Shaocheng Lan — accepted AI-conference papers

2026

ConTSG-Bench: A Unified Benchmark for Conditional Time Series Generation

Shaocheng Lan, Shuqi Gu, Zhangzhi Xiong, Kan Ren

ICML 2026poster

Conditional time series generation plays a critical role in addressing data scarcity and enabling causal analysis in real-world applications. Despite its increasing importance, the field lacks a standardized and systematic benchmarking framework for evaluating generative models across diverse condit…

Cited by 0 Source ↗Scholar ↗

2025

Learning to Select In-Context Demonstration Preferred by Large Language Model

Zheng Zhang, Shaocheng Lan, Lei Song, Jiang Bian, Yexin Li, Kan Ren

ACL 2025finding

In-context learning (ICL) enables large language models (LLMs) to adapt to new tasks during inference using only a few demonstrations. However, ICL performance is highly dependent on the selection of these demonstrations. Recent work explores retrieval-based methods for selecting query-specific demo…

Cited by 0 Source ↗PDF ↗Scholar ↗