TL;DR
It is shown that the classical Bayesian bootstrap closes this gap in U-calibration, which asks one online probability fore-caster to have low regret for every bounded proper loss, including losses unknown when the forecasts are made.
A single, horizon-free algorithm that satisfies the optimal regret rate for every bounded proper loss and also adapt to every smooth proper loss, covering nondifferentiable losses and changes of the active simplex face.
HT-PAder is proposed, a parameter-free algorithm combining restarted AdaGrad experts over a geometric pool of block lengths with a pathwise meta-algorithm, which requires no moment conditions on meta-losses and provides the first parameter-free minimax universal dynamic regret guarantee.
The aggregation with exponential weights (AEW) estimator is not fully understood in the basic setting of model selection aggregation with squared loss. In particular, whether it is minimax-rate optimal in expectation for large enough fixed temperatures and under random design has been an open problem since its introduc...
A more flexible framework in which a predictive model determines the nominal distribution and a separate model estimates a data-dependent radius is developed, which treats calibration as a practical mechanism for reliable decision making rather than a universal guarantee of improved optimization performance.
This work constructs a proposal that dominates the target by a known constant, generally unavailable for non-Gaussian state space models, yielding independent exact smoothing draws and an unbiased likelihood estimator whose relative variance is at most $1/p-1$ per draw at acceptance probability $p$.
This work revisits the regret lower and upper bounds of ϵ -global DP bandits and proves a tighter regret lower bound involving a novel information-theoretic quantity characterising the hardness of ϵ -global DP in stochastic bandits.
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