Zobrazeno 1 - 10
of 5 761
pro vyhledávání: '"Miller, Jeffrey A."'
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
Autor:
Wojnowicz, Michael T., Gili, Kaitlin, Rath, Preetish, Miller, Eric, Miller, Jeffrey, Hancock, Clifford, O'Donovan, Meghan, Elkin-Frankston, Seth, Brunyé, Tad T., Hughes, Michael C.
We seek a computationally efficient model for a collection of time series arising from multiple interacting entities (a.k.a. "agents"). Recent models of spatiotemporal patterns across individuals fail to incorporate explicit system-level collective b
Externí odkaz:
http://arxiv.org/abs/2401.14973
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:
Miller, Jeffrey, Raut, Nabin K., Zulevic, Demitrius, Hart, Harold, Martinez, Luis A., Castelli, Alessandro, Chiao, Raymond, Sharping, Jay E.
The levitation of a macroscopic object within a superconducting resonator provides a unique and novel platform to study optomechanics, quantum information, and gravitational wave detection. Existing mirror-method and single-loop models for calculatin
Externí odkaz:
http://arxiv.org/abs/2306.08662
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