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 Work
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
