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of 29
pro vyhledávání: '"Yao, Weichi"'
Any representation of data involves arbitrary investigator choices. Because those choices are external to the data-generating process, each choice leads to an exact symmetry, corresponding to the group of transformations that takes one possible repre
Externí odkaz:
http://arxiv.org/abs/2301.13724
Publikováno v:
Journal of Machine Learning Research 24 (2023) 1--32
Units equivariance (or units covariance) is the exact symmetry that follows from the requirement that relationships among measured quantities of physics relevance must obey self-consistent dimensional scalings. Here, we express this symmetry in terms
Externí odkaz:
http://arxiv.org/abs/2204.00887
Physical systems obey strict symmetry principles. We expect that machine learning methods that intrinsically respect these symmetries should have higher prediction accuracy and better generalization in prediction of physical dynamics. In this work we
Externí odkaz:
http://arxiv.org/abs/2110.03761
Publikováno v:
Advances in Neural Information Processing Systems, 34, 28848-28863. 2021
There has been enormous progress in the last few years in designing neural networks that respect the fundamental symmetries and coordinate freedoms of physical law. Some of these frameworks make use of irreducible representations, some make use of hi
Externí odkaz:
http://arxiv.org/abs/2106.06610
Time-varying covariates are often available in survival studies and estimation of the hazard function needs to be updated as new information becomes available. In this paper, we investigate several different easy-to-implement ways that random forests
Externí odkaz:
http://arxiv.org/abs/2103.01355
Survival data with time-varying covariates are common in practice. If relevant, they can improve on the estimation of survival function. However, the traditional survival forests - conditional inference forest, relative risk forest and random surviva
Externí odkaz:
http://arxiv.org/abs/2006.00567
This note explores the applicability of unsupervised machine learning techniques towards hard optimization problems on random inputs. In particular we consider Graph Neural Networks (GNNs) -- a class of neural networks designed to learn functions on
Externí odkaz:
http://arxiv.org/abs/1908.05767
Interval-censored data analysis is important in biomedical statistics for any type of time-to-event response where the time of response is not known exactly, but rather only known to occur between two assessment times. Many clinical trials and longit
Externí odkaz:
http://arxiv.org/abs/1901.04599
Autor:
Moradian, Hoora1 (AUTHOR), Yao, Weichi2 (AUTHOR), Larocque, Denis1 (AUTHOR) denis.larocque@hec.ca, Simonoff, Jeffrey S.2 (AUTHOR), Frydman, Halina2 (AUTHOR)
Publikováno v:
Canadian Journal of Statistics. Jun2022, Vol. 50 Issue 2, p533-548. 16p.
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