Generative Adversarial Networks (GANs) are a generative framework with a notorious reputation for instability. Despite significant work in attempting to improve stability, training remains extremely difficult in practice. Nearly all GAN optimization methods are built on either simultaneous (Sim-GDA) or alternating (Alt-GDA) gradient descent-ascent, where the generator and discriminator are updated either at the same time iteratively or in a fixed pattern. Unfortunately, neither Sim-GDA nor Alt-GDA have any strongly convergent properties, nor are they Lyapunov-stable. In this paper, we prove for simple GANs, for which training had been proven non-convergent under Sim-GDA and Alt-GDA, that our newly introduced training method is Lyapunov-stable. We then design a novel oracle-guided GDA training strategy called Dynamic-GDA that leverages generalized analogs of the properties exhibited in the simple case. We also prove that in contrast to Sim/Alt-GDA, GANs with Dynamic-GDA achieve Lyapunov-stable training with non-infinitesimal learning rates. Empirically, we show Dynamic-GDA improves convergence orthogonally to common stabilizing techniques on 8 classes of GAN models and 7 different data sets.

Generative modeling is a powerful technique that involves creating machine learning models capable of creating new data similar to the data it was trained on. Generative Adversarial Networks (GANs) are a leading approach for generative modeling. However, GAN training is known to be a notoriously difficult task. GAN convergence issues are largely caused by the supports of the real and generated distributions being disjoint. To tackle this open problem, we propose a novel GAN pre-training process that effectively aligns the supports of the generated and real data prior to applying traditional adversarial GAN training. The key component of our method, called AlignGAN, is learning a mapping between the input data distribution and a latent representation defined over a hypersphere, regularized by a One Class Classifier. This successfully encourages the generator to produce samples throughout the support of the real data, while not generating samples outside the support. We maintain support alignment through low-bandwidth noise convolutions and additional One Class regularization, leading to continued stable GAN training. We validate our approach against leading stabilization methods on three benchmark datasets, showing AlignGAN routinely produces the best results.

Human activity recognition (HAR) is the process of using mobile sensor data to determine the physical activities performed by individuals. HAR is the backbone of many mobile healthcare applications, such as passive health monitoring systems, early diagnosing systems, and fall detection systems. Effective HAR models rely on deep learning architectures and big data in order to accurately classify activities. Unfortunately, HAR datasets are expensive to collect, are often mislabeled, and have large class imbalances. State-of-the-art approaches to address these challenges utilize Generative Adversarial Networks (GANs) for generating additional synthetic data along with their labels. Problematically, these HAR GANs only synthesize continuous features — features that are represented by real numbers — recorded from gyroscopes, accelerometers, and other sensors that produce continuous data. This is limiting since mobile sensor data commonly has discrete features that provide additional context such as device location and the time-of-day, which have been shown to substantially improve HAR classification. Hence, we studied Conditional Tabular Generative Adversarial Networks (CTGANs) for data generation to synthesize mobile sensor data containing both continuous and discrete features, a task never been done by state-of-the-art approaches. We show HAR-CTGANs generate data with greater realism resulting in allowing better downstream performance in HAR models, and when state-of-the-art models were modified with HAR-CTGAN characteristics, downstream performance also improves.

Corpuses of unstructured textual data, such as text messages between individuals, are often predictive of medical issues such as depression. The text data usually used in healthcare applications has high value and great variety, but is typically small in volume. Generating labeled unstructured text data is important to improve models by augmenting these small datasets, as well as to facilitate anonymization. While methods for labeled data generation exist, not all of them generalize well to small datasets. In this work, we thus perform a much needed systematic comparison of conditional text generation models that are promising for small datasets due to their unified architectures. We identify and implement a family of nine conditional sequence generative adversarial networks for text generation, which we collectively refer to as cSeqGAN models. These models are characterized along two orthogonal design dimensions: weighting strategies and feedback mechanisms. We conduct a comparative study evaluating the generation ability of the nine cSeqGAN models on three diverse text datasets with depression and sentiment labels. To assess the quality and realism of the generated text, we use standard machine learning metrics as well as human assessment via a user study. While the unconditioned models produced predictive text, the cSeqGAN models produced more realistic text. Our comparative study lays a solid foundation and provides important insights for further text generation research, particularly for the small datasets common within the healthcare domain.

