Sequential Gaussian Processes for Online Learning of Nonstationary Functions

Many machine learning problems can be framed in the context of estimating functions, and often these are time-dependent functions that are estimated in real-time as observations arrive. Gaussian processes (GPs) are an attractive choice for modeling real-valued nonlinear functions due to their flexibility and uncertainty quantification. However, the typical GP regression model suffers from several drawbacks: i) Conventional GP inference scales $O(N^{3})$ with respect to the number of observations; ii) updating a GP model sequentially is not trivial; and iii) covariance kernels often enforce stationarity constraints on the function, while GPs with non-stationary covariance kernels are often intractable to use in practice. To overcome these issues, we propose an online sequential Monte Carlo algorithm to fit mixtures of GPs that capture non-stationary behavior while allowing for fast, distributed inference. By formulating hyperparameter optimization as a multi-armed bandit problem, we accelerate mixing for real time inference. Our approach empirically improves performance over state-of-the-art methods for online GP estimation in the context of prediction for simulated non-stationary data and hospital time series data

A simplified implementation of the stationary liquid mass balance method for on-line OUR monitoring in animal cell cultures

This is the peer reviewed version of the following article: [Fontova, A. , Lecina, M. , López‐Repullo, J. , Martínez‐Monge, I. , Comas, P. , Bragós, R. and Cairó, J. J. (2018), A simplified implementation of the stationary liquid mass balance method for on‐line OUR monitoring in animal cell cultures. J. Chem. Technol. Biotechnol. doi:10.1002/jctb.5551], which has been published in final form at [doi:10.1002/jctb.5551]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.BACKGROUND: Compared with other methods, the stationary liquid mass balance method for oxygen uptake rate (OUR) determination offers advantages in terms of estimation accuracy and reduction of stress. However, the need for sophisticated instrumentation, like mass flow controllers and gas analysers, has historically limited wider implementation of such a method. In this paper, a new simplified method based on inexpensive valves for the continuous estimation of OUR in animal cell cultures is evaluated. The determination of OUR values is based on accurate operation of the dissolved oxygen (DO) control loop and monitoring of its internal variables. RESULTS: The method developed was tested empirically in 2¿L bioreactor HEK293 batch cultures. OUR profiles obtained by a dynamic method, global mass balance method and the developed simplified method were monitored and compared. The results show how OUR profile obtained with the proposed method better follows the off-line cell density determination. The OUR estimation frequency was also increased, improving the method capabilities and applications. The theoretical rationale of the method was extended to the sensitivity analysis which was analytically and numerically approached. CONCLUSIONS: The results showed the proposed method to be not only cheap, but also a reliable alternative to monitor the metabolic activity in bioreactors in many biotechnological processes, being a useful tool for high cell density culture strategies implementation based on OUR monitoring.Peer ReviewedPostprint (published version

Non-stationary Stochastic Optimization

We consider a non-stationary variant of a sequential stochastic optimization problem, in which the underlying cost functions may change along the horizon. We propose a measure, termed variation budget, that controls the extent of said change, and study how restrictions on this budget impact achievable performance. We identify sharp conditions under which it is possible to achieve long-run-average optimality and more refined performance measures such as rate optimality that fully characterize the complexity of such problems. In doing so, we also establish a strong connection between two rather disparate strands of literature: adversarial online convex optimization; and the more traditional stochastic approximation paradigm (couched in a non-stationary setting). This connection is the key to deriving well performing policies in the latter, by leveraging structure of optimal policies in the former. Finally, tight bounds on the minimax regret allow us to quantify the "price of non-stationarity," which mathematically captures the added complexity embedded in a temporally changing environment versus a stationary one

Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networks

We apply a multilayer perceptron machine learning (ML) regression approach to infer electromagnetic (EM) duct heights within the marine atmospheric boundary layer (MABL) using sparsely sampled EM propagation data obtained within a bistatic context. This paper explains the rationale behind the selection of the ML network architecture, along with other model hyperparameters, in an effort to demystify the process of arriving at a useful ML model. The resulting speed of our ML predictions of EM duct heights, using sparse data measurements within MABL, indicates the suitability of the proposed method for real-time applications.Comment: 13 pages, 7 figure