3 citations · 7 across the 7 of their papers we have counts for
12 papers
Efficient first-order predictor-corrector multiple objective optimization for fair misinformation detection
Eric Enouen, Katja Mathesius, Sean Wang +2
Multiple-objective optimization (MOO) aims to simultaneously optimize multiple conflicting objectives and has found important applications in machine learning, such as minimizing c…
Self-learn to Explain Siamese Networks Robustly
Chao Chen, Yifan Shen, Guixiang Ma +4
Learning to compare two objects are essential in applications, such as digital forensics, face recognition, and brain network analysis, especially when labeled data is scarce and i…
Robust Spammer Detection by Nash Reinforcement Learning
Yingtong Dou, Guixiang Ma, Philip S. Yu +1
Online reviews provide product evaluations for customers to make decisions. Unfortunately, the evaluations can be manipulated using fake reviews ("spams") by professional spammers,…
Rigorous Explanation of Inference on Probabilistic Graphical Models
Yifei Liu, Chao Chen, Xi Zhang +1
Probabilistic graphical models, such as Markov random fields (MRF), exploit dependencies among random variables to model a rich family of joint probability distributions. Sophistic…
Scalable Explanation of Inferences on Large Graphs
Chao Chen, Yifei Liu, Xi Zhang +1
Probabilistic inferences distill knowledge from graphs to aid human make important decisions. Due to the inherent uncertainty in the model and the complexity of the knowledge, it i…
Semi-supervised Deep Representation Learning for Multi-View Problems
Vahid Noroozi, Sara Bahaadini, Lei Zheng +3
While neural networks for learning representation of multi-view data have been previously proposed as one of the state-of-the-art multi-view dimension reduction techniques, how to…