Jiahua Liu — accepted AI-conference papers

2026

Outlier-Robust Diffusion Solvers for Inverse Problems

Yang Zheng, Jiahua Liu, Tongyao Pang, Wen Li, Zhaoqiang Liu

CVPR 2026

Methods based on diffusion models (DMs) for solving inverse problems (IPs) have recently achieved remarkable performance. However, DM-based methods typically struggle against outliers, which are common in real-world measurements. In this work, to tackle IPs with outliers, we first refine the measure

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2023

Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers

Weng Lam Tam, Xiao Liu, Kaixuan Ji, Lilong Xue, Jiahua Liu, tao li +2

EMNLP 2023long findings

Prompt tuning attempts to update few task-specific parameters in pre-trained models. It has achieved comparable performance to fine-tuning of the full parameter set on both language understanding and generation tasks. In this work, we study the problem of prompt tuning for neural text retrievers. We…

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