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Found 4 papers, 1 papers with code
Learning Social Welfare Functions
We focus on two learning tasks; in the first, the input is vectors of utilities of an action (decision or policy) for individuals in a group and their associated social welfare as judged by a policy maker, whereas in the second, the input is pairwise comparisons between the welfares associated with a given pair of utility vectors.
Axioms for AI Alignment from Human Feedback
The problem of learning a reward function is one of preference aggregation that, we argue, largely falls within the scope of social choice theory.
Generative Social Choice
Traditionally, social choice theory has only been applicable to choices among a few predetermined alternatives but not to more complex decisions such as collectively selecting a textual statement.
Expressivity of Shallow and Deep Neural Networks for Polynomial Approximation
This study explores the number of neurons required for a Rectified Linear Unit (ReLU) neural network to approximate multivariate monomials.
