Federated learning came into being with the increasing concern of privacy security, as
people's sensitive information is being exposed under the era of big data. It is an algorithm …

Driven by privacy concerns and the visions of deep learning, the last four years have
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …

Recent studies show that private training data can be leaked through the gradients sharing
mechanism deployed in distributed machine learning systems, such as federated learning …

Decentralized stochastic optimization methods have gained a lot of attention recently, mainly
because of their cheap per iteration cost, data locality, and their communication-efficiency. In …

Abstract We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose
approach for uncertainty representation and calibration in deep learning. Stochastic Weight …

High-dimensional and sparse (HiDS) matrices are frequently found in various industrial
applications. A latent factor analysis (LFA) model is commonly adopted to extract useful …

Huge scale machine learning problems are nowadays tackled by distributed optimization
algorithms, ie algorithms that leverage the compute power of many devices for training. The …

Next Point-of-Interest (POI) recommendation which is of great value to both users and POI
holders is a challenging task since complex sequential patterns and rich contexts are …

Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …