Abstract
We study several learning tasks (e.g.,regression or clustering) with time series data. These problems have gainedimportance across many fields including biology, medicine, and economics due tothe proliferation of sensors facilitating real-time measurement and rapid dropin storage costs. We will introduce how to construct coresets, which is a smallsubset of the data allowing for fast approximate inference, for the maximumlikelihood objective for these problems, and discuss the differences fromstatic data. We empirically assess the performance of our coreset withsynthetic data and real-world data.
Time
2021-06-18 15:00-15:30
Speaker
Lingxiao Huang, Huawei TCS Lab Room
Guangdong Hotel Shanghai
