29 citations · 41 across the 4 of their papers we have counts for
8 papers
Follow the Prophet: Accurate Online Conversion Rate Prediction in the Face of Delayed Feedback
Haoming Li, Feiyang Pan, Xiang Ao +6
The delayed feedback problem is one of the imperative challenges in online advertising, which is caused by the highly diversified feedback delay of a conversion varying from a few…
GuideBoot: Guided Bootstrap for Deep Contextual Bandits
Feiyang Pan, Haoming Li, Xiang Ao +4
The exploration/exploitation (E&E) dilemma lies at the core of interactive systems such as online advertising, for which contextual bandit algorithms have been proposed. Bayesian a…
Trust the Model When It Is Confident: Masked Model-based Actor-Critic
Feiyang Pan, Jia He, Dandan Tu +1
It is a popular belief that model-based Reinforcement Learning (RL) is more sample efficient than model-free RL, but in practice, it is not always true due to overweighed model err…
GoChat: Goal-oriented Chatbots with Hierarchical Reinforcement Learning
Jianfeng Liu, Feiyang Pan, Ling Luo
A chatbot that converses like a human should be goal-oriented (i.e., be purposeful in conversation), which is beyond language generation. However, existing dialogue systems often h…
Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions
Feiyang Pan, Xiang Ao, Pingzhong Tang +4
It is often observed that the probabilistic predictions given by a machine learning model can disagree with averaged actual outcomes on specific subsets of data, which is also know…
Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings
Feiyang Pan, Shuokai Li, Xiang Ao +2
Click-through rate (CTR) prediction has been one of the most central problems in computational advertising. Lately, embedding techniques that produce low-dimensional representation…