Asia
We introduce a novel framework for analyzing reinforcement learning (RL) in continuous state-action spaces, and use it to prove fast rates of convergence in both
We argue that these properties are satisfied in many continuous state-action Markov decision processes.
On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes
Risk-averse reinforcement learning (RL) seeks to provide a risk-averse policy for high-stakes real-world decision problems. These high-stake domains include autonomous driving (Jin et al., 2019; Sharma et al., 2020), robot collision avoidance (Ahmadi et al., 2021; Hakobyan and Y ang, 2021),
Content-based Unrestricted Adversarial Attack
Unrestricted adversarial attacks typically manipulate the semantic content of an image ( e.g., color or texture) to create adversarial examples that are both effective and photorealistic, demonstrating their ability to deceive human perception
The Star Geometry of Critic-Based Regularizer Learning
Recent works along these lines learn task-dependent regularizers.
On the Powerfulness of Textual Outlier Exposure for Visual OoD Detection (Appendix) A Additional experimental results
Description-level outliers include class-relevant information, but when the class label is omitted, they become very vague and difficult to interpret.
