Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Meng, Chuizheng"'
Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing cov
Externí odkaz:
http://arxiv.org/abs/2408.08815
Estimation of temporal counterfactual outcomes from observed history is crucial for decision-making in many domains such as healthcare and e-commerce, particularly when randomized controlled trials (RCTs) suffer from high cost or impracticality. For
Externí odkaz:
http://arxiv.org/abs/2311.00886
Causal analysis for time series data, in particular estimating individualized treatment effect (ITE), is a key task in many real-world applications, such as finance, retail, healthcare, etc. Real-world time series can include large-scale, irregular,
Externí odkaz:
http://arxiv.org/abs/2303.02320
Physics-informed machine learning (PIML), referring to the combination of prior knowledge of physics, which is the high level abstraction of natural phenomenons and human behaviours in the long history, with data-driven machine learning models, has e
Externí odkaz:
http://arxiv.org/abs/2203.16797
Vast amount of data generated from networks of sensors, wearables, and the Internet of Things (IoT) devices underscores the need for advanced modeling techniques that leverage the spatio-temporal structure of decentralized data due to the need for ed
Externí odkaz:
http://arxiv.org/abs/2106.05223
The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications. However, due to the nature of such deep black-boxed models, concerns about interpretability, fai
Externí odkaz:
http://arxiv.org/abs/2102.06761
Publikováno v:
Journal of Healthcare Informatics Research (2021)
Epidemic spread in a population is traditionally modeled via compartmentalized models which represent the free evolution of disease in absence of any intervention policies. In addition, these models assume full observability of disease cases and do n
Externí odkaz:
http://arxiv.org/abs/2009.01894
Modeling the dynamics of real-world physical systems is critical for spatiotemporal prediction tasks, but challenging when data is limited. The scarcity of real-world data and the difficulty in reproducing the data distribution hinder directly applyi
Externí odkaz:
http://arxiv.org/abs/2006.08831
The ongoing Coronavirus (COVID-19) pandemic highlights the inter-connectedness of our present-day globalized world. With social distancing policies in place, virtual communication has become an important source of (mis)information. As increasing numb
Externí odkaz:
http://arxiv.org/abs/2003.12309
Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications. However, few works exist w
Externí odkaz:
http://arxiv.org/abs/1710.08531