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A geometric way to find the measures of uncertainty from statistical divergences for discrete and finite probability distributions

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arxiv 2106.14874 v1 pith:HVQZYDXM submitted 2021-06-28 quant-ph

Gautam Sharma , Sk Sazim This is my paper · ORCID
classification quant-ph
keywords uncertaintymeasuresdiscretedistributionsdivergencesfinitegeometricprobability
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Exploiting the geometric nature of statistical divergences, we devise a way to define associated induced uncertainty measures for discrete and finite probability distributions. We also report new uncertainty measures and discuss their properties. Further, we apply a similar technique to measure the uncertainty in the preparation of a quantum state.

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  1. Loss Functions and Operators Generated by f-Divergences

    cs.LG 2025-01 conditional novelty 6.0 of 10

    Using f-divergence regularizers inside Fenchel-Young losses yields convex losses and fast f-softargmax operators, and the α=1.5 variant matches or beats cross-entropy in the tasks tested.

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