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Found 7 papers, 3 papers with code
Preference Poisoning Attacks on Reward Model Learning
In addition, we observe that the simpler and more scalable rank-by-distance approaches are often competitive with the best, and on occasion significantly outperform gradient-based methods.
On the Exploitability of Reinforcement Learning with Human Feedback for Large Language Models
Reinforcement Learning with Human Feedback (RLHF) is a methodology designed to align Large Language Models (LLMs) with human preferences, playing an important role in LLMs alignment.
Neural Lyapunov Control for Discrete-Time Systems
However, finding Lyapunov functions for general nonlinear systems is a challenging task.
Certifying Safety in Reinforcement Learning under Adversarial Perturbation Attacks
Our experiments demonstrate both the efficacy of the proposed approach for certifying safety in adversarial environments, and the value of the PSRL framework coupled with adversarial training in improving certified safety while preserving high nominal reward and high-quality predictions of true state.
Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum
Despite considerable advances in deep reinforcement learning, it has been shown to be highly vulnerable to adversarial perturbations to state observations.
Learning Generative Deception Strategies in Combinatorial Masking Games
Next, we present a novel highly scalable approach for approximately solving such games by representing the strategies of both players as neural networks.
CARER: Contextualized Affect Representations for Emotion Recognition
Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs.
