主题：Multi-Fidelity Quantile Regression
主讲人：张耀 新加坡国立大学
主持人：钟清枝 暨南大学
时间：2026年5月15日（周五）中午12:00-14:00
地点：暨南大学石牌校区经济学院大楼（中惠楼）503室
摘要
High-fidelity (HF) data are often expensive to collect and therefore scarce, making conditional quantiles difficult to estimate accurately. We propose a two-stage, model-agnostic method for multi-fidelity quantile regression. The central idea is a local quantile link: at each covariate value, the HF quantile is represented as a low-fidelity (LF) evaluated at a covariate-dependent level. This reformulation reduces the problem to estimating the level function, which can be smoother than the HF quantile itself when the LF and HF conditional distributions have similar shapes. We also study the complementary regime in which this advantage weakens and introduce a correction step to improve robustness. Our theory characterizes when the proposed estimator converges faster than direct quantile regression using HF data alone and when the correction step provides further improvement. Experiments on synthetic and real data show that our method yields more accurate quantile estimates and tighter conformal prediction intervals.
主讲人简介
张耀是新加坡国立大学统计与数据科学系的统计学助理教授。在此之前，他曾担任斯坦福大学博士后研究员，师从Emmanuel Candès教授。他于Mihaela van der Schaar教授指导下获得数学博士学位。其研究方向聚焦于发展轻假设方法，用于分析预测与因果推断中的数据与模型。
校对 |钟清枝
责编 | 彭毅
初审 | 姜云卢
终审发布 | 何凌云
（来源：暨南大学经济学院微信公众号）
