曾國師教授(國立中央大學 數學系)

演講時間：112年3月14日下午1點
演講地點：採線上演講，連結為 https://meet.google.com/voy-xmmx-nfv

講題：Transfer Learning of Coverage Functions via Invariant Properties in the Fourier Domain

摘要： The robotics community has been paying more attention to coverage functions
due to their variant applications (e.g., spatial search and mapping, etc.). Due to their submodularity,
greedy algorithms can find solutions with theoretical guarantees for maximizing coverage problems
even if these problems are NP-hard. However, learning coverage functions is still a challenging problem
since the number of function outcome for N sets is 2^N. Moreover, transfer learning of coverage functions
is unexplored. This research focuses on the transfer learning of coverage functions via utilizing the invariant
properties in the Fourier domain. The proposed algorithms based on these properties can construct Fourier
support for learning coverage functions. Experiments conducted with these algorithms show that the robot
can learn the coverage functions using less samples than the prior learning approaches in different environments.
Experiments also show that the lossless compression rate of the proposed algorithms is up to 40 billion.