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Taro Toyoizumi

2 accepted papers

2026

Dataset Distillation Efficiently Encodes Low-Dimensional Representations from Gradient-Based Learning of Non-Linear Tasks

ICML 2026poster

Dataset distillation, a training-aware data compression technique, has recently attracted increasing attention as an effective tool for mitigating costs of optimization and data storage. However, progress remains largely empirical. Mechanisms underlying the extraction of task-relevant information fr…

Cited by 0SourceScholar
2024

A provable control of sensitivity of neural networks through a direct parameterization of the overall bi-Lipschitzness

NeurIPS 2024poster

While neural networks can enjoy an outstanding flexibility and exhibit unprecedented performance, the mechanism behind their behavior is still not well-understood. To tackle this fundamental challenge, researchers have tried to restrict and manipulate some of their properties in order to gain new in…