Meaning Representation (AMR) is a semantic graph framework which inadequately represent a number of important semantic features including number, (in)definiteness, quantifiers, and intensional contexts. Several propos...
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The modern digital age demands the skills to be able to assess information. In the current world, access to information and knowledge is abundant from different sources. Indeed, high-quality data are much needed in ed...
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Novelty search is a powerful tool for finding sets of complex objects in complicated, open-ended spaces. Recent empirical analysis on a simplified version of novelty search makes it clear that novelty search happens a...
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AI technologies may serve numerous functions in entire retail sector in value chain, and employ the employees for the job done by using to implement AI operations in the value chain. Because of the increasing degree o...
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Poisonous weeds invade ecosystems and compete with edible forage grassland, changing the population spatial distribution. In this paper, a discrete population competition model for the interaction between poisonous we...
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Supported by a state grant, our team of researchers (consisting of both computerscience faculty and Teacher Education faculty) is offering a series of Professional Development sessions to K-8 teachers. These Professi...
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One of the significant challenges in treatment effect estimation is collider bias, a specific form of sample selection bias induced by the common causes of both the treatment and outcome. Identifying treatment effects...
One of the significant challenges in treatment effect estimation is collider bias, a specific form of sample selection bias induced by the common causes of both the treatment and outcome. Identifying treatment effects under collider bias requires well-defined shadow variables in observational data, which are assumed to be related to the outcome and independent of the sample selection mechanism, conditional on the other observed variables. However, finding a valid shadow variable is not an easy task in real-world scenarios and requires domain-specific knowledge from experts. Therefore, in this paper, we propose a novel method that can automatically learn shadow-variable representations from observational data without prior knowledge. To ensure the learned representations satisfy the assumptions of the shadow variable, we introduce a tester to perform hypothesis testing in the representation learning process. We iteratively generate representations and test whether they satisfy the shadow-variable assumptions until they pass the test. With the help of the learned shadow-variable representations, we propose a novel treatment effect estimator to address collider bias. Experiments show that the proposed methods outperform existing treatment effect estimation methods under collider bias and prove their potential application value.
The 2024 Workshop on AI for Digital Twins and Cyber-Physical Applications (AI4DT&CP 2024) is at its second edition, held in conjunction with IJCAI 2024: the 33rd International Joint Conference on Artificial Intell...
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Systematic reviews are time-consuming endeavors. Historically speaking, knowledgeable humans have had to screen and extract data from studies before it can be analyzed. However, large language models (LLMs) hold promi...
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Jitter in multimedia traffic is mainly introduced by variations in network characteristics. Ifjitter is so significant in the application that is receiving, this can result in a degraded performance in real-time multi...
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