Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Weylandt, Michael"'
Autor:
Wentland, Christopher R., Weylandt, Michael, Swiler, Laura P., Ehrmann, Thomas S., Bull, Diana
Attribution of climate impacts to a source forcing is critical to understanding, communicating, and addressing the effects of human influence on the climate. While standard attribution methods, such as optimal fingerprinting, have been successfully a
Externí odkaz:
http://arxiv.org/abs/2409.01396
We show that certain Graph Laplacian linear sets of equations exhibit optimal accuracy, guaranteeing that the relative error is no larger than the norm of the relative residual and that optimality occurs for carefully chosen right-hand sides. Such se
Externí odkaz:
http://arxiv.org/abs/2405.07877
Algorithmic fairness has emerged as an important consideration when using machine learning to make high-stakes societal decisions. Yet, improved fairness often comes at the expense of model accuracy. While aspects of the fairness-accuracy tradeoff ha
Externí odkaz:
http://arxiv.org/abs/2206.00074
Autor:
Weylandt, Michael, Michailidis, George
Network data are commonly collected in a variety of applications, representing either directly measured or statistically inferred connections between features of interest. In an increasing number of domains, these networks are collected over time, su
Externí odkaz:
http://arxiv.org/abs/2202.04719
Network models provide a powerful and flexible framework for analyzing a wide range of structured data sources. In many situations of interest, however, multiple networks can be constructed to capture different aspects of an underlying phenomenon or
Externí odkaz:
http://arxiv.org/abs/2111.01273
Publikováno v:
SSP 2021: Proceedings of the 2021 IEEE Statistical Signal Processing Workshop 2021, pp.561-565. 2021
Graph signal processing (GSP) provides a powerful framework for analyzing signals arising in a variety of domains. In many applications of GSP, multiple network structures are available, each of which captures different aspects of the same underlying
Externí odkaz:
http://arxiv.org/abs/2104.02810
Publikováno v:
DSLW 2021: Proceedings of the IEEE Data Science and Learning Workshop 2021, pp.1-8. 2021
Clustering is a ubiquitous problem in data science and signal processing. In many applications where we observe noisy signals, it is common practice to first denoise the data, perhaps using wavelet denoising, and then to apply a clustering algorithm.
Externí odkaz:
http://arxiv.org/abs/2012.04762
Autor:
Weylandt, Michael, Michailidis, George
Publikováno v:
ICASSP 2021: Proceedings of the 2021 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.5609-5613. 2021
Clustering of time series data exhibits a number of challenges not present in other settings, notably the problem of registration (alignment) of observed signals. Typical approaches include pre-registration to a user-specified template or time warpin
Externí odkaz:
http://arxiv.org/abs/2012.04756
Autor:
Weylandt, Michael
Publikováno v:
2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp.500-504
We consider the problem of estimating multiple principal components using the recently-proposed Sparse and Functional Principal Components Analysis (SFPCA) estimator. We first propose an extension of SFPCA which estimates several principal components
Externí odkaz:
http://arxiv.org/abs/1907.12012
Financial markets for Liquified Natural Gas (LNG) are an important and rapidly-growing segment of commodities markets. Like other commodities markets, there is an inherent spatial structure to LNG markets, with different price dynamics for different
Externí odkaz:
http://arxiv.org/abs/1907.10152