Seonghwan Park — accepted AI-conference papers

2025

An Analysis of Concept Bottleneck Models: Measuring, Understanding, and Mitigating the Impact of Noisy Annotations

Seonghwan Park, Jueun Mun, Donghyun Oh, Namhoon Lee

NeurIPS 2025poster

Concept bottleneck models (CBMs) ensure interpretability by decomposing predictions into human interpretable concepts. Yet the annotations used for training CBMs that enable this transparency are often noisy, and the impact of such corruption is not well understood. In this study, we present the fir…

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2025

ZIP: An Efficient Zeroth-order Prompt Tuning for Black-box Vision-Language Models

Seonghwan Park, Jaehyeon Jeong, Yongjun Kim, Jaeho Lee, Namhoon Lee

ICLR 2025poster

Recent studies have introduced various approaches for prompt-tuning black-box vision-language models, referred to as black-box prompt-tuning (BBPT). While BBPT has demonstrated considerable potential, it is often found that many existing methods require an excessive number of queries (i.e., function…

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