Shuqing Li — accepted AI-conference papers

2026

Position: Code Benchmarks Should Prioritize Rigor, Reliability, and Reproducibility

Jialun Cao, Yuk-Kit Chan, Zixuan Ling, Wenxuan Wang, Shuqing Li, Mingwei Liu +9

ICML 2026poster

Code-related benchmarks play a critical role in evaluating large language models (LLMs), yet their quality fundamentally shapes how the com- munity interprets model capabilities. In the past few years, awareness of benchmark quality has grown. Yet, after a decade-scale (2014 - 2025) survey over 572 …

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2019

Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers

Liwei Wu, Shuqing Li, Cho-Jui Hsieh, James L Sharpnack

NeurIPS 2019poster

In deep neural nets, lower level embedding layers account for a large portion of the total number of parameters. Tikhonov regularization, graph-based regularization, and hard parameter sharing are approaches that introduce explicit biases into training in a hope to reduce statistical complexity. Alt…

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