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Xiaoyan Hu

The Chinese University of Hong Kong

4 papers hereh-index 553 citations11 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author3
  • middle author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG3
  • cs.CV1
affiliations
  • The Chinese University of Hong Kong
HomepageORCID 0000-0002-5766-1059
same name
  • Xiaoyan Hu — 6 papers, h 9
  • Xiaoyan Hu — 1 paper, h 1
  • Xiaoyan Hu — 1 paper, h 1
  • Xiaoyan Hu — 1 paper, h 2
  • Xiaoyan Hu — 1 paper, h 3
  • Xiaoyan Hu — 1 paper, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

collaborators

4 papers

cs.CV2026

SVCBench: A Streaming Video Counting Benchmark for Spatial-Temporal State Maintenance

Pengyiang Liu, Zhongyue Shi, Hongye Hao +7

Video understanding requires models to continuously track and update world state during playback. Although existing benchmarks have advanced video understanding evaluation across m…

cs.LG2025

PromptWise: Online Learning for Cost-Aware Prompt Assignment in Generative Models

Xiaoyan Hu, Lauren Pick, Ho-fung Leung +1

The rapid advancement of generative AI has provided users with a wide range of well-trained models to address diverse prompts. When selecting a model for a given prompt, users shou…

cs.LG2025

PAK-UCB Contextual Bandit: An Online Learning Approach to Prompt-Aware Selection of Generative Models and LLMs

Xiaoyan Hu, Ho-fung Leung, Farzan Farnia

Selecting a sample generation scheme from multiple prompt-based generative models, including large language models (LLMs) and prompt-guided image and video generation models, is ty…

cs.LG2025

A Multi-Armed Bandit Approach to Online Selection and Evaluation of Generative Models

Xiaoyan Hu, Ho-fung Leung, Farzan Farnia

Existing frameworks for evaluating and comparing generative models consider an offline setting, where the evaluator has access to large batches of data produced by the models. Howe…

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