Title: Conformal prediction for time-series and flow-based generative models

Date: Jan 7th, 2025

Time: 1:30PM – 3PM

Location: Groseclose 403, meeting link

Chen Xu

Operations Research PhD Student

H. Milton Stewart School of Industrial and Systems Engineering

Georgia Institute of Technology

Committee: 

Dr. Yao Xie (Advisor), H. Milton Stewart School of Industrial and Systems Engineering 

Dr. Arkadi Nemirovski, H. Milton Stewart School of Industrial and Systems Engineering 

Dr. Johannes Milz, H. Milton Stewart School of Industrial and Systems Engineering 

Dr. Xiuyuan Cheng, Duke University 

Dr. Feng Qiu, Argonne National Laboratory

Abstract: This thesis addresses two key areas: quantifying uncertainty in point prediction models (Chapters 1 and 2) and modeling data distributions with flow-based generative models (Chapters 3 and 4). Chapter 1 extends conformal prediction to time-series data, providing prediction intervals with bounded conditional coverage gaps and demonstrating superior empirical performance. Chapter 2 builds on this by sequentially updating non-conformity quantiles to better capture time-series dependencies and introducing ellipsoidal prediction regions for multivariate time-series. On the other hand, Chapter 3 develops flow-based models using ordinary differential equations, enabling novel sample generation and likelihood estimation via a framework based on the Jordan-Kinderlehrer-Otto scheme for stage-wise training. Finally, Chapter 4 enhances the scalability of ODE-based models by introducing a local flow matching approach, improving training efficiency and distillation performance.