Zobrazeno 1 - 10
of 252
pro vyhledávání: '"Spirtes, Peter"'
We give a novel nonparametric pointwise consistent statistical test (the Markov Checker) of the Markov condition for directed acyclic graph (DAG) or completed partially directed acyclic graph (CPDAG) models given a dataset. We also introduce the Cros
Externí odkaz:
http://arxiv.org/abs/2409.20187
Autor:
Dong, Xinshuai, Ng, Ignavier, Huang, Biwei, Sun, Yuewen, Jin, Songyao, Legaspi, Roberto, Spirtes, Peter, Zhang, Kun
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 parameter identifiability of these models by investigating whether the edge c
Externí odkaz:
http://arxiv.org/abs/2407.16975
The widespread integration of Machine Learning systems in daily life, particularly in high-stakes domains, has raised concerns about the fairness implications. While prior works have investigated static fairness measures, recent studies reveal that a
Externí odkaz:
http://arxiv.org/abs/2406.06736
Autor:
Zeng, Donghuo, Legaspi, Roberto S., Sun, Yuewen, Dong, Xinshuai, Ikeda, Kazushi, Spirtes, Peter, Zhang, kun
Customizing persuasive conversations related to the outcome of interest for specific users achieves better persuasion results. However, existing persuasive conversation systems rely on persuasive strategies and encounter challenges in dynamically adj
Externí odkaz:
http://arxiv.org/abs/2404.13792
Gene regulatory network inference (GRNI) is a challenging problem, particularly owing to the presence of zeros in single-cell RNA sequencing data: some are biological zeros representing no gene expression, while some others are technical zeros arisin
Externí odkaz:
http://arxiv.org/abs/2403.15500
Large language models (LLMs) can easily generate biased and discriminative responses. As LLMs tap into consequential decision-making (e.g., hiring and healthcare), it is of crucial importance to develop strategies to mitigate these biases. This paper
Externí odkaz:
http://arxiv.org/abs/2403.08743
Autor:
Dong, Xinshuai, Huang, Biwei, Ng, Ignavier, Song, Xiangchen, Zheng, Yujia, Jin, Songyao, Legaspi, Roberto, Spirtes, Peter, Zhang, Kun
Most existing causal discovery methods rely on the assumption of no latent confounders, limiting their applicability in solving real-life problems. In this paper, we introduce a novel, versatile framework for causal discovery that accommodates the pr
Externí odkaz:
http://arxiv.org/abs/2312.11001
Publikováno v:
The 12th International Conference on Learning Representations (ICLR 2024)
We reveal and address the frequently overlooked yet important issue of disguised procedural unfairness, namely, the potentially inadvertent alterations on the behavior of neutral (i.e., not problematic) aspects of data generating process, and/or the
Externí odkaz:
http://arxiv.org/abs/2311.14688
Autor:
Zheng, Yujia, Huang, Biwei, Chen, Wei, Ramsey, Joseph, Gong, Mingming, Cai, Ruichu, Shimizu, Shohei, Spirtes, Peter, Zhang, Kun
Publikováno v:
Journal of Machine Learning Research 25 (2024)
Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering. We describe $\textit{causal-learn}$, an open-source Python library for causal discovery. This library focuses on brin
Externí odkaz:
http://arxiv.org/abs/2307.16405
Causal discovery aims to recover causal structures generating the observational data. Despite its success in certain problems, in many real-world scenarios the observed variables are not the target variables of interest, but the imperfect measures of
Externí odkaz:
http://arxiv.org/abs/2210.11021