Atsushi Shimizu — accepted AI-conference papers

2024

Improved Active Learning via Dependent Leverage Score Sampling

Atsushi Shimizu, Xiaoou Cheng, Christopher Musco, Jonathan Weare

ICLR 2024oral

We show how to obtain improved active learning methods in the agnostic (adversarial noise) setting by combining marginal leverage score sampling with non-independent sampling strategies that promote spatial coverage. In particular, we propose an easily implemented method based on the \emph{pivotal s…

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2018

Complexity Reduction Algorithm for Optimum Quantizer Design Based on Amplitude Sparseness

Yukihiro Bandoh, Seishi Takamura, Atsushi Shimizu

ICASSP 2018accepted

The design of an optimum quantizer can be formulated as an optimization problem that finds the quantization indices that minimize the quantization error. One solution of the optimization problem is DP quantization, an approach based on dynamic programming. It is known that a quantized signal does no…

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