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Efficient online learning with pairwise loss functions is a crucial component in building large-scale learning system that maximizes the area under the Receiver Operator Characteristic (ROC) curve. In this paper we investigate the generalization performance of online learning algorithms with pairwise loss functions. We show that the existing proof(More)
Multi-task learning leverages shared information among data sets to improve the learning performance of individual tasks. The paper applies this framework for data where each task is a phase-shifted periodic time series. In particular, we develop a novel Bayesian nonparamet-ric model capturing a mixture of Gaussian processes where each task is a sum of a(More)
Non-CPU devices on a modern system-on-a-chip (SoC), ranging from accelerators to I/O controllers, account for a significant portion of the chip area. It is therefore vital for system energy efficiency that idle devices can enter a low-power state while still meeting the performance expectation. This is called device runtime Power Management (PM) for which(More)
This paper investigates an antenna selection scheme performing interference mitigation in device-to-device (D2D) communication underlaying cellular networks. Exact closed-form expression of the ergodic achievable rate is derived. Based on this result, the analysis in the high SNR regime at the base station (BS) and the scenario with very large antenna(More)
Gaussian processes (GP) provide an attractive machine learning model due to their non-parametric form, their flexibility to capture many types of observation data, and their generic inference procedures. Sparse GP inference algorithms address the cubic complexity of GPs by focusing on a small set of pseudo-samples. To date, such approaches have focused on(More)
In this paper, we investigate an interference mitigation scheme by antenna selection in device-to-device (D2D) communication underlaying downlink cellular networks. We first present the closed-form expression of the system achievable rate and its asymptotic behaviors at high signal-to-noise ratio (SNR) and the large antenna number scenarios. It is shown(More)
Many astronomical phenomena exhibit patterns that have periodic behavior. An important step when analyzing data from such processes is the problem of identifying the period: estimating the period of a periodic function based on noisy observations made at irregularly spaced time points. This problem is still a difficult challenge despite extensive study in(More)