Language models have been shown to be very effective in predicting brain recordings of subjects experiencing complex language stimuli.

Inthispaper,we are concerned with getting well-calibrated predictions in regression tasks. We propose the calibrated regression method using the maximum mean discrepancy by minimizing the kernel embedding measure.

In practice, building f and g requires the computation for wtiwtj for all i,j. B.2 Classification For the classification task with the logistic regression model, we modify the formula of logistic regression in teaching objectives to make it convenient for derivation. It also indicates that with probability at least p1, the LST teacher can achieve exponential teachability in the iteration t. In order to achieve exponential teachiability in T iterations, the sufficient condition in Eq. (22) must be satisfied in all T iterations. Then, we use a pre-trained DenseNet [65] shown in [53] to generate 1024 dim features and the confidencescoreforeachimage.

Contextual bandits are a core technology for personalized mobile health interventions, where decision-making requires adapting to complex, non-linear user behaviors. While Thompson Sampling (TS) is a preferred strategy for these problems, its performance hinges on the quality of the underlying reward model. Standard linear models suffer from high bias, while neural network approaches are often brittle and difficult to tune in online settings. Conversely, tree ensembles dominate tabular data prediction but typically rely on heuristic uncertainty quantification, lacking a principled probabilistic basis for TS. We propose Bayesian Forest Thompson Sampling (BFTS), the first contextual bandit algorithm to integrate Bayesian Additive Regression Trees (BART), a fully probabilistic sum-of-trees model, directly into the exploration loop. We prove that BFTS is theoretically sound, deriving an information-theoretic Bayesian regret bound of $\tilde{O}(\sqrt{T})$. As a complementary result, we establish frequentist minimax optimality for a "feel-good" variant, confirming the structural suitability of BART priors for non-parametric bandits. Empirically, BFTS achieves state-of-the-art regret on tabular benchmarks with near-nominal uncertainty calibration. Furthermore, in an offline policy evaluation on the Drink Less micro-randomized trial, BFTS improves engagement rates by over 30% compared to the deployed policy, demonstrating its practical effectiveness for behavioral interventions.