29 citations · 75 across the 35 of their papers we have counts for
6 papers · 1 filter
LDL: A Defense for Label-Based Membership Inference Attacks
Arezoo Rajabi, Dinuka Sahabandu, Luyao Niu +2
The data used to train deep neural network (DNN) models in applications such as healthcare and finance typically contain sensitive information. A DNN model may suffer from overfitt…
Game of Trojans: A Submodular Byzantine Approach
Dinuka Sahabandu, Arezoo Rajabi, Luyao Niu +3
Machine learning models in the wild have been shown to be vulnerable to Trojan attacks during training. Although many detection mechanisms have been proposed, strong adaptive attac…
Trojan Horse Training for Breaking Defenses against Backdoor Attacks in Deep Learning
Arezoo Rajabi, Bhaskar Ramasubramanian, Radha Poovendran
Machine learning (ML) models that use deep neural networks are vulnerable to backdoor attacks. Such attacks involve the insertion of a (hidden) trigger by an adversary. As a conseq…
Privacy-Preserving Reinforcement Learning Beyond Expectation
Arezoo Rajabi, Bhaskar Ramasubramanian, Abdullah Al Maruf +1
Cyber and cyber-physical systems equipped with machine learning algorithms such as autonomous cars share environments with humans. In such a setting, it is important to align syste…
Shaping Advice in Deep Reinforcement Learning
Baicen Xiao, Bhaskar Ramasubramanian, Radha Poovendran
Reinforcement learning involves agents interacting with an environment to complete tasks. When rewards provided by the environment are sparse, agents may not receive immediate feed…
Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement Learning
Baicen Xiao, Bhaskar Ramasubramanian, Radha Poovendran
This paper considers multi-agent reinforcement learning (MARL) tasks where agents receive a shared global reward at the end of an episode. The delayed nature of this reward affects…