arXiv Machine Learning By Omar Abbadi, Rida Laraki, Panayotis Mertikopoulos

What preferences can - and cannot - predict in multi-agent online learning

Read the original on arXiv Machine Learning →

arXiv:2608. 13810v1 Announce Type: cross Abstract: We examine the interplay between ordinal, preference-based solution concepts in games and the long-run behavior of game dynamics, asking in particular to what extent the combinatorial data of a game -- its preference graph -- determine the outcomes of no-regret learning dynamics -- such as follow-the-regularized-leader (FTRL).

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arXiv Machine Learning
Sep 22

Learning in Structured Stackelberg Games

arXiv:2504.09006v5 Announce Type: replace-cross Abstract: We initiate the study of structured Stackelberg games, a novel form of strategic interaction between a leader and a follower where contextual...

By Maria-Florina Balcan, Kiriaki Fragkia, Keegan Harris