N-FPN inferences are computed by repeated application of the operator until a fixed point condition is satisfied. Thus, by construction, N-FPNs are implicit ...
Jun 4, 2021 · This work introduces Nash Fixed Point Networks (N-FPNs), a class of implicit-depth neural networks that output Nash equilibria of contextual games.
This work introduces Nash Fixed Point Networks (N-FPNs), a class of implicit neural networks that learn to predict the equilibria given only the context, ...
Jun 2, 2021 · We introduce Nash Fixed Point Networks (N-FPNs), a class of neural networks that naturally output equilibria.
Jul 20, 2021 · I Study games depending on external parameter d. I Wish to predict outcome of game knowing only d. I Reduce game to variational inequality then ...
This repo provides the code for the paper Learning to Predict Equilibria via Fixed Point Networks (preprint available here), which was joint work by Howard ...
Jun 2, 2021 · This work introduces Nash Fixed Point Networks (N-FPNs), a class of implicit-depth neural networks that output Nash equilibria of contextual ...
We study the problem of predicting the outcome of a contextual game, given only the context, and assuming that the player's cost functions are unknown.
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Jul 22, 2024 · N-FPN inferences are computed by repeated application of the operator until a fixed point condition is satisfied. Thus, by construction, N-FPNs ...
Learn to predict equilibria via Fixed Point Networks. Published in (under review), 2021. We apply the FPN technology developed in an earlier work to the ...