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This work builds on Tuning without Forgetting (TwF) method to preserve previously learned mappings and shows that TwF provides an indistinguishability guarantee for the learner whenever the set of protected agents contains another sample with the same label.
It is shown that a soft $Q$-learning algorithm converges almost surely to the fixed point of a soft Bellman equation defined by the stationary averages that the behavioral policy and the chosen kernel induce, and what separates the resulting policy from an optimal one.
This work provides what appears to be the first \emph{radically uncoupled} algorithm with sub-exponential convergence guarantees to coarse correlated equilibria in discounted general-sum Markov games without imposing any structural restrictions on the game.
This paper shows that these communication mappings can be derived from a strategic communication game played on a weighted influence network, and predicts that a speaker exaggerates more when their audience is less influenced by them and under strong exaggeration, credible public expression collapses to a binary stance...
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