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A popular line of recent research incorporates ML advice in the design of online algorithms to improve their performance in typical instances. These papers treat the ML algorithm as a blackbox, and redesign online algorithms to take advantage of ML predictions. In this paper, we ask the complementary question: can we redesign ML algorithms to provide better predictions for online algorithms? We explore this question in the context of the classic rent-or-buy problem, and show that incorporating optimization benchmarks directly in ML loss functions leads to significantly better performance, while maintaining a worst-case adversarial result when the advice is completelymore »Free, publicly-accessible full text available July 1, 2023
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Free, publicly-accessible full text available July 1, 2023
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Free, publicly-accessible full text available August 27, 2022
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East Antarctica is covered by thick sheets of ice and is underlain by stable cratonic lithosphere, extensive mountain ranges, and subglacial basins. The sparse seismic coverage in this region makes it difficult to assess the crustal and mantle structure, which are important to understanding the tectonic evolution of the continent as well as the behavior of the overlying ice sheets. Present tomographic models lack resolution and are often inconsistent with one another; therefore, delineating sub-surface characteristics associated with old rift systems or structures that would allow us to assess the origins of the Wilkes and Aurora subglacial basins, for instance,more »
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Free, publicly-accessible full text available August 1, 2022