  Certifiable, adaptive uncertainty estimates for unknown quantities are an
essential ingredient of sequential decision-making algorithms. Standard
approaches rely on problem-dependent concentration results and are limited to a
specific combination of parameterization, noise family, and estimator. In this
paper, we revisit the likelihood-based inference principle and propose to use
likelihood ratios to construct any-time valid confidence sequences without
requiring specialized treatment in each application scenario. Our method is
especially suitable for problems with well-specified likelihoods, and the
resulting sets always maintain the prescribed coverage in a model-agnostic
manner. The size of the sets depends on a choice of estimator sequence in the
likelihood ratio. We discuss how to provably choose the best sequence of
estimators and shed light on connections to online convex optimization with
algorithms such as Follow-the-Regularized-Leader. To counteract the initially
large bias of the estimators, we propose a reweighting scheme that also opens
up deployment in non-parametric settings such as RKHS function classes. We
provide a non-asymptotic analysis of the likelihood ratio confidence sets size
for generalized linear models, using insights from convex duality and online
learning. We showcase the practical strength of our method on generalized
linear bandit problems, survival analysis, and bandits with various additive
noise distributions.
