Publication year
2021Publisher
[S.l.] : [S.n.]
In
ICML 2021: The Thirty-eighth International Conference on Machine Learning, pp. 1-11Annotation
ICML 2021: The Thirty-eighth International Conference on Machine Learning (18-24 July, 2021)
Publication type
Article in monograph or in proceedings

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Organization
SW OZ DCC AI
Languages used
English (eng)
Book title
ICML 2021: The Thirty-eighth International Conference on Machine Learning
Page start
p. 1
Page end
p. 11
Subject
Cognitive artificial intelligenceAbstract
Ridge Rider (RR) is an algorithm for finding diverse solutions to optimization problems by following eigenvectors of the Hessian ("ridges"). RR is designed for conservative gradient systems (i.e. settings involving a single loss function), where it branches at saddles - the only relevant bifurcation points. We generalize this idea to non-conservative, multi-agent gradient systems by identifying new types of bifurcation points and proposing a method to follow eigenvectors with complex eigenvalues. We give theoretical motivation for our method - denoted Game Ridge Rider (GRR) - by leveraging machinery from the field of dynamical systems. Finally, we empirically motivate our method by constructing novel toy problems where we can visualize new phenomena and by finding diverse solutions in the iterated prisoners' dilemma, where existing methods often converge to a single solution mode.
This item appears in the following Collection(s)
- Academic publications [227437]
- Faculty of Social Sciences [28417]
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