Recurrent Classifier Chains (RCCs) are a leading approach for multi-label classification as they directly model the interdependencies between classes. Unfortunately, existing RCCs assume that every training instance is completely labeled with all its ground truth classes. In practice often only a subset of an instance's labels are annotated, while the annotations for other classes aremissing. RCCs fail in this missing label scenario, predicting many false negatives and potentially missing important classes. In this work, we propose Robust-RCC, the first strategy for tackling this open problem of RCCs failing formulti-label missing-label data. Robust-RCC is a new type of deep recurrent classifier chain empowered to model inter-class relationships essential for predicting thecomplete label set most likely to match the ground truth. The key to Robust-RCC is the design of the Multi Incomplete Label Risk (MILR) function, which we prove to be equal in expectation to the true risk of the ground truth full label set despite being computed from incompletely labeled data. Our experimental study demonstrates that Robust-RCC consistently beats six state-of-of-the-art methods by as much as 30% in predicting the true labels.

Early classification algorithms help users react faster to their machine learning model's predictions. Early warning systems in hospitals, for example, let clinicians improve their patients' outcomes by accurately predicting infections. While early classification systems are advancing rapidly, a major gap remains: existing systems do not consider irregular time series, which have uneven and often-long gaps between their observations. Such series are notoriously pervasive in impactful domains like healthcare. We bridge this gap and study early classification of irregular time series, a new setting for early classifiers that opens doors to more real-world problems. Our solution, Stop&Hop, uses a continuous-time recurrent network to model ongoing irregular time series in real time, while an irregularity-aware halting policy, trained with reinforcement learning, predicts when to stop and classify the streaming series. By taking real-valued step sizes, the halting policy flexibly decides exactly when to stop ongoing series in real time. This way, Stop&Hop seamlessly integrates information contained in the timing of observations, a new and vital source for early classification in this setting, with the time series values to provide early classifications for irregular time series. Using four synthetic and three real-world datasets, we demonstrate that Stop&Hop consistently makes earlier and more-accurate predictions than state-of-the-art alternatives adapted to this new problem. Our code is publicly available at https://github.com/thartvigsen/StopAndHop.

In important domains from video stream analytics to human context recognition, datasets are only partially-labeled. Worse yet, the labels are often applied sequentially, as annotators choose labels frame-by-frame or timestep-by-timestep in sequence. With labels not collected independently, this results in sequential bias in the labeling. Unfortunately, current state-of-the-art methods for partially labeled data are rendered ineffective under sequential bias. In this work, we propose a novel solution to tackling this open sequential bias problem, called DeepSPU. DeepSPU recovers missing labels by constructing a model of the sequentially biased labeling process itself. This labeling model is then learned jointly with the prediction model that infers the missing labels in an iterative training process. Further, we regulate this training using a theoretically-justified cost functions that prevent our model from converging to incorrect but low-cost solution. Our experimental studies demonstrate that DeepSPU consistently outperforms the state-of-the-art methods by over 10% on a rich variety of real-world datasets.

Open set recognition models address the real-world scenario where classes of data unobserved during training are encountered in testing after deployment. Closed set classifiers wrongly attempt to classify instances from an unknown class as belonging to one of the known classes from the training set, which reduces the model’s accuracy. Ideally, these unknown instances should be recognized as such, while known instances should continue to be accurately classified. Unfortunately, state-of-the-art open set methods solve this problem by making restrictive assumptions on the variance and/or boundedness of the distributions of known classes. In this paper, we propose a novel method, Variational Open-Set Recognition (VOSR) that eliminates these assumptions. VOSR incorporates a closed set classifier, an unknown detector, and a novel Structured Gaussian Mixture Variational Autoencoder (SGM-VAE) that guarantees separable class distributions with known variances in its la-tent space. Further, by encouraging a large distance between class-specific distributions, VOSR increases the likelihood that instances from unknown classes lie in low-probability regions and thus are more readily identifiable. In rigorous evaluation, we demonstrate that VOSR outperforms state-of-the-art open set classifiers with up to a 14% F1 score increase in identifying instances from unknown classes in multiple image classification and human activity recognition datasets.

