Zobrazeno 1 - 10
of 160
pro vyhledávání: '"Cheng, Debo"'
Intervention intuition is often used in model explanation where the intervention effect of a feature on the outcome is quantified by the difference of a model prediction when the feature value is changed from the current value to the baseline value.
Externí odkaz:
http://arxiv.org/abs/2410.15648
Recommender systems are extensively utilised across various areas to predict user preferences for personalised experiences and enhanced user engagement and satisfaction. Traditional recommender systems, however, are complicated by confounding bias, p
Externí odkaz:
http://arxiv.org/abs/2410.12451
In recommender systems, various latent confounding factors (e.g., user social environment and item public attractiveness) can affect user behavior, item exposure, and feedback in distinct ways. These factors may directly or indirectly impact user fee
Externí odkaz:
http://arxiv.org/abs/2410.12366
With the widespread use of Graph Neural Networks (GNNs) for representation learning from network data, the fairness of GNN models has raised great attention lately. Fair GNNs aim to ensure that node representations can be accurately classified, but n
Externí odkaz:
http://arxiv.org/abs/2410.11493
The rapid development of Large Language Models (LLMs) creates new opportunities for recommender systems, especially by exploiting the side information (e.g., descriptions and analyses of items) generated by these models. However, aligning this side i
Externí odkaz:
http://arxiv.org/abs/2409.20052
Autor:
Gao, Wentao, Xu, Ziqi, Li, Jiuyong, Liu, Lin, Liu, Jixue, Le, Thuc Duy, Cheng, Debo, Zhao, Yanchang, Chen, Yun
As the growing demand for long sequence time-series forecasting in real-world applications, such as electricity consumption planning, the significance of time series forecasting becomes increasingly crucial across various domains. This is highlighted
Externí odkaz:
http://arxiv.org/abs/2409.19871
Estimating causal effects is crucial for decision-makers in many applications, but it is particularly challenging with observational network data due to peer interactions. Many algorithms have been proposed to estimate causal effects involving networ
Externí odkaz:
http://arxiv.org/abs/2408.11492
Autor:
Gao, Wentao, Li, Jiuyong, Cheng, Debo, Liu, Lin, Liu, Jixue, Le, Thuc Duy, Du, Xiaojing, Chen, Xiongren, Zhao, Yanchang, Chen, Yun
Global Climate Models (GCMs) are crucial for predicting future climate changes by simulating the Earth systems. However, GCM outputs exhibit systematic biases due to model uncertainties, parameterization simplifications, and inadequate representation
Externí odkaz:
http://arxiv.org/abs/2408.12063
Graph unlearning technology has become increasingly important since the advent of the `right to be forgotten' and the growing concerns about the privacy and security of artificial intelligence. Graph unlearning aims to quickly eliminate the effects o
Externí odkaz:
http://arxiv.org/abs/2408.09705
In recommender systems, latent variables can cause user-item interaction data to deviate from true user preferences. This biased data is then used to train recommendation models, further amplifying the bias and ultimately compromising both recommenda
Externí odkaz:
http://arxiv.org/abs/2408.09651