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Heterogeneous Causal Learning for Effectiveness Optimization in User Marketing
By attracting users with rewards, marketing methods are effective to boost user activity in the desired products.
Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects
We propose to formulate the effectiveness of treatment as a parametrizable model, expanding to a multitude of treatment intensities and complexities through the continuous policy treatment function, and the likelihood of matching.
Generic Object Detection With Dense Neural Patterns and Regionlets
This paper addresses the challenge of establishing a bridge between deep convolutional neural networks and conventional object detection frameworks for accurate and efficient generic object detection.
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