Recent advances in adversarial training for adversarial robustness
Adversarial training is one of the most effective approaches defending against adversarial
examples for deep learning models. Unlike other defense strategies, adversarial training …
examples for deep learning models. Unlike other defense strategies, adversarial training …
Reinforcement learning for selective key applications in power systems: Recent advances and future challenges
With large-scale integration of renewable generation and distributed energy resources,
modern power systems are confronted with new operational challenges, such as growing …
modern power systems are confronted with new operational challenges, such as growing …
Beta-crown: Efficient bound propagation with per-neuron split constraints for neural network robustness verification
Bound propagation based incomplete neural network verifiers such as CROWN are very
efficient and can significantly accelerate branch-and-bound (BaB) based complete …
efficient and can significantly accelerate branch-and-bound (BaB) based complete …
Robust reinforcement learning: A review of foundations and recent advances
Reinforcement learning (RL) has become a highly successful framework for learning in
Markov decision processes (MDP). Due to the adoption of RL in realistic and complex …
Markov decision processes (MDP). Due to the adoption of RL in realistic and complex …
A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability
In the past few years, significant progress has been made on deep neural networks (DNNs)
in achieving human-level performance on several long-standing tasks. With the broader …
in achieving human-level performance on several long-standing tasks. With the broader …
Resilient machine learning for networked cyber physical systems: A survey for machine learning security to securing machine learning for CPS
Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and
information or cyber worlds. Their deployment in critical infrastructure have demonstrated a …
information or cyber worlds. Their deployment in critical infrastructure have demonstrated a …
Adversarial policies: Attacking deep reinforcement learning
Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial
perturbations to their observations, similar to adversarial examples for classifiers. However …
perturbations to their observations, similar to adversarial examples for classifiers. However …
Robust deep reinforcement learning against adversarial perturbations on state observations
A deep reinforcement learning (DRL) agent observes its states through observations, which
may contain natural measurement errors or adversarial noises. Since the observations …
may contain natural measurement errors or adversarial noises. Since the observations …
Policy gradient method for robust reinforcement learning
This paper develops the first policy gradient method with global optimality guarantee and
complexity analysis for robust reinforcement learning under model mismatch. Robust …
complexity analysis for robust reinforcement learning under model mismatch. Robust …
[HTML][HTML] Leveraging reinforcement learning for dynamic traffic control: A survey and challenges for field implementation
In recent years, the advancement of artificial intelligence techniques has led to significant
interest in reinforcement learning (RL) within the traffic and transportation community …
interest in reinforcement learning (RL) within the traffic and transportation community …