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Found 6 papers, 2 papers with code
Nonlinear Multi-objective Reinforcement Learning with Provable Guarantees
We first state a distinct reward-aware version of value iteration that calculates a non-stationary policy that is approximately optimal for a given model of the environment.
Do Not Harm Protected Groups in Debiasing Language Representation Models
Language Representation Models (LRMs) trained with real-world data may capture and exacerbate undesired bias and cause unfair treatment of people in various demographic groups.
Welfare and Fairness in Multi-objective Reinforcement Learning
We study fair multi-objective reinforcement learning in which an agent must learn a policy that simultaneously achieves high reward on multiple dimensions of a vector-valued reward.
Proportionally Fair Clustering
We extend the fair machine learning literature by considering the problem of proportional centroid clustering in a metric context.
Random Dictators with a Random Referee: Constant Sample Complexity Mechanisms for Social Choice
Constant sample complexity means that the mechanism (potentially randomized) only uses a constant number of ordinal queries regardless of the number of voters and alternatives.
Sequential Deliberation for Social Choice
In large scale collective decision making, social choice is a normative study of how one ought to design a protocol for reaching consensus.
Computer Science and Game Theory Multiagent Systems
