5 papers
Is It Still Worth Training a Classical Model in the Era of LLMs? A Crossover Benchmark on Tabular Data
Kaihua Ding
Large language models can label a tabular row from a plain-English description with no training - a capability now shipping in mainstream spreadsheet tools such as Microsoft Copilo…
Scaling Laws for Classical Machine Learning on Tabular Data: A Benchmark Study
Kaihua Ding
Prior classical-ML learning-curve work fits power laws to tree, linear, and kernel models on tabular data, but at small scale: typically one curve, one team, a handful of cells. We…
When LLMs Agree, Are They Right? Auditing Self-Consistency and Cross-Model Agreement as Confidence Signals
Kaihua Ding
LLM-as-judge (Zheng et al., 2023) is increasingly the default for evaluating AI systems in enterprise pipelines, often scaled to ensembles (Verga et al., 2024) or "mixture-of-exper…
Variance-Bounded Evaluation of Entity-Centric AI Systems Without Ground Truth: Theory and Measurement
Kaihua Ding
Reliable evaluation of AI systems remains a fundamental challenge when ground truth labels are unavailable, particularly for systems generating natural language outputs like AI cha…
Iterative Causal Segmentation: Filling the Gap between Market Segmentation and Marketing Strategy
Kaihua Ding, Jingsong Cui, Mohammad Soltani +1
The field of causal Machine Learning (ML) has made significant strides in recent years. Notable breakthroughs include methods such as meta learners (arXiv:1706.03461v6) and heterog…