Derives vector-valued self-normalized concentration bounds for light-tailed processes beyond sub-Gaussianity, with applications to online linear regression and linear bandits.
A.3 Proof of Theorem 2 It follows from Proposition 1 that (ρI+V t)−1/2Mt ≤ Pt i=1 ei(λ) + log 2 δ λ
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Vector-valued self-normalized concentration inequalities beyond sub-Gaussianity
Derives vector-valued self-normalized concentration bounds for light-tailed processes beyond sub-Gaussianity, with applications to online linear regression and linear bandits.