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Query rewriting via large language models
J Liu, B Mozafari - ar** with poorly written queries
before passing them down to the query optimizer. Manual rewriting is not scalable, as it is …
before passing them down to the query optimizer. Manual rewriting is not scalable, as it is …
Causal representation learning from multiple distributions: A general setting
In many problems, the measured variables (eg, image pixels) are just mathematical
functions of the latent causal variables (eg, the underlying concepts or objects). For the …
functions of the latent causal variables (eg, the underlying concepts or objects). For the …
Score-based causal discovery of latent variable causal models
Identifying latent variables and the causal structure involving them is essential across
various scientific fields. While many existing works fall under the category of constraint …
various scientific fields. While many existing works fall under the category of constraint …
Learning Discrete Concepts in Latent Hierarchical Models
Learning concepts from natural high-dimensional data (eg, images) holds potential in
building human-aligned and interpretable machine learning models. Despite its …
building human-aligned and interpretable machine learning models. Despite its …
Adversarial-Causal Representation Learning Networks for Machine fault diagnosis under unseen conditions based on vibration and acoustic signals
F Wu, Z **ang, D **ao, Y Hao, Y Qin, H Pu… - … Applications of Artificial …, 2025 - Elsevier
To address the challenges of obtaining diverse data, domain generalization (DG) methods
for fault diagnosis have been developed. Domain adversarial methods are currently the …
for fault diagnosis have been developed. Domain adversarial methods are currently the …
Differentiable Causal Discovery For Latent Hierarchical Causal Models
Discovering causal structures with latent variables from observational data is a fundamental
challenge in causal discovery. Existing methods often rely on constraint-based, iterative …
challenge in causal discovery. Existing methods often rely on constraint-based, iterative …
On the Parameter Identifiability of Partially Observed Linear Causal Models
Linear causal models are important tools for modeling causal dependencies and yet in
practice, only a subset of the variables can be observed. In this paper, we examine the …
practice, only a subset of the variables can be observed. In this paper, we examine the …
Identifying Latent State-Transition Processes for Individualized Reinforcement Learning
The application of reinforcement learning (RL) involving interactions with individuals has
grown significantly in recent years. These interactions, influenced by factors such as …
grown significantly in recent years. These interactions, influenced by factors such as …
A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery
Real-world data often violates the equal-variance assumption (homoscedasticity), making it
essential to account for heteroscedastic noise in causal discovery. In this work, we explore …
essential to account for heteroscedastic noise in causal discovery. In this work, we explore …
Permutation-Based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data
Recent advances have shown that statistical tests for the rank of cross-covariance matrices
play an important role in causal discovery. These rank tests include partial correlation tests …
play an important role in causal discovery. These rank tests include partial correlation tests …