Infinite Mixture of Global Gaussian Processes

  • Perez-Cruz F.
  • Pradier M.

In this paper, we propose a simple and powerful approach to solve nonlinear regression problems using an infinite mixture of global Gaussian processes (IMoGGP). Our method is able to deal with arbitrary output distributions, nonstationary signals, heteroscedastic noise and multimodal predictive distributions straightforwardly, without the modeler needing to know these attributes a priori. The IMoGGP can be interpreted as a mixture of experts, in which the experts are not local and they cooperate in the whole input space to provide accurate regression estimates. It can also be framed as a Dependent Dirichlet Process to solve discriminative tasks. Simulations show that our method gives comparative results to state-of-the-art approaches and its simplicity makes it an attractive method for non-ML-expert practitioners that do not want to rely on many different models to test which one fits their data best.

Recent Publications

August 09, 2017

A Cloud Native Approach to 5G Network Slicing

  • Francini A.
  • Miller R.
  • Sharma S.

5G networks will have to support a set of very diverse and often extreme requirements. Network slicing offers an effective way to unlock the full potential of 5G networks and meet those requirements on a shared network infrastructure. This paper presents a cloud native approach to network slicing. The cloud ...

August 01, 2017

Modeling and simulation of RSOA with a dual-electrode configuration

  • De Valicourt G.
  • Liu Z.
  • Violas M.
  • Wang H.
  • Wu Q.

Based on the physical model of a bulk reflective semiconductor optical amplifier (RSOA) used as a modulator in radio over fiber (RoF) links, the distributions of carrier density, signal photon density, and amplified spontaneous emission photon density are demonstrated. One of limits in the use of RSOA is the lower ...

July 12, 2017

PrivApprox: Privacy-Preserving Stream Analytics

  • Chen R.
  • Christof Fetzer
  • Le D.
  • Martin Beck
  • Pramod Bhatotia
  • Thorsten Strufe

How to preserve users' privacy while supporting high-utility analytics for low-latency stream processing? To answer this question: we describe the design, implementation and evaluation of PRIVAPPROX, a data analytics system for privacy-preserving stream processing. PRIVAPPROX provides three properties: (i) Privacy: zero-knowledge privacy (ezk) guarantees for users, a privacy bound tighter ...