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.
June 30, 2019
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 hierarchicalā¦
Geng Ji, Dehua Cheng, Huazhong Ning, Changhe Yuan, Hanning Zhou, Liang Xiong, Erik B. Sudderth,
June 30, 2019
February 21, 2020
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 theā¦
Michael Tsang, Dehua Cheng, Hanpeng Liu, Xue Feng, Eric Zhou, Yan Liu,
February 21, 2020