summarise posterior samples into a single point estimate…
Emilio Salinas, Terrence R. Stanford, Nicholas V Swindale
Intuitively, combining multiple sources of evidence should lead to more accurate decisions than considering single sources of evidence individually. In practice, however, the proper computation may be difficult, or may require additional data that are inaccessible. Here, based on the concept of conditional…
Emilio Salinas, Terrence R Stanford
Intuitively, combining multiple sources of evidence should lead to more accurate decisions than considering single sources of evidence individually. In practice, however, the proper computation may be difficult, or may require additional data that are inaccessible. Here, based on the concept of conditional…
Rita Sharma, David Poole
In this paper we examine the problem of inference in Bayesian Networks with discrete random variables that have very large or even unbounded domains. For example, in a domain where we are trying to identify a person, we may have variables that have as domains, the set of all names, the set of all postal codes, or the…
Tim Genewein, Tom McGrath, Grégoire Delétang, Vladimir Mikulik + 3 more
'Miljan Martic' 'Shane Legg' 'Pedro A. Ortega'] Probability trees are one of the simplest models of causal generative processes. They possess clean semantics and—unlike causal Bayesian networks—they can represent context-specific causal dependencies, which are necessary for e.g. causal induction. Yet, they have…
Davide Bacciu
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Madeleine E. St. Ville, Christopher S. McMahan, Joe D. Bible, Joshua M. Tebbs + 1 more
'Joshua M. Tebbs' 'Christopher R. Bilder'] Title: ABSTRACT When screening for low-prevalence diseases, pooling specimens (e.g., blood, urine, swabs, etc.) through group testing has the potential to substantially reduce costs when compared to testing specimens individually. A common goal in group testing applications is…
Julie Drevet, Jan Drugowitsch, Valentin Wyart
Statistical inference is the optimal process for forming and maintaining accurate beliefs about uncertain environments. However, human inference comes with costs due to its associated biases and limited precision. Indeed, biased or imprecise inference can trigger variable beliefs and unwarranted changes in behavior.…
Gunther Schauberger, Stefanie J. Klug, Moritz Berger
Background Conditional logistic regression trees have been proposed as a flexible alternative to the standard method of conditional logistic regression for the analysis of matched case-control studies. While they allow to avoid the strict assumption of linearity and automatically incorporate interactions, conditional…
Edo Dotan, Asaf Schers, Elya Wygoda, Tal Pupko + 1 more
Accurate inference of phylogenetic trees is fundamental to evolutionary biology, yet existing methods rely on complex pipelines involving multiple sequence alignment, explicit evolutionary models, and computationally intensive tree search procedures. Here, we present BetaInfer, a generative framework that reformulates…
Venelin Mitov, Tanja Stadler
Phylogenetic comparative methods have been used to model trait evolution, to test selection versus neutral hypotheses, to estimate optimal trait-values, and to quantify the rate of adaptation towards these optima. Several authors have proposed algorithms calculating the likelihood for trait evolution models, such as…
Guy Baele, Luiz M. Carvalho, Marius Brusselmans, Gytis Dudas + 5 more
In Bayesian phylogenetic and phylodynamic studies it is common to summarise the posterior distribution of trees with a time-calibrated consensus phylogeny. While the maximum clade credibility (MCC) tree is often used for this purpose, we here show that a novel consensus tree method – the highest independent posterior…
Vladimir Kondratyev, Marian Dryzhakov, Timur Gimadiev, Dmitriy Slutskiy
In this work, we provide further development of the junction tree variational autoencoder (JT VAE) architecture in terms of implementation and application of the internal feature space of the model. Pretraining of JT VAE on a large dataset and further optimization with a regression model led to a latent space that can…