Search Results for author: Dapeng Liu

Found 5 papers, 1 papers with code

Cross-Task Knowledge Distillation in Multi-Task Recommendation

no code implementations20 Feb 2022 Chenxiao Yang, Junwei Pan, Xiaofeng Gao, Tingyu Jiang, Dapeng Liu, Guihai Chen

Multi-task learning (MTL) has been widely used in recommender systems, wherein predicting each type of user feedback on items (e. g, click, purchase) are treated as individual tasks and jointly trained with a unified model.

Knowledge Distillation Multi-Task Learning +1

Follow the Prophet: Accurate Online Conversion Rate Prediction in the Face of Delayed Feedback

1 code implementation13 Aug 2021 Haoming Li, Feiyang Pan, Xiang Ao, Zhao Yang, Min Lu, Junwei Pan, Dapeng Liu, Lei Xiao, Qing He

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 minutes to several days.

online learning

Convolutional Normalizing Flows for Deep Gaussian Processes

no code implementations17 Apr 2021 Haibin Yu, Dapeng Liu, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet

Deep Gaussian processes (DGPs), a hierarchical composition of GP models, have successfully boosted the expressive power of their single-layer counterpart.

Gaussian Processes Variational Inference

Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning

no code implementations19 Nov 2019 Pin Wang, Dapeng Liu, Jiayu Chen, Hanhan Li, Ching-Yao Chan

Simulation results show that the augmented AIRL outperforms all the baseline methods, and its performance is comparable with that of the experts on all of the four metrics.

Autonomous Driving Decision Making +2

Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions

no code implementations26 May 2019 Feiyang Pan, Xiang Ao, Pingzhong Tang, Min Lu, Dapeng Liu, Lei Xiao, Qing He

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 known as the issue of miscalibration.

Click-Through Rate Prediction

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