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  1. An implementation of the TKDE paper "Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising"

    Python 20 12

  2. A training and testing framework supporting experiments in CIKM 2016 paper "User Response Learning for Directly Optimizing Campaign Performance in Display Advertising"

    Python 16 6

  3. Deep Recurrent Survival Analysis, an auto-regressive deep model for time-to-event data analysis with censorship handling. An implementation of our AAAI 2019 paper.

    Python 47 22

  4. An implementation of our CIKM 2018 paper "Deep Conversion Attribution with Dual-attention Recurrent Neural Network"

    Python 22 10

  5. Lifelong sequential modeling for user response prediction. A comprehensive evaluation framework for our SIGIR 2019 paper.

    Python 42 7

  6. Deep learning for flexible market price modeling (landscape forecasting) in real-time bidding advertising. An implementation of our KDD 2019 paper.

    Python 14 3

92 contributions in the last year

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August 2019

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