Human activity recognition (HAR), the task of predicting the activities performed by an individual using mobile sensor data, is an active and important area of research. Unfortunately, it is very costly to collect the data required to train robust HAR classifiers. To tackle this issue, there has been an increasing focus on generating synthetic HAR data for augmentation purposes. The state-of-the-art generative HAR approaches utilize Generative Adversarial Networks (GANs) to produce realistic synthetic HAR data. However, these solutions can not generate personalized data that matches the behavior of particular users, limiting their potential use cases. This is particularly problematic in the mobile health domain, where the target users are often elderly or disabled and are thus likely to have activity signals that are unique from the general population. To overcome this drawback, we propose a novel controllable GAN solution Control-HAR-GAN. Our approach learns user and activity signals independently, and when generating synthetic instances, practitioners can specify both the activity to be generated as well as the user the data should match. This has the added benefit in that our model supports novel user-activity pairs by generating examples that match the data that would have been recorded by a particular user if they had performed the target activity, even if the user never performed that activity during the data collection process. We show that our model outperforms the existing HAR GAN approach in generating observed user-activity pairs by up to 10%. Additionally, our approach can also perform the task of novel user-activity pair generation, which is impossible for existing approaches.

In big data applications from digital health to assisted living smart systems, only a fraction of data instances used for training classifiers t end to be labeled. One important subfield of weakly labeled learning, called Positive Unlabeled (PU) learning, does not require a completely labeled dataset in order to train a strong classifier. This is crucial as in many domains it is expensive or impossible to obtain a completely labeled dataset. While prior PU work assumed that unlabeled instances occurred with a random uniform distribution, we observe that labeled (and unlabeled) data tends to occur in long contiguous sequences (or bursts) due the prevalent burst labeling behavior by human annotators. Burst labeling leads to a sequential bias in PU data not addressed by state-of-the-art methods. To tackle this open problem of learning under sequential bias, we propose BurstPU, the first framework for training a classifier on sequentially labeled PU data. BurstPU addresses the challenge that two interdependent models must be learned, namely, the classification model and the labeling likelihood model, with the later predicting the likelihood that a given instance is labeled. The labeling likelihood model is then needed during the training of the classification model to account for the bias in the labeling process. Our experimental study demonstrates that BurstPU consistently outperforms all state-of-the-art PU methods on a rich variety of diverse real-world datasets, and can learn from fewer labeled instances compared to state-of-art PU methods.

Current research in passive health monitoring utilizes machine learning methods to infer users' symptoms and health status from smartphone-sensed data, which can be gathered on a large scale. However, reasoning about smartphone-sensed health behaviors should engage health experts who may not want to solely rely on computational approaches as they provide limited insights. We designed and proposed a visualization framework for the INTeractive Observation of Smartphone-Inferred Symptoms (INTOSIS), that supports contextualization of symptomatic days by presenting a holistic picture of complex smartphone data for analysts to find c oncerning b ehavior patterns. For instance, while sedentary behavior caused by the flu is concerning, sedentary behaviors on holidays are non-concerning. INTOSIS visualizes multiple smartphone sensor data channels such as geo-location, app usage and screen usage. It uses visual metaphors to effectively represent the data to help analysts derive important human-understandable spatio-temporal contexts and assign health consequences with corresponding semantic labels. INTOSIS provides timeline visualizations of contextual clues such as screen and app usage at night that support an analyst in being able to reason about and then extract plausible explanations for the occurrence of certain symptoms, such as, sleep problems. We validate INTOSIS with intuitive use cases, using a real-world smartphone-sensed dataset, along with expert evaluation.