With the advent of sophisticated machine learning (ML) techniques and the
promising results they yield, especially in medical applications, where they
have been investigated for different tasks to enhance the decision-making
process. Since visualization is such an effective tool for human comprehension,
memorization, and judgment, we have presented a first-of-its-kind estimation
approach we refer to as Visualized Learning for Machine Learning (VL4ML) that
not only can serve to assist physicians and clinicians in making reasoned
medical decisions, but it also allows to appreciate the uncertainty
visualization, which could raise incertitude in making the appropriate
classification or prediction. For the proof of concept, and to demonstrate the
generalized nature of this visualized estimation approach, five different case
studies are examined for different types of tasks including classification,
regression, and longitudinal prediction. A survey analysis with more than 100
individuals is also conducted to assess users' feedback on this visualized
estimation method. The experiments and the survey demonstrate the practical
merits of the VL4ML that include: (1) appreciating visually clinical/medical
estimations; (2) getting closer to the patients' preferences; (3) improving
doctor-patient communication, and (4) visualizing the uncertainty introduced
through the black box effect of the deployed ML algorithm. All the source codes
are shared via a GitHub repository.

本文介绍了一种名为 VL4ML 的可视化学习方法，该方法在医疗应用中得到了广泛的探究。作者通过 VL4ML 展示了其演示性质，以证明其在医疗决策中的应用。作者还在文章中针对分类、回归以及纵向预测的不同类型任务进行了五个案例研究。通过分析调查，本文证实了 VL4ML 的实用价值。

通过可视化学习和深度学习进行情感医疗估计和决策

Affective Medical Estimation and Decision Making via Visualized Learning and Deep Learning

A framework is introduced for sequentially solving convex stochastic
minimization problems, where the objective functions change slowly, in the
sense that the distance between successive minimizers is bounded. The
minimization problems are solved by sequentially applying a selected
optimization algorithm, such as stochastic gradient descent (SGD), based on
drawing a number of samples in order to carry the iterations. Two tracking
criteria are introduced to evaluate approximate minimizer quality: one based on
being accurate with respect to the mean trajectory, and the other based on
being accurate in high probability (IHP). An estimate of a bound on the
minimizers' change, combined with properties of the chosen optimization
algorithm, is used to select the number of samples needed to meet the desired
tracking criterion. A technique to estimate the change in minimizers is
provided along with analysis to show that eventually the estimate upper bounds
the change in minimizers. This estimate of the change in minimizers provides
sample size selection rules that guarantee that the tracking criterion is met
for sufficiently large number of time steps. Simulations are used to confirm
that the estimation approach provides the desired tracking accuracy in
practice, while being efficient in terms of number of samples used in each time
step.

介绍了一种框架，用于解决凸随机极小化问题，其中目标函数变化缓慢，通过应用选择的优化算法，例如随机梯度下降（SGD），来顺序地解决极小化问题。有两种跟踪标准来评估近似极小值的质量，一种是基于对均值轨迹的准确性，另一种是基于高概率的准确性。提供了估计极小值变化的技术，以及分析结果表明最终估计会上限极小值的变化。这个估计量提供了样本量选择规则，保证了足够的时间步长使跟踪标准得到满足。实验表明，该估计方法在实践中提供了所需的跟踪精度，同时在每个时间步中使用的样本数量方面是高效的。

自适应序列随机优化

Adaptive Sequential Stochastic Optimization

Many processes in biology, chemistry, physics, medicine, and engineering are
modeled by a system of differential equations. Such a system is usually
characterized via unknown parameters and estimating their 'true' value is thus
required. In this paper we focus on the quite common systems for which the
derivatives of the states may be written as sums of products of a function of
the states and a function of the parameters.
For such a system linear in functions of the unknown parameters we present a
necessary and sufficient condition for identifiability of the parameters. We
develop an estimation approach that bypasses the heavy computational burden of
numerical integration and avoids the estimation of system states derivatives,
drawbacks from which many classic estimation methods suffer. We also suggest an
experimental design for which smoothing can be circumvented. The optimal rate
of the proposed estimators, i.e., their $\sqrt n$-consistency, is proved and
simulation results illustrate their excellent finite sample performance and
compare it to other estimation approaches.

本文研究了一类常见系统的可辨识性以及提出了一种估计方法，避免了数值积分和系统状态导数估计的计算负担，并建议了一种适用于该方法的实验设计。对该估计器的最优速率进行了证明，并将其优异的有限样本性能与其他估计方法进行了比较。