无需复杂调参，用新算法实现高维预测校准。

We study online calibration of multi-dimensional forecasts over an arbitrary convex set $P \subset \mathbb{R}^d$ relative to an arbitrary norm $|\cdot|$. We connect this to external regret minimization for online linear optimization (OLO): if one can guarantee $O(\sqrt{ρT})$ worst-case regret after $T$ rounds when actions are drawn from $P$ and losses from the dual $|\cdot|_*$ unit norm ball, then one can obtain $ε$-calibrated forecasts after $T = \exp(\tilde O(ρ/ε^2))$ rounds. When $P$ is the $d$-dimensional simplex and $|\cdot|$ is the $\ell_1$-norm, the $O(\sqrt{T\log d})$ experts regret bound yields $ε$-calibrated forecasts after $T = \exp(\tilde O(\log d/ε^2)) = d^{\tilde O(1/ε^2)}$ rounds, recovering a recent result of Peng (2025). Interestingly, our algorithm obtains this guarantee without requiring access to any online linear optimization subroutine or knowledge of the optimal rate $ρ$ -- in fact, our algorithm is identical for every setting of $P$ and $|\cdot|$. Instead, we show that the optimal regularizer for the above OLO problem can be used to upper bound the above calibration error by a swap regret, which we then minimize by running the recent TreeSwap algorithm (Dagan et al., 2024; Peng and Rubinstein, 2024) with Follow-The-Leader as a subroutine. The resulting algorithm is highly efficient and plays a distribution over simple averages of past observations in each round. Finally, we prove that any online calibration algorithm that guarantees $εT$ $\ell_1$-calibration error over the $d$-dimensional simplex requires $T \geq \exp(\mathrm{poly}(1/ε))$ (assuming $d \geq \mathrm{poly}(1/ε)$). This strengthens the corresponding $d^{Ω(\log(1/ε))}$ lower bound of Peng (2025), and shows that an exponential dependence on $1/ε$ is necessary.