64 citations · 149 across the 8 of their papers we have counts for
8 papers
NASRec: Weight Sharing Neural Architecture Search for Recommender Systems
Tunhou Zhang, Dehua Cheng, Yuchen He +7
The rise of deep neural networks offers new opportunities in optimizing recommender systems. However, optimizing recommender systems using deep neural networks requires delicate ar…
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction
Qingquan Song, Dehua Cheng, Hanning Zhou +3
Click-Through Rate (CTR) prediction is one of the most important machine learning tasks in recommender systems, driving personalized experience for billions of consumers. Neural ar…
Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection
Michael Tsang, Dehua Cheng, Hanpeng Liu +3
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 w…
Detecting Statistical Interactions from Neural Network Weights
Michael Tsang, Dehua Cheng, Yan Liu
Interpreting neural networks is a crucial and challenging task in machine learning. In this paper, we develop a novel framework for detecting statistical interactions captured by a…
On Bochner's and Polya's Characterizations of Positive-Definite Kernels and the Respective Random Feature Maps
Jie Chen, Dehua Cheng, Yan Liu
Positive-definite kernel functions are fundamental elements of kernel methods and Gaussian processes. A well-known construction of such functions comes from Bochner's characterizat…
Spectral Sparsification of Random-Walk Matrix Polynomials
Dehua Cheng, Yu Cheng, Yan Liu +2
We consider a fundamental algorithmic question in spectral graph theory: Compute a spectral sparsifier of random-walk matrix-polynomial whe…
Scalable Parallel Factorizations of SDD Matrices and Efficient Sampling for Gaussian Graphical Models
Dehua Cheng, Yu Cheng, Yan Liu +2
Motivated by a sampling problem basic to computational statistical inference, we develop a nearly optimal algorithm for a fundamental problem in spectral graph theory and numerical…
Model Selection for Topic Models via Spectral Decomposition
Dehua Cheng, Xinran He, Yan Liu
Topic models have achieved significant successes in analyzing large-scale text corpus. In practical applications, we are always confronted with the challenge of model selection, i.…