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Statistical Relational Learning [PDF]
, 2011 Statistical relational learning a.k.a. probabilistic inductive logic programming deals with machine learning and data mining in relational domains where observations may be missing, partially observed, or noisy.
De Raedt, Luc, Kersting, Kristian
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Statistical Relational Learning [PDF]
, 2013 Relational learning refers to learning from data that have a complex structure. This structure may be either internal (a data instance may itself have a complex structure) or external (relationships between this instance and other data elements). Statistical relational learning refers to the use of statistical learning methods in a relational learning ...
Blockeel, Hendrik, Hendrik Blockeel
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On Statistical Efficiency in Learning [PDF]
IEEE Transactions on Information Theory, 2021 A central issue of many statistical learning problems is to select an appropriate model from a set of candidate models. Large models tend to inflate the variance (or overfitting), while small models tend to cause biases (or underfitting) for a given fixed dataset.
Jie Ding 0002 +3 more
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Manifold learning in statistical tasks
Учёные записки Казанского университета: Серия Физико-математические науки, 2018 Many tasks of data analysis deal with high-dimensional data, and curse of dimensionality is an obstacle to the use of many methods for their solving.
A.V. Bernstein
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Statistical learning and memory [PDF]
Cognition, 2020 Learners often need to identify and remember recurring units in continuous sequences, but the underlying mechanisms are debated. A particularly prominent candidate mechanism relies on distributional statistics such as Transitional Probabilities (TPs).
Ansgar D. Endress +2 more
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Orthogonal statistical learning
The Annals of Statistics, 2023 Reorganized, added experiments and additional ...
Dylan J. Foster, Vasilis Syrgkanis
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Variable Selection and Allocation in Joint Models via Gradient Boosting Techniques
Mathematics, 2023 Modeling longitudinal data (e.g., biomarkers) and the risk for events separately leads to a loss of information and bias, even though the underlying processes are related to each other.
Colin Griesbach +2 more
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scDLC: a deep learning framework to classify large sample single-cell RNA-seq data
BMC Genomics, 2022 Background Using single-cell RNA sequencing (scRNA-seq) data to diagnose disease is an effective technique in medical research. Several statistical methods have been developed for the classification of RNA sequencing (RNA-seq) data, including, for ...
Yan Zhou +5 more
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BMC Genomics, 2021 Background Identifying differentially expressed genes between the same or different species is an urgent demand for biological and medical research. For RNA-seq data, systematic technical effects and different sequencing depths are usually encountered ...
Yan Zhou +4 more
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Statistical Learning in Wasserstein Space [PDF]
IEEE Control Systems Letters, 2021 We seek a generalization of regression and principle component analysis (PCA) in a metric space where data points are distributions metrized by the Wasserstein metric. We recast these analyses as multimarginal optimal transport problems. The particular formulation allows efficient computation, ensures existence of optimal solutions, and admits a ...
Amir-Hossein Karimi +2 more
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