Contributed Papers

Efficient Learning and Uncertainty Quantification in High-Dimensional and Computational Systems

Mon, Aug 4, 10:30 AM - 12:20 PM Room CC-205C Music City Center

Uncertainty Quantification in Complex Systems Interest Group co: Uncertainty Quantification in Complex Systems Interest Group

5 Presentations

A Bayesian Optimization Framework for Personalized System Design Based on Computer Experiments

10:35 AM - 10:50 AM

Co-authors: Jiacheng Liu (University of Michigan), Wenbo Sun, Jiacheng Liu (University of Michigan), Jingwen Hu (University of Michigan Transportation Research Institute), Judy Jin (University of Michigan)

Bayesian Optimization Personalized System Design Vehicle Restraint System Design

WITHDRAWN A Likelihood-Based Approach to Distribution Regression Using Conditional Deep Generative Models

10:50 AM - 11:05 AM

Co-authors: Shivam Kumar, Yun Yang (University of Illinois Urbana-Champaign), Lizhen Lin

Deep Generative Model Conditional Distribution Smoothness Disparity Sieve MLE Manifold Curse of dimensionality

Uncertainty of Network Topology with Applications to Out-of-Distribution Detection

11:05 AM - 11:20 AM

Co-authors: Chun-Hao Yang (National Taiwan University), Sing-Yuan Yeh (National Taiwan University), Chun-Hao Yang (National Taiwan University)

Persistent Homology Bayesian Neural Network Out-of-Distribution Uncertainty Topological Data Analysis

Fast data inversion for high-dimensional dynamical systems from noisy measurements

11:20 AM - 11:35 AM

Co-authors: Yizi Lin, Xubo Liu (University of California, Santa Barbara), Mengyang Gu (University of California, Santa Barbara), Paul Segall (Stanford University), Mengyang Gu (University of California, Santa Barbara)

Bayesian prior latent factor models Gaussian processes expectation-maximization algorithm Kalman filter

Multi-Task Active Learning with Efficient Resource Allocation

11:35 AM - 11:50 AM

Co-authors: Hanwen Ye, Annie Qu (University of California At Irvine), Hanwen Ye

Active learning Sequential decision-making Surrogate learning Sample efficiency Feature acquisition Resource allocation