Dehua Cheng

Dehua works on machine learning problems in the field of personalization, which includes model training, compression, and AutoML. Prior to joining Facebook, Dehua graduated with a PhD in Computer Science at the University of Southern California, with a focus on machine learning.

Dehua's Publications

June 30, 2019

RESEARCH

NLP

Variational Training for Large-Scale Noisy-OR Bayesian Networks

We propose a stochastic variational inference algorithm for training large-scale Bayesian networks, where noisy-OR conditional distributions are used to capture higher-order relationships. One application is to the learning of hierarchical…

Geng Ji, Dehua Cheng, Huazhong Ning, Changhe Yuan, Hanning Zhou, Liang Xiong, Erik B. Sudderth,

June 30, 2019

February 21, 2020

RESEARCH

Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection

Recommendation is a prevalent application of machine learning that affects many users; therefore, it is important for recommender models to be accurate and interpretable. In this work, we propose a method to both interpret and augment the…

Michael Tsang, Dehua Cheng, Hanpeng Liu, Xue Feng, Eric Zhou, Yan Liu,

February 21, 2020