Zobrazeno 1 - 10
of 366
pro vyhledávání: '"Tin D"'
A data analyst might worry about generalization if dropping a very small fraction of data points from a study could change its substantive conclusions. Finding the worst-case data subset to drop poses a combinatorial optimization problem. To overcome
Externí odkaz:
http://arxiv.org/abs/2408.09008
If the conclusion of a data analysis is sensitive to dropping very few data points, that conclusion might hinge on the particular data at hand rather than representing a more broadly applicable truth. How could we check whether this sensitivity holds
Externí odkaz:
http://arxiv.org/abs/2408.07240
Prior knowledge and symbolic rules in machine learning are often expressed in the form of label constraints, especially in structured prediction problems. In this work, we compare two common strategies for encoding label constraints in a machine lear
Externí odkaz:
http://arxiv.org/abs/2307.03886
Test log-likelihood is commonly used to compare different models of the same data or different approximate inference algorithms for fitting the same probabilistic model. We present simple examples demonstrating how comparisons based on test log-likel
Externí odkaz:
http://arxiv.org/abs/2212.00219
Publikováno v:
California Agriculture, Vol 60, Iss 4, Pp 192-199 (2006)
Radiofrequency (RF) is an advanced telecommunication technology first invented in the early 1900s, which is in use today for wireless communication worldwide. Because of its ability to penetrate and heat various materials, RF has the potential to d
Externí odkaz:
https://doaj.org/article/1888145867d74bfdaedf49a749120587
Markov chain Monte Carlo (MCMC) methods are often used in clustering since they guarantee asymptotically exact expectations in the infinite-time limit. In finite time, though, slow mixing often leads to poor performance. Modern computing environments
Externí odkaz:
http://arxiv.org/abs/2202.11258
Autor:
Stephenson, William T., Ghosh, Soumya, Nguyen, Tin D., Yurochkin, Mikhail, Deshpande, Sameer K., Broderick, Tamara
Gaussian processes (GPs) are used to make medical and scientific decisions, including in cardiac care and monitoring of atmospheric carbon dioxide levels. Notably, the choice of GP kernel is often somewhat arbitrary. In particular, uncountably many k
Externí odkaz:
http://arxiv.org/abs/2106.06510
Computational couplings of Markov chains provide a practical route to unbiased Monte Carlo estimation that can utilize parallel computation. However, these approaches depend crucially on chains meeting after a small number of transitions. For models
Externí odkaz:
http://arxiv.org/abs/2104.04514
Publikováno v:
In Public Health January 2024 226:255-260
Completely random measures (CRMs) and their normalizations (NCRMs) offer flexible models in Bayesian nonparametrics. But their infinite dimensionality presents challenges for inference. Two popular finite approximations are truncated finite approxima
Externí odkaz:
http://arxiv.org/abs/2009.10780