97 citations · 203 across the 11 of their papers we have counts for
11 papers
Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks
Chen Ma, Xiangyu Guo, Li Chen +2
One major problem in black-box adversarial attacks is the high query complexity in the hard-label attack setting, where only the top-1 predicted label is available. In this paper,…
Knowledge-Enhanced Top-K Recommendation in Poincaré Ball
Chen Ma, Liheng Ma, Yingxue Zhang +3
Personalized recommender systems are increasingly important as more content and services become available and users struggle to identify what might interest them. Thanks to the abi…
Switching Transferable Gradient Directions for Query-Efficient Black-Box Adversarial Attacks
Chen Ma, Shuyu Cheng, Li Chen +2
We propose a simple and highly query-efficient black-box adversarial attack named SWITCH, which has a state-of-the-art performance in the score-based setting. SWITCH features a hig…
Simulating Unknown Target Models for Query-Efficient Black-box Attacks
Chen Ma, Li Chen, Jun-Hai Yong
Many adversarial attacks have been proposed to investigate the security issues of deep neural networks. In the black-box setting, current model stealing attacks train a substitute…
Universal Successor Features for Transfer Reinforcement Learning
Chen Ma, Dylan R. Ashley, Junfeng Wen +1
Transfer in Reinforcement Learning (RL) refers to the idea of applying knowledge gained from previous tasks to solving related tasks. Learning a universal value function (Schaul et…
Learning to Combat Compounding-Error in Model-Based Reinforcement Learning
Chenjun Xiao, Yifan Wu, Chen Ma +2
Despite its potential to improve sample complexity versus model-free approaches, model-based reinforcement learning can fail catastrophically if the model is inaccurate. An algorit…