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Found 14 papers, 2 papers with code
Empirical investigation of multi-source cross-validation in clinical machine learning
However, such estimates tend to be highly overoptimistic when compared to accuracy obtained from deploying models to sources not represented in the dataset, such as a new hospital.
Does Differentially Private Synthetic Data Lead to Synthetic Discoveries?
Objectives: The aim of this study is to evaluate the Mann-Whitney U test on DP-synthetic biomedical data in terms of Type I and Type II errors, in order to establish whether statistical hypothesis testing performed on privacy preserving synthetic data is likely to lead to loss of test's validity or decreased power.
Training neural networks with synthetic electrocardiograms
By allowing the randomization to increase beyond what is typically observed in the real-world data the performance is on par or superseding the performance of networks trained with real data.
A Link between Coding Theory and Cross-Validation with Applications
We show that the maximal number of classification problems with fixed class proportion, for which a learning algorithm can achieve zero LPOCV error, equals the maximal number of code words in a constant weight code (CWC), with certain technical properties.
Generalized vec trick for fast learning of pairwise kernel models
Pairwise learning corresponds to the supervised learning setting where the goal is to make predictions for pairs of objects.
A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression
Problems of that kind are often referred to as pairwise learning, dyadic prediction or network inference problems.
Tournament Leave-pair-out Cross-validation for Receiver Operating Characteristic (ROC) Analysis
This method extends LPO by creating a tournament from pair comparisons to produce a ranking for the data.
Playtime Measurement with Survival Analysis
Throughout this paper, we illustrate the application of these methods to real world game development problems on the Hipster Sheep mobile game.
Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method
In this work we analyze kernel-based methods for pairwise learning, with a particular focus on a recently-suggested two-step method.
Fast Kronecker product kernel methods via generalized vec trick
Kronecker product kernel provides the standard approach in the kernel methods literature for learning from graph data, where edges are labeled and both start and end vertices have their own feature representations.
Spectral Analysis of Symmetric and Anti-Symmetric Pairwise Kernels
We consider the problem of learning regression functions from pairwise data when there exists prior knowledge that the relation to be learned is symmetric or anti-symmetric.
A two-step learning approach for solving full and almost full cold start problems in dyadic prediction
Dyadic prediction methods operate on pairs of objects (dyads), aiming to infer labels for out-of-sample dyads.
Identification of functionally related enzymes by learning-to-rank methods
For a given query, the search operation results in a ranking of the enzymes in the database, from very similar to dissimilar enzymes, while information about the biological function of annotated database enzymes is ignored.
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data
In domains like bioinformatics, information retrieval and social network analysis, one can find learning tasks where the goal consists of inferring a ranking of objects, conditioned on a particular target object.
