Bayesian Inference
Bayes' Theorem allows a program to infer the probabilities of likely causes from the probabilities of their effects, when what it is given are the probabilities of effects, given the causes.
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Measuring Goal-Directedness
Neural Information Processing Systems
In order to build more useful AI systems, a natural inclination is to try to make them more agentic . But while agents built from language models are touted as the next big advance [Wang et al., 2024],
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Sample Efficient Bayesian Learning of Causal Graphs from Interventions
Neural Information Processing Systems
In general, interventional data, i.e., data collected after a perturbation in
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