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Efficient Bayesian Optimization with Deep Kernel Learning and Transformer Pre-trained on Multiple Heterogeneous Datasets

Autor: Lyu, Wenlong, Hu, Shoubo, Chuai, Jie, Chen, Zhitang

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. With the increasing number of black-box optimization tasks solved and even more to solv

Externí odkaz: http://arxiv.org/abs/2308.04660

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Reframed GES with a Neural Conditional Dependence Measure

Autor: Shen, Xinwei, Zhu, Shengyu, Zhang, Jiji, Hu, Shoubo, Chen, Zhitang

In a nonparametric setting, the causal structure is often identifiable only up to Markov equivalence, and for the purpose of causal inference, it is useful to learn a graphical representation of the Markov equivalence class (MEC). In this paper, we r

Externí odkaz: http://arxiv.org/abs/2206.08531

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Contrastive ACE: Domain Generalization Through Alignment of Causal Mechanisms

Autor: Wang, Yunqi, Liu, Furui, Chen, Zhitang, Lian, Qing, Hu, Shoubo, Hao, Jianye, Wu, Yik-Chung

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. The fundam

Externí odkaz: http://arxiv.org/abs/2106.00925

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A Causal Direction Test for Heterogeneous Populations

Autor: Nia, Vahid Partovi, Li, Xinlin, Asgharian, Masoud, Hu, Shoubo, Chen, Zhitang, Geng, Yanhui

A probabilistic expert system emulates the decision-making ability of a human expert through a directional graphical model. The first step in building such systems is to understand data generation mechanism. To this end, one may try to decompose a mu

Externí odkaz: http://arxiv.org/abs/2006.04877

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Domain Generalization via Multidomain Discriminant Analysis

Autor: Hu, Shoubo, Zhang, Kun, Chen, Zhitang, Chan, Laiwan

Domain generalization (DG) aims to incorporate knowledge from multiple source domains into a single model that could generalize well on unseen target domains. This problem is ubiquitous in practice since the distributions of the target data may rarel

Externí odkaz: http://arxiv.org/abs/1907.11216

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Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models

Autor: Hu, Shoubo, Chen, Zhitang, Nia, Vahid Partovi, Chan, Laiwan, Geng, Yanhui

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. In practice, however, observations are often collected from multiple

Externí odkaz: http://arxiv.org/abs/1809.08568

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A Kernel Embedding-based Approach for Nonstationary Causal Model Inference

Autor: Hu, Shoubo, Chen, Zhitang, Chan, Laiwan

Publikováno v: Neural computation, 30(5), 1394-1425, 2018

Although nonstationary data are more common in the real world, most existing causal discovery methods do not take nonstationarity into consideration. In this letter, we propose a kernel embedding-based approach, ENCI, for nonstationary causal model i

Externí odkaz: http://arxiv.org/abs/1809.08560

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