Learning Ordinal Embedding from Sets

Ordinal embedding is the task of computing a meaningful multidimensional representation of objects, for which only qualitative constraints on their distance functions are known. In particular, we consider comparisons of the form “Which object from the pair (j, k) is more similar to object i?”. In this paper, we generalize this framework to the case where the ordinal constraints are not given at the level of individual points, but at the level of sets, and propose a distributional triplet embedding approach in a scalable learning framework. We show that the query complexity of our approach is on par with the single-item approach. Without having access to features of the items to be embedded, we show the applicability of our model on toy datasets for the task of reconstruction and demonstrate the validity of the obtained embeddings in experiments on synthetic and real-world datasets.

Continuous learning of emergent behavior in robotic matter

One of the main challenges in robotics is the development of systems that can adapt to their environment and achieve autonomous behavior. Current approaches typically aim to achieve this by increasing the complexity of the centralized controller by, e.g., direct modeling of their behavior, or implementing machine learning. In contrast, we simplify the controller using a decentralized and modular approach, with the aim of finding specific requirements needed for a robust and scalable learning strategy in robots. To achieve this, we conducted experiments and simulations on a specific robotic platform assembled from identical autonomous units that continuously sense their environment and react to it. By letting each unit adapt its behavior independently using a basic Monte Carlo scheme, the assembled system is able to learn and maintain optimal behavior in a dynamic environment as long as its memory is representative of the current environment, even when incurring damage. We show that the physical connection between the units is enough to achieve learning, and no additional communication or centralized information is required. As a result, such a distributed learning approach can be easily scaled to larger assemblies, blurring the boundaries between materials and robots, paving the way for a new class of modular “robotic matter” that can autonomously learn to thrive in dynamic or unfamiliar situations, for example, encountered by soft robots or self-assembled (micro)robots in various environments spanning from the medical realm to space explorations.

Holistic Evaluation and Support of Remote, Adjunct Faculty

Remote, adjunct faculty are becoming a large population for many institutions as online learning continues to grow. Because of the growth in this population of instructors, traditional means of evaluating faculty may not be efficient or scalable. Learning management systems (LMSs) can provide teaching analytics for many instructional behaviors. By building an analytics dashboard that collects instructor and student behaviors in online classrooms, institutions may be able to evaluate and support instructors in a more cost-effective and efficient way. This chapter will discuss the use of teaching analytics and their role in creating a holistic approach to teaching evaluation and faculty support.

Application of concise machine learning to construct accurate and interpretable EHR computable phenotypes

ABSTRACTObjectiveElectronic health records (EHRs) can improve patient care by enabling systematic identification of patients for targeted decision support. But, this requires scalable learning of computable phenotypes. To this end, we developed the feature engineering automation tool (FEAT) and assessed it in targeting screening for the under-diagnosed, under-treated disease primary aldosteronism.Materials and MethodsWe selected 1199 subjects receiving longitudinal care in one health system between 2007 and 2017 and classified them for hypertension (N=608), hypertension with unexplained hypokalemia (N=172), and apparent treatment-resistant hypertension (N=176) by chart review. We derived 331 features from EHR encounters, diagnoses, laboratories, medications, vitals, and notes. We modified FEAT to encourage model parsimony and compared its models’ performance and interpretability to that of expert-curated heuristics and conventional machine learning.ResultsFEAT models trained to replicate expert-curated heuristics had higher AUPRC scores than all other models (p < 0.001) except random forests and were smaller than all other models (p < 1e-6) except decision trees. FEAT models trained to predict chart review phenotypes exhibited similar AUPRC scores to penalized logistic regression while being substantially simpler than all other models (p < 1e-6). For treatment-resistant hypertension, FEAT learned a six-feature, clinically intuitive model that demonstrated an adjusted PPV of 0.73 and sensitivity of 0.54 in testing.DiscussionFEAT learns computable phenotypes that approach the performance of expert-curated heuristics and conventional machine learning without sacrificing interpretability.ConclusionBy constructing accurate and interpretable computable phenotypes at scale, FEAT has the potential to facilitate widespread, systematic clinical decision support.