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Doing the right thing for the right reason: Evaluating artificial moral cognition by probing cost insensitivity
Is it possible to evaluate the moral cognition of complex artificial agents?
Beyond Bayes-optimality: meta-learning what you know you don't know
This is in contrast to risk-sensitive agents, which additionally exploit the higher-order moments of the return, and ambiguity-sensitive agents, which act differently when recognizing situations in which they lack knowledge.
Your Policy Regularizer is Secretly an Adversary
Policy regularization methods such as maximum entropy regularization are widely used in reinforcement learning to improve the robustness of a learned policy.
Model-Free Risk-Sensitive Reinforcement Learning
Since the Gaussian free energy is known to be a certainty-equivalent sensitive to the mean and the variance, the learning rule has applications in risk-sensitive decision-making.
Shaking the foundations: delusions in sequence models for interaction and control
The recent phenomenal success of language models has reinvigorated machine learning research, and large sequence models such as transformers are being applied to a variety of domains.
Stochastic Approximation of Gaussian Free Energy for Risk-Sensitive Reinforcement Learning
Since the Gaussian free energy is known to be a certainty-equivalent sensitive to the mean and the variance, the learning rule has applications in risk-sensitive decision-making.
Causal Analysis of Agent Behavior for AI Safety
As machine learning systems become more powerful they also become increasingly unpredictable and opaque.
Human-interpretable model explainability on high-dimensional data
The importance of explainability in machine learning continues to grow, as both neural-network architectures and the data they model become increasingly complex.
