1 code implementation • 5 May 2022 • Asadullah Hill Galib, Andrew McDonald, Tyler Wilson, Lifeng Luo, Pang-Ning Tan
Accurate forecasting of extreme values in time series is critical due to the significant impact of extreme events on human and natural systems.
1 code implementation • 2 May 2022 • Andrew McDonald, Pang-Ning Tan, Lifeng Luo
In particular, existing normalizing flow architectures struggle to model multivariate extremes, characterized by heavy-tailed marginal distributions and asymmetric tail dependence among variables.
1 code implementation • 28 Jun 2021 • Andrew McDonald, Lai Wei, Vaibhav Srivastava
In this paper, we address the problem of multi-robot online estimation and coverage control by combining low- and high-fidelity data to learn and cover a sensory function of interest.
no code implementations • 12 Jan 2021 • Lai Wei, Andrew McDonald, Vaibhav Srivastava
Modeling the sensory field as a realization of a Gaussian Process and using Bayesian techniques, we devise a policy which aims to balance the tradeoff between learning the sensory function and covering the environment.
no code implementations • CVPR 2015 • Dimitris Stamos, Samuele Martelli, Moin Nabi, Andrew McDonald, Vittorio Murino, Massimiliano Pontil
However, previous work has highlighted the possible danger of simply training a model from the combined datasets, due to the presence of bias.