GreenMO: Virtualized User-proportionate MIMO

no code implementations • 29 Nov 2022 • Agrim Gupta, Sajjad Nassirpour, Manideep Dunna, Eamon Patamasing, Alireza Vahid, Dinesh Bharadia

The reason is that traditionally MIMO requires a separate RF chain per antenna, so the power consumption scales with number of antennas, instead of number of users, hence becomes energy inefficient.

Ranking Recovery from Limited Comparisons using Low-Rank Matrix Completion

no code implementations • 14 Jun 2018 • Tal Levy, Alireza Vahid, Raja Giryes

The partial and noisy data of pairwise comparisons is transformed into a matrix form.

Low-Rank Matrix Completion

Accelerated Bayesian Optimization throughWeight-Prior Tuning

no code implementations • 21 May 2018 • Alistair Shilton, Sunil Gupta, Santu Rana, Pratibha Vellanki, Laurence Park, Cheng Li, Svetha Venkatesh, Alessandra Sutti, David Rubin, Thomas Dorin, Alireza Vahid, Murray Height, Teo Slezak

In this paper we show how such auxiliary data may be used to construct a GP covariance corresponding to a more appropriate weight prior for the objective function.

Transfer Learning

Covariance Function Pre-Training with m-Kernels for Accelerated Bayesian Optimisation

no code implementations • 15 Feb 2018 • Alistair Shilton, Sunil Gupta, Santu Rana, Pratibha Vellanki, Cheng Li, Laurence Park, Svetha Venkatesh, Alessandra Sutti, David Rubin, Thomas Dorin, Alireza Vahid, Murray Height

The paper presents a novel approach to direct covariance function learning for Bayesian optimisation, with particular emphasis on experimental design problems where an existing corpus of condensed knowledge is present.

Bayesian Optimisation Experimental Design