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The federated learning setting is prone to suffering from non-identically distributed data across participating agents. This gives rise to the task of model personalization, where agents collaborate to train several different machine learning models instea ...
We propose FedGP, a framework for privacy-preserving data release in the federated learning setting. We use generative adversarial networks, generator components of which are trained by FedAvg algorithm, to draw private artificial data samples and empirica ...
Medium-range numerical weather prediction (NWP) is crucial to human activities. Reliable weather forecasts allow better resource management and are essential for disaster preparation. Modern NWP models provide accurate medium-range forecasts, but they requ ...
This paper brings together machine learning and investigative journalism to examine sockpuppets accounts, a historical breed of fake accounts that are non-automated and human-controlled. Due to their flexible and human-centered nature, sockpuppets pose a c ...
Decentralized optimization methods enable on-device training of machine learning models without a central coordinator. In many scenarios communication between devices is energy demanding and time consuming and forms the bottleneck of the entire system. We ...
Gaze estimation allows robots to better understand users and thus to more precisely meet their needs. In this paper, we are interested in gaze sensing for analyzing collaborative tasks and manipulation behaviors in human-robot interactions (HRI), which dif ...
In a typical face recognition pipeline, the task ofthe face detector is to localize the face region. However, the facedetector localizes regions that look like a face, irrespective of theliveliness of the face, which makes the entire system susceptible to ...
We consider the problem of planning paths on graphs with some edges whose traversability is uncertain; for each uncertain edge, we are given a probability of being traversable (e.g., by a learned classifier). We categorize different interpretations of the ...
Macroscale and mesoscale simulations of hyper-concentrated sediment-laden flows rely on robust couplings of the Reynolds-Averaged Navier-Stokes equations in conjunction with the shear-stress transport k-ω turbulence model. Also other closure laws for model ...
Discrete Choice Models (DCMs) have a distinct advantage over Machine Learning (ML) classification algorithms, in that they employ a highly interpretable linear structure. However, a key drawback of DCMs compared to ML is the need to specify the utility fun ...
