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With the proliferation of high bandwidth cameras and AR/VR devices, and their increasing use in situation awareness applications, edge computing is gaining prominence to meet the throughput requirements of such applications. This work focuses on camera ...

Deep Reinforcement Learning (DRL) achieves great success in various domains. Communication in today's DRL algorithms takes non-negligible time compared to the computation. However, prior DRL frameworks usually focus on computation management while ...

The ability to scale out training workloads has been one of the key performance enablers of deep learning. The main scaling approach is data-parallel GPU-based training, which has been boosted by hardware and software support for highly efficient point-...

Federated learning (FL) is a machine learning paradigm that enables a cluster of decentralized edge devices to collaboratively train a shared machine learning model without exposing users' raw data. However, the intensive model training computation is ...

Machine Learning (ML) has emerged as a core technology to provide learning models to perform complex tasks. Boosted by Machine Learning as a Service (MLaaS), the number of applications relying on ML capabilities is ever increasing. However, ML models ...

Federated Learning (FL) is a popular deep learning approach that prevents centralizing large amounts of data, and instead relies on clients that update a global model using their local datasets. Classical FL algorithms use a central federator that, for ...

We explore the promises and challenges of employing sequential decision-making algorithms -- such as bandits, reinforcement learning, and active learning -- in law and public policy. While such algorithms have well-characterized performance in the ...

Deep Neural Networks (DNNs) have demonstrated impressive performance on many machine-learning tasks such as image recognition and language modeling, and are becoming prevalent even on mobile platforms. Despite so, designing neural architectures still ...

As more and more mobile/embedded applications employ Deep Neural Networks (DNNs) involving sensitive user data, mobile/embedded devices must provide a highly secure DNN execution environment to prevent privacy leaks. Aimed at securing DNN data, recent ...

Financial texts (e.g., economic news) play an important role in predicting stock prices. The effects of texts of different semantics (e.g., launching a product and reporting a small product bug) last for different time horizons. Despite the importance ...

Pre-trained language model (LM) has led to significant performance gains in various natural language processing (NLP) applications due to its strong literacy, e.g., the ability to capture word dependencies. However, the existing pre-trained LMs largely ...

Building customised IoT applications is often labor intensive. While several frameworks support developers in building sophisticated applications, they mainly focus on the communication with the underlying devices and their automation. Modular ...

Machine Learning (ML) is more than just training models, the whole life-cycle must be considered. Once deployed, a ML model needs to be constantly managed, supervised and debugged to guarantee its availability, validity and robustness in dynamic ...

Despite the large number of patients in Electronic Health Records (EHRs), the subset of usable data for modeling outcomes of specific phenotypes are often imbalanced and of modest size. This can be attributed to the uneven coverage of medical concepts ...

Early detection of influenza-like symptoms can prevent widespread flu viruses and enable timely treatments, particularly in the post-pandemic era. Mobile sensing leverages an increasingly diverse set of embedded sensors to capture fine-grained ...

Generating a novel and optimized molecule with desired chemical properties is an essential part of the drug discovery process. Failure to meet one of the required properties can frequently lead to failure in a clinical test which is costly. In addition, ...

Clinical machine learning models experience significantly degraded performance in datasets not seen during training, e.g., new hospitals or populations. Recent developments in domain generalization offer a promising solution to this problem by creating ...

Pre-training (PT) has been used successfully in many areas of machine learning. One area where PT would be extremely impactful is over electronic health record (EHR) data. Successful PT strategies on this modality could improve model performance in data-...

Recent advances in training deep learning models have demonstrated the potential to provide accurate chest X-ray interpretation and increase access to radiology expertise. However, poor generalization due to data distribution shifts in clinical settings ...

Balanced representation learning methods have been applied successfully to counterfactual inference from observational data. However, approaches that account for survival outcomes are relatively limited. Survival data are frequently encountered across ...