Data Valuation using Reinforcement Learning

Understand

Quantifying the value of data is a fundamental problem in machine learning.

• Data valuation has multiple important use cases: (1) building insights about the learning task, (2) domain adaptation, (3) corrupted sample discovery, and (4) robust learning.
• To adaptively learn data values jointly with the target task predictor model, we propose a meta learning framework which we name Data Valuation using Reinforcement Learning (DVRL).
• We employ a data value estimator (modeled by a deep neural network) to learn how likely each datum is used in training of the predictor model.