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Found 7 papers, 6 papers with code
Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive Learning
Discovering achievements with a hierarchical structure in procedurally generated environments presents a significant challenge.
Rethinking Value Function Learning for Generalization in Reinforcement Learning
Our work focuses on training RL agents on multiple visually diverse environments to improve observational generalization performance.
Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization
We focus on the problem of adversarial attacks against models on discrete sequential data in the black-box setting where the attacker aims to craft adversarial examples with limited query access to the victim model.
Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks
However, the vulnerability of neural networks against adversarial attacks poses a serious threat to the people affected by these systems.
Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble
However, prior methods typically require accurate estimation of the behavior policy or sampling from OOD data points, which themselves can be a non-trivial problem.
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Exploiting Safe Spots in Neural Networks for Preemptive Robustness and Out-of-Distribution Detection
Recent advances on adversarial defense mainly focus on improving the classifier’s robustness against adversarially perturbed inputs.
Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization
Solving for adversarial examples with projected gradient descent has been demonstrated to be highly effective in fooling the neural network based classifiers.
