The replacement bootstrap for dependent data

Sani, Amir; Lazaric, Alessandro; Ryabko, Daniil

doi:10.1109/isit.2015.7282644

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Our goal here is to devise a meta-learning scheme that leverages multiple algorithms, each tailored to a specific environment, and dynamically chooses the optimal one. This idea is leveraged in online learning [30]; however, to the best of the author's knowledge, it is hitherto unexplored for resource allocation in RAN.…”

Section: Universal Policy Learning Through a Meta-learnermentioning

confidence: 99%

“…Furthermore, we draw ideas from the expert-learning paradigm [29] and enrich our policy decisions with a meta-learning scheme that combines our adversarial learning algorithm (that can be at times conservative) with any other algorithm (e.g., [7]) that can perform better on more easy scenarios, where the environment is known beforehand (e.g., static/stationary patterns), or changes slowly. In this way, our proposed meta-algorithm becomes both fast-learning and robust, obtaining the best of both worlds; an idea that has been used in online learning [30], but not to network management.…”

Section: Introductionmentioning

confidence: 99%

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Energy-Aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning

Kalntis

Iosifidis

2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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The design of Open Radio Access Network (O-RAN) compliant systems for configuring the virtualized Base Stations (vBSs) is of paramount importance for network operators. This task is challenging since optimizing the vBS scheduling procedure requires knowledge of parameters, which are erratic and demanding to obtain in advance. In this paper, we propose an online learning algorithm for balancing the performance and energy consumption of a vBS. This algorithm provides performance guarantees under unforeseeable conditions, such as non-stationary traffic and network state, and is oblivious to the vBS operation profile. We study the problem in its most general form and we prove that the proposed technique achieves sub-linear regret (i.e., zero average optimality gap) even in a fast-changing environment. By using real-world data and various trace-driven evaluations, our findings indicate savings of up to 74.3% in the power consumption of a vBS in comparison with state-of-the-art benchmarks.

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Section: Universal Policy Learning Through a Meta-learnermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy-Aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning

Kalntis

Iosifidis

2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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The replacement bootstrap for dependent data

Sani

Lazaric

Ryabko

2015

2015 IEEE International Symposium on Information Theory (ISIT)

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Applications that deal with time-series data often require evaluating complex statistics for which each time series is essentially one data point. When only a few time series are available, bootstrap methods are used to generate additional samples that can be used to evaluate empirically the statistic of interest. In this work a novel bootstrap method is proposed, which is shown to have some asymptotic consistency guarantees under the only assumption that the time series are stationary and ergodic. This contrasts previously available results that impose mixing or finite-memory assumptions on the data. Empirical evaluation on simulated and real data, using a practically relevant and complex extrema statistic is provided.

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Agent-Based Model Calibration Using Machine Learning Surrogates

2017

Self Cite

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Taking agent-based models (ABM) closer to the data is an open challenge. This paper explicitly tackles parameter space exploration and calibration of ABMs combining supervised machine-learning and intelligent sampling to build a surrogate meta-model. The proposed approach provides a fast and accurate approximation of model behaviour, dramatically reducing computation time. In that, our machine-learning surrogate facilitates large scale explorations of the parameter-space, while providing a powerful filter to gain insights into the complex functioning of agent-based models. The algorithm introduced in this paper merges model simulation and output analysis into a surrogate meta-model, which substantially ease ABM calibration. We successfully apply our approach to the Brock and Hommes (1998) asset pricing model and to the "Island" endogenous growth model (Fagiolo and Dosi, 2003). Performance is evaluated against a relatively large outof-sample set of parameter combinations, while employing different user-defined statistical tests for output analysis. The results demonstrate the capacity of machine learning surrogates to facilitate fast and precise exploration of agent-based models' behaviour over their often rugged parameter spaces. KEY WORDSAgent based model; calibration; machine learning; surrogate; meta-model. JELC15, C52, C63.

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The replacement bootstrap for dependent data

Cited by 3 publications

References 23 publications

Energy-Aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning

Energy-Aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning

The replacement bootstrap for dependent data

Agent-Based Model Calibration Using Machine Learning Surrogates

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