all AI news
Robust Uncertainty Bounds in Reproducing Kernel Hilbert Spaces: A Convex Optimization Approach. (arXiv:2104.09582v3 [cs.LG] UPDATED)
Sept. 13, 2022, 1:12 a.m. | Paul Scharnhorst, Emilio T. Maddalena, Yuning Jiang, Colin N. Jones
cs.LG updates on arXiv.org arxiv.org
The problem of establishing out-of-sample bounds for the values of an unkonwn
ground-truth function is considered. Kernels and their associated Hilbert
spaces are the main formalism employed herein along with an observational model
where outputs are corrupted by bounded measurement noise. The noise can
originate from any compactly supported distribution and no independence
assumptions are made on the available data. In this setting, we show how
computing tight, finite-sample uncertainty bounds amounts to solving parametric
quadratically constrained linear programs. Next, …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Principal Data Engineer
@ RS21 | Remote
SQL/Power BI Developer
@ ICF | Virginia Remote Office (VA99)
Senior Machine Learning Engineer (Canada Remote)
@ Fullscript | Ottawa, ON
Software Engineer - MLOps.
@ Renesas Electronics | Toyosu, Japan
Junior Data Scientist / Artificial Intelligence consultant
@ Deloitte | Luxembourg, LU