Search Results for author:
Found 8 papers, 2 papers with code
Efficient Bayesian Optimization with Deep Kernel Learning and Transformer Pre-trained on Multiple Heterogeneous Datasets
Bayesian optimization (BO) is widely adopted in black-box optimization problems and it relies on a surrogate model to approximate the black-box response function.
Reframed GES with a Neural Conditional Dependence Measure
In this paper, we revisit the Greedy Equivalence Search (GES) algorithm, which is widely cited as a score-based algorithm for learning the MEC of the underlying causal structure.
Rethinking Client Reweighting for Selfish Federated Learning
To this end, we study a new $\textit{selfish}$ variant of federated learning, in which the ultimate objective is to learn a model with optimal performance on internal clients $\textit{alone}$ instead of all clients.
Contrastive ACE: Domain Generalization Through Alignment of Causal Mechanisms
Domain generalization aims to learn knowledge invariant across different distributions while semantically meaningful for downstream tasks from multiple source domains, to improve the model's generalization ability on unseen target domains.
A Causal Direction Test for Heterogeneous Populations
Our simulation result show that the proposed adjustment significantly improves the performance of the causal direction test statistic for heterogeneous data.
Domain Generalization via Multidomain Discriminant Analysis
Domain generalization (DG) aims to incorporate knowledge from multiple source domains into a single model that could generalize well on unseen target domains.
A Kernel Embedding-based Approach for Nonstationary Causal Model Inference
Although nonstationary data are more common in the real world, most existing causal discovery methods do not take nonstationarity into consideration.
Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models
The inference of the causal relationship between a pair of observed variables is a fundamental problem in science, and most existing approaches are based on one single causal model.
