Australia'deeply frustrated' over Laos methanol poisoning charges The Australian government has said it is deeply frustrated and bitterly disappointed that Laos is not pursuing more serious charges in relation to the deaths of six backpackers who died after drinking methanol-laced alcohol in 2024. Australians Bianca Jones and Holly Morton-Bowles, both 19, were among six people who died at a hostel in Vang Vieng in November 2024. The statement comes after reports in Australian media that Laos would press charges that collectively carry penalties of up to one year in jail and a fine of A$1600 ($1100; £829) against those allegedly responsible. Australian Foreign Ministry Penny Wong said they have summoned Laos' ambassador to Canberra. Laos has yet to confirm any charges but the BBC understands authorities there will hold a press conference later on Friday.

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We also show that when stochastic mixability does not hold in a certain sense (described in Section 5), then the risk minimizer is not unique in a bad way.

Then, we intend to calculate the constraint part. The algorithm for Licence method for single-target interventiion scenario is shown in Algorithm 1. The details of experimental baselines are demonstrated as follows. AIT [11] is an active learning method that utilize f-score to select intervention queries. REAL fidelity means the model always choose the highest fidelity to conduct experiments.

For example, to study the causal relations between the drugs and diseases, one can conduct clinical tests via selectively administering the medicines to the patients.

V ov95, CBL06] and online convex optimization [Haz16, Ora19] have been developed. Until the labels of all examples of X have been predicted: The learning algorithm picks a point x X and makes a prediction z R about its label.

A key challenge in probabilistic regression is ensuring that predictive distributions accurately reflect true empirical uncertainty. Minimizing overall prediction error often encourages models to prioritize informativeness over calibration, producing narrow but overconfident predictions. However, in safety-critical settings, trustworthy uncertainty estimates are often more valuable than narrow intervals. Realizing the problem, several recent works have focused on post-hoc corrections; however, existing methods either rely on weak notions of calibration (such as PIT uniformity) or impose restrictive parametric assumptions on the nature of the error. To address these limitations, we propose a novel nonparametric re-calibration algorithm based on conditional kernel mean embeddings, capable of correcting calibration error without restrictive modeling assumptions. For efficient inference with real-valued targets, we introduce a novel characteristic kernel over distributions that can be evaluated in $\mathcal{O}(n \log n)$ time for empirical distributions of size $n$. We demonstrate that our method consistently outperforms prior re-calibration approaches across a diverse set of regression benchmarks and model classes.