A deep latent variable model is a powerful method for capturing complex
distributions. These models assume that underlying structures, but unobserved,
are present within the data. In this dissertation, we explore high-dimensional
problems related to physiological monitoring using latent variable models.
First, we present a novel deep state-space model to generate electrical
waveforms of the heart using optically obtained signals as inputs. This can
bring about clinical diagnoses of heart disease via simple assessment through
wearable devices. Second, we present a brain signal modeling scheme that
combines the strengths of probabilistic graphical models and deep adversarial
learning. The structured representations can provide interpretability and
encode inductive biases to reduce the data complexity of neural oscillations.
The efficacy of the learned representations is further studied in epilepsy
seizure detection formulated as an unsupervised learning problem. Third, we
propose a framework for the joint modeling of physiological measures and
behavior. Existing methods to combine multiple sources of brain data provided
are limited. Direct analysis of the relationship between different types of
physiological measures usually does not involve behavioral data. Our method can
identify the unique and shared contributions of brain regions to behavior and
can be used to discover new functions of brain regions. The success of these
innovative computational methods would allow the translation of biomarker
findings across species and provide insight into neurocognitive analysis in
numerous biological studies and clinical diagnoses, as well as emerging
consumer applications.

本文探索运用深层潜变量模型解决与生理监测相关的高维问题，包括利用心电波形生成模型进行心脏疾病诊断、结合概率图模型和深度对抗学习的大脑信号建模方案以及生理测量和行为联合建模的框架。这些创新性计算方法的成功应用能够在生物学研究、临床诊断和新兴消费应用中实现生物标记发现的跨物种翻译，并为神经认知分析提供洞察力。

生理信号的深层潜变量建模

Deep Latent Variable Modeling of Physiological Signals

The abstract visual reasoning ability in human intelligence benefits
discovering underlying rules in the novel environment. Raven's Progressive
Matrix (RPM) is a classic test to realize such ability in machine intelligence
by selecting from candidates. Recent studies suggest that solving RPM in an
answer-generation way boosts a more in-depth understanding of rules. However,
existing generative solvers cannot discover the global concept-changing rules
without auxiliary supervision (e.g., rule annotations and distractors in
candidate sets). To this end, we propose a deep latent variable model for
Concept-changing Rule ABstraction (CRAB) by learning interpretable concepts and
parsing concept-changing rules in the latent space. With the iterative learning
process, CRAB can automatically abstract global rules shared on the dataset on
each concept and form the learnable prior knowledge of global rules. CRAB
outperforms the baselines trained without auxiliary supervision in the
arbitrary-position answer generation task and achieves comparable and even
higher accuracy than the compared models trained with auxiliary supervision.
Finally, we conduct experiments to illustrate the interpretability of CRAB in
concept learning, answer selection, and global rule abstraction.

提出了一种深度潜变量模型来学习可解释的概念，并在该过程中分析潜在空间中的概念转换规则，自动抽象出数据集上共享的全局规则，并实现抽象位置上的答案生成任务。

解决 Raven 进阶矩阵问题的概念变化规则的抽象化

Abstracting Concept-Changing Rules for Solving Raven's Progressive  Matrix Problems

Providing a suitable reward function to reinforcement learning can be
difficult in many real world applications. While inverse reinforcement learning
(IRL) holds promise for automatically learning reward functions from
demonstrations, several major challenges remain. First, existing IRL methods
learn reward functions from scratch, requiring large numbers of demonstrations
to correctly infer the reward for each task the agent may need to perform.
Second, existing methods typically assume homogeneous demonstrations for a
single behavior or task, while in practice, it might be easier to collect
datasets of heterogeneous but related behaviors. To this end, we propose a deep
latent variable model that is capable of learning rewards from demonstrations
of distinct but related tasks in an unsupervised way. Critically, our model can
infer rewards for new, structurally-similar tasks from a single demonstration.
Our experiments on multiple continuous control tasks demonstrate the
effectiveness of our approach compared to state-of-the-art imitation and
inverse reinforcement learning methods.

研究表明通过使用深度潜在变量模型可以实现无监督学习来自不同但相关的任务演示数据的奖励函数，从而有效地解决逆强化学习中从少量演示推断奖励的问题，并在多个连续控制任务中展示了实验结果。