Publications [#384665] of Robert Calderbank

Conference articles PUBLISHED

1. Ding, J; Calderbank, R; Tarokh, V, Gradient information for representation and modeling, Advances in Neural Information Processing Systems, vol. 32 (January, 2019)
   (last updated on 2026/01/17)

   Abstract:
   Motivated by Fisher divergence, in this paper we present a new set of information quantities which we refer to as gradient information. These measures serve as surrogates for classical information measures such as those based on logarithmic loss, Kullback-Leibler divergence, directed Shannon information, etc. in many data-processing scenarios of interest, and often provide significant computational advantage, improved stability, and robustness. As an example, we apply these measures to the Chow-Liu tree algorithm, and demonstrate remarkable performance and significant computational reduction using both synthetic and real data.