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Clark Glymour

8 accepted papers

2022

Action-Sufficient State Representation Learning for Control with Structural Constraints

Biwei Huang, Chaochao Lu, Liu Leqi, Jose Miguel Hernandez-Lobato, Clark Glymour, Bernhard Schölkopf +1
ICML 2022spotlight

Perceived signals in real-world scenarios are usually high-dimensional and noisy, and finding and using their representation that contains essential and sufficient information required by downstream decision-making tasks will help improve computational efficiency and generalization ability in the ta…

Cited by 48Source ↗PDF ↗Scholar ↗
2022

Latent Hierarchical Causal Structure Discovery with Rank Constraints

Biwei Huang, Charles Low, Feng Xie, Clark Glymour, Kun Zhang
NeurIPS 2022accept

Most causal discovery procedures assume that there are no latent confounders in the system, which is often violated in real-world problems. In this paper, we consider a challenging scenario for causal structure identification, where some variables are latent and they may form a hierarchical graph st…

Cited by 57Source ↗PDF ↗Scholar ↗
2020

Domain Adaptation as a Problem of Inference on Graphical Models

Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, QINGSONG LIU, Clark Glymour
NeurIPS 2020poster

This paper is concerned with data-driven unsupervised domain adaptation, where it is unknown in advance how the joint distribution changes across domains, i.e., what factors or modules of the data distribution remain invariant or change across domains. To develop an automated way of domain adaptatio…

Cited by 80Source ↗PDF ↗code ↗Scholar ↗
2020

Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs

Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang
NeurIPS 2020spotlight

Causal discovery aims to recover causal structures or models underlying the observed data. Despite its success in certain domains, most existing methods focus on causal relations between observed variables, while in many scenarios the observed ones may not be the underlying causal variables (e.g., i…

Cited by 120Source ↗PDF ↗Scholar ↗
2019

Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models

Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour
ICML 2019oral

In many scientific fields, such as economics and neuroscience, we are often faced with nonstationary time series, and concerned with both finding causal relations and forecasting the values of variables of interest, both of which are particularly challenging in such nonstationary environments. In th…

Cited by 94Source ↗PDF ↗Scholar ↗
2019

Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering

Biwei Huang, Kun Zhang, Pengtao Xie, Mingming Gong, Eric P Xing, Clark Glymour
NeurIPS 2019poster

State-of-the-art approaches to causal discovery usually assume a fixed underlying causal model. However, it is often the case that causal models vary across domains or subjects, due to possibly omitted factors that affect the quantitative causal effects. As a typical example, causal connectivity in…

Cited by 26Source ↗PDF ↗code ↗Scholar ↗
2019

Triad Constraints for Learning Causal Structure of Latent Variables

Ruichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang
NeurIPS 2019poster

Learning causal structure from observational data has attracted much attention, and it is notoriously challenging to find the underlying structure in the presence of confounders (hidden direct common causes of two variables). In this paper, by properly leveraging the non-Gaussianity of the data, we…

Cited by 84Source ↗PDF ↗Scholar ↗
2016

Domain Adaptation with Conditional Transferable Components

Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, Bernhard Schölkopf
ICML 2016poster

Domain adaptation arises in supervised learning when the training (source domain) and test (target domain) data have different distributions. Let X and Y denote the features and target, respectively, previous work on domain adaptation considers the covariate shift situation where the distribution of…

Cited by 435Source ↗PDF ↗Scholar ↗