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pro vyhledávání: '"Shoemaker, Christine"'
We address the challenges posed by heteroscedastic noise in contextual decision-making. We propose a consistent Shrinking Neighborhood Estimation (SNE) technique that successfully estimates contextual performance under unpredictable variances. Furthe
Externí odkaz:
http://arxiv.org/abs/2308.10235
Publikováno v:
Neural Networks, Volume 176, 2024, 106335
Providing a model that achieves a strong predictive performance and is simultaneously interpretable by humans is one of the most difficult challenges in machine learning research due to the conflicting nature of these two objectives. To address this
Externí odkaz:
http://arxiv.org/abs/2307.05639
Publikováno v:
In Environmental Modelling and Software September 2024 180
Publikováno v:
In Neural Networks August 2024 176
Publikováno v:
In Journal of Hydrology May 2024 634
Publikováno v:
In Environmental Modelling and Software April 2024 175
Autor:
Kulkarni, Neeraj, Gonzalez-Pumariega, Gonzalo, Khurana, Amulya, Shoemaker, Christine, Delimitrou, Christina, Albonesi, David
Multi-tenancy for latency-critical applications leads to re-source interference and unpredictable performance. Core reconfiguration opens up more opportunities for colocation,as it allows the hardware to adjust to the dynamic performance and power ne
Externí odkaz:
http://arxiv.org/abs/2008.00329
This paper describes Plumbing for Optimization with Asynchronous Parallelism (POAP) and the Python Surrogate Optimization Toolbox (pySOT). POAP is an event-driven framework for building and combining asynchronous optimization strategies, designed for
Externí odkaz:
http://arxiv.org/abs/1908.00420
Multi-Objective Optimization (MOO) is very difficult for expensive functions because most current MOO methods rely on a large number of function evaluations to get an accurate solution. We address this problem with surrogate approximation and paralle
Externí odkaz:
http://arxiv.org/abs/1903.02167
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