In the study of various diseases, heterogeneity among patients usually leads to different progression patterns and may require different types of therapeutic intervention. Therefore, it is important to study patient subtyping, which is grouping of patients into disease characterizing subtypes. Subtyping from complex patient data is challenging because of the information heterogeneity and temporal dynamics. Long-Short Term Memory (LSTM) has been successfully used in many domains for processing sequential data, and recently applied for analyzing longitudinal patient records. The LSTM units are designed to handle data with constant elapsed times between consecutive elements of a sequence. Given that time lapse between successive elements in patient records can vary from days to months, the design of traditional LSTM may lead to suboptimal performance. In this paper, we propose a novel LSTM unit called Time-Aware LSTM (T-LSTM) to handle irregular time intervals in longitudinal patient records. We learn a subspace decomposition of the cell memory which enables time decay to discount the memory content according to the elapsed time. We propose a patient subtyping model that leverages the proposed T-LSTM in an auto-encoder to learn a powerful single representation for sequential records of patients, which are then used to cluster patients into clinical subtypes. Experiments on synthetic and real world datasets show that the proposed T-LSTM architecture captures the underlying structures in the sequences with time irregularities.

Many data mining applications involve a set of related learning tasks. Multi-task learning (MTL) is a learning paradigm that improves generalization performance by transferring knowledge among those tasks. MTL has attracted so much attention in the community, and various algorithms have been successfully developed. Recently, distributed MTL has also been studied for related tasks whose data is distributed across different geographical regions. One prominent challenge of the distributed MTL frameworks is to maintain the privacy of the data. The distributed data may contain sensitive and private information such as patients' records and registers of a company. In such cases, distributed MTL frameworks are required to preserve the privacy of the data. In this paper, we propose a novel privacy-preserving distributed MTL framework to address this challenge. A privacy-preserving proximal gradient algorithm, which asynchronously updates models of the learning tasks, is introduced to solve a general class of MTL formulations. The proposed asynchronous approach is robust against network delays and provides a guaranteed differential privacy through carefully designed perturbation. Theoretical guarantees of the proposed algorithm are derived and supported by the extensive experimental results.

Heterogeneous hyper-networks is used to represent multi-modal and composite interactions between data points. In such networks, several different types of nodes form a hyperedge. Heterogeneous hyper-network embedding learns a distributed node representation under such complex interactions while preserving the network structure. However, this is a challenging task due to the multiple modalities and composite interactions. In this study, a deep approach is proposed to embed heterogeneous attributed hyper-networks with complicated and non-linear node relationships. In particular, a fully-connected and graph convolutional layers are designed to project different types of nodes into a common low-dimensional space, a tuple-wise similarity function is proposed to preserve the network structure, and a ranking based loss function is used to improve the similarity scores of hyperedges in the embedding space. The proposed approach is evaluated on synthetic and real world datasets and a better performance is obtained compared with baselines.

Sign languages are visual languages used as the primary communication medium for the Deaf community. The signs comprise manual and non-manual articulators such as hand shapes, upper body movement, and facial expressions. Sign Language Recognition (SLR) aims to learn spatial and temporal representations from the videos of the signs. Most SLR studies focus on manual features often extracted from the shape of the dominant hand or the entire frame. However, facial expressions combined with hand and body gestures may also play a significant role in discriminating the context represented in the sign videos. In this study, we propose an isolated SLR framework based on Spatial-Temporal Graph Convolutional Networks (ST-GCNs) and Multi-Cue Long Short-Term Memorys (MC-LSTMs) to exploit multi-articulatory (e.g., body, hands, and face) information for recognizing sign glosses. We train an ST-GCN model for learning representations from the upper body and hands. Meanwhile, spatial embeddings of hand shape and facial expression cues are extracted from Convolutional Neural Networks (CNNs) pre-trained on large-scale hand and facial expression datasets. Thus, the proposed framework coupling ST-GCNs with MC-LSTMs for multi-articulatory temporal modeling can provide insights into the contribution of each visual Sign Language (SL) cue to recognition performance. To evaluate the proposed framework, we conducted extensive analyzes on two Turkish SL benchmark datasets with different linguistic properties, BosphorusSign22k and AUTSL. While we obtained comparable recognition performance with the skeleton-based state-of-the-art, we observe that incorporating multiple visual SL cues improves the recognition performance, especially in certain sign classes where multi-cue information is vital. The code is available at: https://github.com/ogulcanozdemir/multicue-slr.

One major limitation of linear models is the lack of capability to capture predictive information from interactions between features. While introducing high-order feature interaction terms can overcome this limitation, this approach tremendously increases the model complexity and imposes significant challenges in the learning against overfitting. In this paper, we proposed a novel Multi-Task feature Interaction Learning~(MTIL) framework to exploit the task relatedness from high-order feature interactions, which provides better generalization performance by inductive transfer among tasks via shared representations of feature interactions. We formulate two concrete approaches under this framework and provide efficient algorithms: the shared interaction approach and the embedded interaction approach. The former assumes tasks share the same set of interactions, and the latter assumes feature interactions from multiple tasks come from a shared subspace. We have provided efficient algorithms for solving the two approaches. Extensive empirical studies on both synthetic and real datasets have demonstrated the effectiveness of the proposed framework.

Many real-world machine learning applications involve several learning tasks which are inter-related. For example, in healthcare domain, we need to learn a predictive model of a certain disease for many hospitals. The models for each hospital may be different because of the inherent differences in the distributions of the patient populations. However, the models are also closely related because of the nature of the learning tasks modeling the same disease. By simultaneously learning all the tasks, multi-task learning (MTL) paradigm performs inductive knowledge transfer among tasks to improve the generalization performance. When datasets for the learning tasks are stored at different locations, it may not always be feasible to transfer the data to provide a data centralized computing environment due to various practical issues such as high data volume and privacy. In this paper, we propose a principled MTL framework for distributed and asynchronous optimization to address the aforementioned challenges. In our framework, gradient update does not wait for collecting the gradient information from all the tasks. Therefore, the proposed method is very efficient when the communication delay is too high for some task nodes. We show that many regularized MTL formulations can benefit from this framework, including the low-rank MTL for shared subspace learning. Empirical studies on both synthetic and real-world datasets demonstrate the efficiency and effectiveness of the proposed framework.