Location: Luskin Conference Center

Bin Yu, CDSS Chancellor’s Distinguished Professor in Statistics, EECS, and Computational Biology, UC Berkeley

Abstract:

Data Science is central to AI and has driven most of the recent advances in biomedicine and beyond. Human judgment calls are ubiquitous at every step of the data science life cycle (DSLC): problem formulation, data cleaning,EDA, modeling, and reporting. Such judgment calls are often responsible for the “dangers” of AI by creating auniverse of hidden uncertainties well beyond sample-to-sample uncertainty. To mitigate these dangers, veridical (truthful) data science is introduced based on three key principles: Predictability, Computability and Stability (PCS).The PCS framework (including documentation) unifies, streamlines, and expands on the ideas and best practices ofstatistics and machine learning. This talk showcases PCS uncertainty quantification (PCS-UQ) with applications toprediction including deep learning. It compares PCS-UQ and makes connections with Conformal Prediction (CP). Over a collection of 17 regression tabular datasets, 6 multi-class tabular datasets, and 4 deep learning datasets, PCS-UQ reduces the size of the prediction intervals or sets by around 20% on average when compared to the best CPmethod among the ones used by PCS-UQ, and has better subgroup coverages than CP overall.

Bio:

Bin Yu is CDSS Chancellor’s Distinguished Professor in Statistics, EECS, and Computational Biology, and Scientific Advisor at theSimons Institute for the Theory of Computing, all at UC Berkeley. Herresearch focuses on the practice and theory of statistical machine learning,veridical data science, responsible and safe AI, and solving interdisciplinarydata problems in neuroscience, genomics, and precision medicine. She andher team have developed algorithms such as iterative random forests (iRF),stability-driven NMF, and adaptive wavelet distillation (AWD) from deeplearning models. She is a member of the National Academy of Sciences and of the American Academy of Arts and Sciences. She was a Guggenheim Fellow, IMS President, and delivered the IMS Rietz and Wald Lectures and Distinguished Achievement Award and Lecture (formerly Fisher Lecture) of COPSS. She holds an Honorary Doctorate from The University of Lausanne.

Wen-Xin Zhou, Associate Professor
Department of Information and Decision Sciences, University of Illinois Chicago

Abstract:

Expected Shortfall (ES), also known as superquantile or Conditional Value-at-Risk, has been recognized as an important risk measure in economics and finance. In this talk, we consider a joint regression framework that simultaneously models the conditional quantile and ES of a response variable given a set of covariates, for which the state-of-the-art approach is based on minimizing a joint loss function that is non-differentiable and non-convex. Motivated by the idea of using orthogonal scores to reduce sensitivity to nuisance parameters, we study a two-step framework for fitting joint quantile and ES regression models nonparametrically over RKHSs and using deep neural networks. We establish a non-asymptotic theory for the proposed estimators, carefully characterizing the impact of quantile estimation without relying on sample splitting. For ES kernel ridge regression, we further propose a fast inference method to construct pointwise confidence bands. For NN-based ES regression, we introduce a Huberized estimator that is robust against heavy tails in the response distribution. A Python package, quantes (https://pypi.org/project/quantes/), has been developed to implement various ES regression methods.

Bio:

Dr. Wenxin Zhou is an Associate Professor in the Department of Information and Decision Sciences at the University of Illinois Chicago. Before joining UIC, he was a faculty member at UC San Diego. His research focuses on high-dimensional statistical inference, nonparametric methods, robust statistics, and quantile regression. He currently serves as an Associate Editor for the Journal of the Royal Statistical Society Series B and the Annals of Applied Probability. He earned his PhD from the Hong Kong University of Science and Technology.

Hongtu Zhu, Professor
Department of Biostatistics, University of North Carolina at Chapel Hill

Abstract:

We introduce the Non-Crossing Quantile (NQ) Network, a novel approach for conditional distribution learning. By incorporating non-negative activation functions, the NQ network ensures monotonicity in learned distributions, effectively eliminating the issue of quantile crossing. The NQ network offers a highly adaptable deep distributional learning framework, applicable to a wide range of tasks, from non-parametric quantile regression to causal effect estimation and distributional reinforcement learning (RL). We further establish a comprehensive theoretical foundation for the deep NQ estimator and its application in distributional RL, providing rigorous analysis to support its effectiveness. Extensive experiments demonstrate the robustness and versatility of the NQ network across various domains, including clinical trials, e-commerce, games, and healthcare, highlighting its potential for real-world applications. This is based on a series of joint works with Drs. Shen, Luo, and Shi and Mr. Huang.

Bio:

Dr. Hongtu Zhu is a Professor of Biostatistics, Statistics, Radiology, Computer Science, and Genetics at the University of North Carolina at Chapel Hill. He previously served as a DiDi Fellow and Chief Scientist of Statistics at DiDi Chuxing (2018–2020) and held the Endowed Bao-Shan Jing Professorship in Diagnostic Imaging at MD Anderson Cancer Center (2016–2018). Dr. Zhu is a Fellow of IEEE, ASA, and IMS and currently serves as the Coordinating Editor of JASA and the Editor of JASA Applications and Case Studies. He has received several prestigious awards, including the Established Investigator Award from the Cancer Prevention Research Institute of Texas (2016) and the INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice (2019). Dr. Zhu has authored over 350 publications in top journals such as Nature, Science, Cell, and Nature Genetics as well as all major statistical journals, and has presented more than 55 conference papers at leading machine learning and AI conferences, including NeurIPS, KDD, AAAI, ICML, and ICLR.

Location: Franz Hall 2258A

Lan Wang, Professor
Miami Herbert Business School, University of Miami

Abstract: TBA

Bio: TBA

Location: Franz Hall 2258A

Stefan Wager, Associate Professor of Operations, Information, and Technology
Graduate School of Business, Stanford University

Abstract: TBA

Bio: TBA

Location: CHS 33-105

Stats/Biostats Joint Seminar

Ian McKeague, Professor of Biostatistics
Mailman School of Public Health, Columbia University

Abstract: TBA

Bio: TBA