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pro vyhledávání: '"Reimherr, Matthew"'
Many fMRI analyses examine functional connectivity, or statistical dependencies among remote brain regions. Yet popular methods for studying whole-brain functional connectivity often yield results that are difficult to interpret. Factor analysis offe
Externí odkaz:
http://arxiv.org/abs/2409.13963
In this paper we consider the problem of releasing a Gaussian Differentially Private (GDP) 3D human face. The human face is a complex structure with many features and inherently tied to one's identity. Protecting this data, in a formally private way,
Externí odkaz:
http://arxiv.org/abs/2409.08301
Autor:
Lin, Haotian, Reimherr, Matthew
Many existing two-phase kernel-based hypothesis transfer learning algorithms employ the same kernel regularization across phases and rely on the known smoothness of functions to obtain optimality. Therefore, they fail to adapt to the varying and unkn
Externí odkaz:
http://arxiv.org/abs/2402.14966
The U.S. Census Longitudinal Business Database (LBD) product contains employment and payroll information of all U.S. establishments and firms dating back to 1976 and is an invaluable resource for economic research. However, the sensitive information
Externí odkaz:
http://arxiv.org/abs/2309.02416
Autor:
Lin, Haotian, Reimherr, Matthew
Many existing mechanisms to achieve differential privacy (DP) on infinite-dimensional functional summaries often involve embedding these summaries into finite-dimensional subspaces and applying traditional DP techniques. Such mechanisms generally tre
Externí odkaz:
http://arxiv.org/abs/2309.00125
Publikováno v:
Journal of Computational and Graphical Statistics 2024
Functional regression analysis is an established tool for many contemporary scientific applications. Regression problems involving large and complex data sets are ubiquitous, and feature selection is crucial for avoiding overfitting and achieving acc
Externí odkaz:
http://arxiv.org/abs/2303.14801
It is common for data structures such as images and shapes of 2D objects to be represented as points on a manifold. The utility of a mechanism to produce sanitized differentially private estimates from such data is intimately linked to how compatible
Externí odkaz:
http://arxiv.org/abs/2209.12667
Autor:
Lin, Haotian, Reimherr, Matthew
We study the transfer learning (TL) for the functional linear regression (FLR) under the Reproducing Kernel Hilbert Space (RKHS) framework, observing the TL techniques in existing high-dimensional linear regression is not compatible with the truncati
Externí odkaz:
http://arxiv.org/abs/2206.04277
Differential privacy (DP) quantifies privacy loss by analyzing noise injected into output statistics. For non-trivial statistics, this noise is necessary to ensure finite privacy loss. However, data curators frequently release collections of statisti
Externí odkaz:
http://arxiv.org/abs/2204.01102
Publikováno v:
Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
Implementations of the exponential mechanism in differential privacy often require sampling from intractable distributions. When approximate procedures like Markov chain Monte Carlo (MCMC) are used, the end result incurs costs to both privacy and acc
Externí odkaz:
http://arxiv.org/abs/2204.01132