Jinfeng Yi

Enhancing the Robustness of Neural Collaborative Filtering Systems Under Malicious Attacks

2018 · IEEE Transactions on Multimedia

Recommendation systems have become ubiquitous in online shopping in recent decades due to their power in reducing excessive choices of customers and industries. Recent collaborative filtering methods based on the deep neural network are studied …

On the Convergence and Robustness of Adversarial Training

2021 · arXiv (Cornell University)

Improving the robustness of deep neural networks (DNNs) to adversarial examples is an important yet challenging problem for secure deep learning. Across existing defense techniques, adversarial training with Projected Gradient Decent (PGD) is amongst the …

Diverse Few-Shot Text Classification with Multiple Metrics

2018

Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar, Yu Cheng, Gerald Tesauro, Haoyu Wang, Bowen Zhou. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human …

Spanning Attack: Reinforce Black-box Attacks with Unlabeled Data

2020 · arXiv (Cornell University)

Adversarial black-box attacks aim to craft adversarial perturbations by querying input-output pairs of machine learning models. They are widely used to evaluate the robustness of pre-trained models. However, black-box attacks often suffer from the issue …

Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System

2021

The general aim of the recommender system is to provide personalized suggestions to users, which is opposed to suggesting popular items. However, the normal training paradigm, i.e., fitting a recommender model to recover the user …