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Rate-Optimal Online Convex Optimization in Adaptive Linear Control
Neural Information Processing Systems
This general framework encapsulates numerous variations of learning in linear control that have been studied extensively in the literature. When the system parameters are known ahead of time and the costs are fixed and known (convex) quadratics, this amounts to the classical "planning"
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FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning
Neural Information Processing Systems
Euclidean metric is suboptimal and that feature distributions are heterogeneous.
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