6 papers
Towards Fine-Grained and Verifiable Concept Bottleneck Models
Yingying Fang, Haijie Xu, Shuang Wu +2
Concept Bottleneck Models (CBMs) offer interpretable alternatives to black-box predictors by introducing human-relatable concepts before the final output. However, existing CBMs st…
Combinatorial Bandit Bayesian Optimization for Tensor Outputs
Jingru Huang, Haijie Xu, Jie Guo +2
Bayesian optimization (BO) has been widely used to optimize expensive and black-box functions across various domains. However, existing BO methods have not addressed tensor-output…
Function-on-Function Bayesian Optimization
Jingru Huang, Haijie Xu, Manrui Jiang +1
Bayesian optimization (BO) has been widely used to optimize expensive and gradient-free objective functions across various domains. However, existing BO methods have not addressed…
Design of Experiment for Discovering Directed Mixed Graph
Haijie Xu, Chen Zhang
We study the problem of experimental design for accurately identifying the causal graph structure of a simple structural causal model (SCM), where the underlying graph may include…
Quickest Causal Change Point Detection by Adaptive Intervention
Haijie Xu, Chen Zhang
We propose an algorithm for change point monitoring in linear causal models that accounts for interventions. Through a special centralization technique, we can concentrate the chan…
Functional-Edged Network Modeling
Haijie Xu, Chen Zhang
Contrasts with existing works which all consider nodes as functions and use edges to represent the relationships between different functions. We target at network modeling whose ed…