3 papers
cs.LG2025
Fine-Tuned In-Context Learners for Efficient Adaptation
Jorg Bornschein, Clare Lyle, Yazhe Li +3
When adapting large language models (LLMs) to a specific downstream task, two primary approaches are commonly employed: (1) prompt engineering, often with in-context few-shot learn…
stat.ML2025
On the Hardness of Conditional Independence Testing In Practice
Zheng He, Roman Pogodin, Yazhe Li +3
Tests of conditional independence (CI) underpin a number of important problems in machine learning and statistics, from causal discovery to evaluation of predictor fairness and out…
cs.LG2025
Practical Kernel Tests of Conditional Independence
Roman Pogodin, Antonin Schrab, Yazhe Li +2
We describe a data-efficient, kernel-based approach to statistical testing of conditional independence. A major challenge of conditional independence testing is to obtain the corre…