13 citations · 38 across the 9 of their papers we have counts for
11 papers
A Learning-Based Approach to Approximate Coded Computation
Navneet Agrawal, Yuqin Qiu, Matthias Frey +4
Lagrange coded computation (LCC) is essential to solving problems about matrix polynomials in a coded distributed fashion; nevertheless, it can only solve the problems that are rep…
Fast Rate Generalization Error Bounds: Variations on a Theme
Xuetong Wu, Jonathan H. Manton, Uwe Aickelin +1
A recent line of works, initiated by Russo and Xu, has shown that the generalization error of a learning algorithm can be upper bounded by information measures. In most of the rele…
A Bayesian Approach to (Online) Transfer Learning: Theory and Algorithms
Xuetong Wu, Jonathan H. Manton, Uwe Aickelin +1
Transfer learning is a machine learning paradigm where knowledge from one problem is utilized to solve a new but related problem. While conceivable that knowledge from one task cou…
On Minimizing Symbol Error Rate Over Fading Channels with Low-Resolution Quantization
Neil Irwin Bernardo, Jingge Zhu, Jamie Evans
We analyze the symbol error probability (SEP) of -ary pulse amplitude modulation (-PAM) receivers equipped with optimal low-resolution quantizers. We first show that the opti…
Online Transfer Learning: Negative Transfer and Effect of Prior Knowledge
Xuetong Wu, Jonathan H. Manton, Uwe Aickelin +1
Transfer learning is a machine learning paradigm where the knowledge from one task is utilized to resolve the problem in a related task. On the one hand, it is conceivable that kno…
Is Phase Shift Keying Optimal for Channels with Phase-Quantized Output?
Neil Irwin Bernardo, Jingge Zhu, Jamie Evans
This paper establishes the capacity of additive white Gaussian noise (AWGN) channels with phase-quantized output. We show that a rotated -phase shift keying scheme is the capa…