Derives vector-valued self-normalized concentration bounds for light-tailed processes beyond sub-Gaussianity, with applications to online linear regression and linear bandits.
Lastly, if JT (ρ)is obtained from Theorem 5, and ˆσu,t,δ1 is σ(1 + o(1))with high probability, then the same regret bound holds
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.