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Efficient online learning with pairwise loss functions is a crucial component in building largescale 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 techniques(More)
Efficient online learning with pairwise loss functions is a crucial component in building largescale 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 techniques(More)
Multi-task learning models using Gaussian processes (GP) have been recently developed and successfully applied in various applications. The main difficulty with this approach is the computational cost of inference using the union of examples from all tasks. The paper investigates this problem for the grouped mixed-effect GP model where each individual(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 presents an analytical performance investigation of both beamforming (BF) and interference cancellation (IC) strategies for a device-to-device (D2D) communication system underlaying a cellular network with an M−antenna base station (BS). We first derive new closed-form expressions for the ergodic achievable rate for BF and IC precoding strategies(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 nonparametric model capturing a mixture of Gaussian processes where each task is a sum of a(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)
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)