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This article proposes a novel variational framework based on tangent transformation for a broad class of probability models characterized by strongly super-Gaussian likelihoods, thereby inducing conjugacy with Gaussian priors over model parameters in an otherwise intractable setup.
This work introduces a variant of the Thompson Sampling algorithm that uses a fractional or $\alpha$-posterior instead of the standard posterior, and identifies general regularity conditions on the prior and reward distributions that enable a regret analysis of $\alpha$-TS without assuming any tractable approximation o...
This work introduces a probabilistic symbolic regression framework that represents mathematical expressions as ensembles of symbolic trees, and develops posterior concentration guarantees when symbolic expressions approximate the underlying relationship arbitrarily well, with a near-parametric rate when an exact finite...
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