Whilesomerecent works propose to distill an ensemble model into a single model to reduce such costs,thereisstillaperformance gapbetween theensemble anddistilledmodels.

The flexibility of these methods is often limited bytherequirement thatthecutsbeaxisaligned.

Acommon approach toquantify epistemic uncertainty, usable across a wide class of prediction models, is to train amodel ensemble. In a naïve implementation, the ensemble approach has high computational cost and high memory demand. This challenges in particular modern deep learning, where evenasingle deep network isalready demanding interms ofcompute and memory,and has givenrise toanumber ofattempts toemulate the model ensemble without actually instantiating separate ensemble members.

These concerns are more relevant than ever, since the architecture size, amount of training data, and computing power used by state-of-the-art models continue to increase.

And model misspecification has not been considered as an unresolved issue [36,38,53,54].

Large Language Models (LLMs) hold promise in addressing complex medical problems. However, while most prior studies focus on improving accuracy and reasoning abilities, a significant bottleneck in developing effective healthcare agents lies in the readability of LLM-generated responses, specifically, their ability to answer public health problems clearly and simply to people without medical backgrounds. In this work, we introduce RephQA, a benchmark for evaluating the readability of LLMs in public health question answering (QA). It contains 533 expert-reviewed QA pairs from 27 sources across 13 topics, and includes a proxy multiple-choice task to assess informativeness, along with two readability metrics: Flesch-Kincaid grade level and professional score. Evaluation of 25 LLMs reveals that most fail to meet readability standards, highlighting a gap between reasoning and effective communication. To address this, we explore four readability-enhancing strategies-standard prompting, chain-of-thought prompting, Group Relative Policy Optimization (GRPO), and a token-adapted variant. Token-adapted GRPO achieves the best results, advancing the development of more practical and user-friendly public health agents. These results represent a step toward building more practical agents for public health.

This paper introduces a novel and scalable framework for uncertainty estimation and separation with applications in data driven modeling in science and engineering tasks where reliable uncertainty quantification is critical. Leveraging an ensemble of quantile regression (E-QR) models, our approach enhances aleatoric uncertainty estimation while preserving the quality of epistemic uncertainty, surpassing competing methods, such as Deep Ensembles (DE) and Monte Carlo (MC) dropout. To address challenges in separating uncertainty types, we propose an algorithm that iteratively improves separation through progressive sampling in regions of high uncertainty. Our framework is scalable to large datasets and demonstrates superior performance on synthetic benchmarks, offering a robust tool for uncertainty quantification in data-driven applications.

As a result, the development network, which generalizes the unnormalized of methods to quantify neural network uncertainty is an Gaussian densities to have modes of highdimensional increasingly important subject in deep learning research submanifolds instead of just discrete (Amodei et al., 2016). In particular, neural networks tend to points. Fitting the Morse neural network via a KLdivergence produce confidently wrong predictions when presented with loss yields 1) a (unnormalized) generative Out-Of-Distribution (OOD) inputs, that is, inputs that are density, 2) an OOD detector, 3) a calibration far away from the data distribution with which the model temperature, 4) a generative sampler, along was trained (Murphy, 2023; Nagarajan et al., 2021; Liu with in the supervised case 5) a distance awareclassifier.

In this paper, we focus on ensemble UQ techniques, either Bayesian Recent advances in deep neural networks have demonstrated or non-Bayesian, as this group is less explored compared to remarkable performance in a wide variety of applications, the solely Bayesian techniques. An ensemble model aggregates ranging from recommendation systems and improving user the predictions of multiple individual base-learners (or experience to natural language processing and speech recognition ensemble members), which in our case are neural networks (Abiodun et al. 2018). Nevertheless, blindly relying (NNs), and the empirical variance of their predictions gives on the outcome of these models can have harmful effects, an approximate measure of uncertainty. The idea behind this especially in high-stake domains such as healthcare heuristic is highly intuitive: the more the base-learners disagree and autonomous driving, as models can provide inaccurate on the outcome, the more uncertain they are. Therefore, predictions when queried in out-of-distribution data the goal of ensemble members is to have a great level points (Amodei et al. 2016). Consequently, correctly quantifying of disagreement (variability) in the areas where little or no the uncertainty of models' predictions is an admissible data is available, and to have a high level of agreement in mechanism to distinguish where a model can or cannot regions with abundance of data (Pearce et al. 2018).

Estimating the uncertainty in deep neural network predictions is crucial for many real-world applications. A common approach to model uncertainty is to choose a parametric distribution and fit the data to it using maximum likelihood estimation. The chosen parametric form can be a poor fit to the data-generating distribution, resulting in unreliable uncertainty estimates. In this work, we propose SampleNet, a flexible and scalable architecture for modeling uncertainty that avoids specifying a parametric form on the output distribution. SampleNets do so by defining an empirical distribution using samples that are learned with the Energy Score and regularized with the Sinkhorn Divergence. SampleNets are shown to be able to well-fit a wide range of distributions and to outperform baselines on large-scale real-world regression tasks.