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Bryan Kian Hsiang Low

ICML 2026×7ACL 2026×2社会与人本数据与预训练安全可解释性理论与学习动力学
新星分资深
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方向广度13
合作网络9
0 一作 · 9 篇 · ICML 2026 / ACL 2026

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2026 顶会论文

9
作者位次 #3/3ICML 2026Poster· Social Aspects / Accountability, Transparency, and Interpretability

Adalina: Adaptive Linear Approximation for the Shapley Value and Beyond

Weida Li · Yaoliang Yu · Bryan Kian Hsiang Low

可解释性社会与人本OpenReview
作者位次 #4/4ICML 2026Poster· Theory / Online Learning and Bandits

Keep Everyone Happy: Online Fair Division of Numerous Items with Few Copies

Arun Verma · Indrajit Saha · Makoto Yokoo · Bryan Kian Hsiang Low

多模态理论与学习动力学OpenReview
作者位次 #4/4ACL 2026Findings· Findings of ACL

Prompting the Unknown: Understanding Response Uncertainty in Large Language Models

Ze Yu Zhang · Arun Verma · Finale Doshi-Velez · Bryan Kian Hsiang Low

摘要

Large language models (LLMs) are widely used in decision-making across diverse domains. Ensuring the generation of safe and reliable responses is critical for the effective deployment of LLM-based applications, particularly in high-stakes domains such as healthcare and finance. Most of these appl...

数据与预训练社会与人本理论与学习动力学OpenReview 项目页
作者位次 #3/3ACL 2026Findings· Findings of ACL

EUL o I nf: Efficient Hessian-Free Entropy Based Uncertainty-Aware Data Influence Approximation

Runxin Cai · Jingtan Wang · Bryan Kian Hsiang Low

摘要

In Large Language Model post-training, high-quality data effectively enhances model performance with fine-tuning, highlighting the need to identify high-quality and beneficial fine-tuning data. However, one of the most popular data valuation paradigms, influence function and its variants, are com...

强化学习与后训练效率与系统数据与预训练OpenReview 项目页

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