Lili Zheng is a current postdoctoral researcher in the Department of Electrical and Computer Engineering at Rice University, mentored by Prof. Genevera I. Allen. Prior to this, she obtained her Ph.D. degree from the Department of Statistics at the University of Wisconsin-Madison, mentored by Prof. Garvesh Raskutti. Her research interests include graph learning, interpretable machine learning, uncertainty quantification, tensor data analysis, ensemble methods, and time series. Her website can be found at https://lili-zheng-stat.github.io
Talk: Uncertainty Quantification for Interpretable Machine Learning
Abstract: Interpretable machine learning has been widely deployed for scientific discoveries and decision-making, while its reliability hinges on the critical role of uncertainty quantification (UQ). In this talk, I will discuss UQ in two challenging scenarios motivated by scientific and societal applications: selective inference for large-scale graph learning and UQ for model-agnostic machine learning interpretations. Specifically, the first part concerns graphical model inference when only irregular, patchwise observations are available, a common setting in neuroscience, healthcare, genomics, and econometrics. To filter out low-confidence edges due to the irregular measurements, I will present a novel inference method that quantifies the uneven edgewise uncertainty levels over the graph as well as an FDR control procedure; this is achieved by carefully disentangling the dependencies across the graph and consequently yields more reliable graph selection. In the second part, I will discuss the computational and statistical challenges associated with UQ for feature importance of any machine learning model. I will take inspiration from recent advances in conformal inference and utilize an ensemble framework to address these challenges. This leads to an almost computationally free, assumption-light, and statistically powerful inference approach for occlusion-based feature importance. For both parts of the talk, I will highlight the potential applications of my research in science and society as well as how it contributes to more reliable and trustworthy data science.