3 papers
cs.LG2024
Causal Discovery-Driven Change Point Detection in Time Series
Shanyun Gao, Raghavendra Addanki, Tong Yu +2
Change point detection in time series aims to identify moments when the probability distribution of time series changes. It is widely applied in many areas, such as human activity…
stat.ML2024
Continuous Treatment Effects with Surrogate Outcomes
Zhenghao Zeng, David Arbour, Avi Feller +4
In many real-world causal inference applications, the primary outcomes (labels) are often partially missing, especially if they are expensive or difficult to collect. If the missin…
cs.CL2024
Augment before You Try: Knowledge-Enhanced Table Question Answering via Table Expansion
Yujian Liu, Jiabao Ji, Tong Yu +6
Table question answering is a popular task that assesses a model's ability to understand and interact with structured data. However, the given table often does not contain sufficie…