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This paper presents the first polynomial-time algorithm for learning with exact group invariances that applies uniformly to finite and infinite groups, and shows that exact symmetries can be identified from data and exploited for learning in polynomial time.
This work studies adaptive symmetry discovery for dynamical system identification and addresses how a system can be identified from a single trajectory when it is equivariant with respect to an unknown symmetry group.
GCC formulates coded computing through a natural end-to-end mean-squared error loss that directly measures the discrepancy between the desired computations and their recovered estimates, and enables theoretical performance guarantees for GCC under two complementary straggler regimes.
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