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pro vyhledávání: '"Miller, Jeffrey W"'
Dirichlet distributions are commonly used for modeling vectors in a probability simplex. When used as a prior or a proposal distribution, it is natural to set the mean of a Dirichlet to be equal to the location where one wants the distribution to be
Externí odkaz:
http://arxiv.org/abs/2410.13050
Feature selection can greatly improve performance and interpretability in machine learning problems. However, existing nonparametric feature selection methods either lack theoretical error control or fail to accurately control errors in practice. Man
Externí odkaz:
http://arxiv.org/abs/2410.02208
Autor:
Zito, Alessandro, Miller, Jeffrey W.
Non-negative matrix factorization (NMF) is widely used in many applications for dimensionality reduction. Inferring an appropriate number of factors for NMF is a challenging problem, and several approaches based on information criteria or sparsity-in
Externí odkaz:
http://arxiv.org/abs/2404.10974
Autor:
Melikechi, Omar, Miller, Jeffrey W.
Stability selection is a popular method for improving feature selection algorithms. One of its key attributes is that it provides theoretical upper bounds on the expected number of false positives, E(FP), enabling control of false positives in practi
Externí odkaz:
http://arxiv.org/abs/2403.15877
Under model misspecification, it is known that Bayesian posteriors often do not properly quantify uncertainty about true or pseudo-true parameters. Even more fundamentally, misspecification leads to a lack of reproducibility in the sense that the sam
Externí odkaz:
http://arxiv.org/abs/2311.02019
Principal variables analysis (PVA) is a technique for selecting a subset of variables that capture as much of the information in a dataset as possible. Existing approaches for PVA are based on the Pearson correlation matrix, which is not well-suited
Externí odkaz:
http://arxiv.org/abs/2309.13162
Autor:
Spencer, Neil A., Miller, Jeffrey W.
This article establishes novel strong uniform laws of large numbers for randomly weighted sums such as bootstrap means. By leveraging recent advances, these results extend previous work in their general applicability to a wide range of weighting proc
Externí odkaz:
http://arxiv.org/abs/2209.04083
Autor:
Miller, Jeffrey W.
This article establishes general conditions for posterior consistency of Bayesian finite mixture models with a prior on the number of components. That is, we provide sufficient conditions under which the posterior concentrates on neighborhoods of the
Externí odkaz:
http://arxiv.org/abs/2205.03384
Autor:
Weinstein, Eli N., Miller, Jeffrey W.
Insights into complex, high-dimensional data can be obtained by discovering features of the data that match or do not match a model of interest. To formalize this task, we introduce the "data selection" problem: finding a lower-dimensional statistic
Externí odkaz:
http://arxiv.org/abs/2109.02712
Autor:
Zemplenyi, Michele, Miller, Jeffrey W.
Inferring the causal structure of a system typically requires interventional data, rather than just observational data. Since interventional experiments can be costly, it is preferable to select interventions that yield the maximum amount of informat
Externí odkaz:
http://arxiv.org/abs/2103.15229