Analytical/ML Mixed Approach for Concurrency Regulation in Software Transactional Memory

In this article we exploit a combination of analytical and Machine Learning (ML) techniques in order to build a performance model allowing to dynamically tune the level of concurrency of applications based on Software Transactional Memory (STM). Our mixed approach has the advantage of reducing the training time of pure machine learning methods, and avoiding approximation errors typically affecting pure analytical approaches. Hence it allows very fast construction of highly reliable performance models, which can be promptly and effectively exploited for optimizing actual application runs. We also present a real implementation of a concurrency regulation architecture, based on the mixed modeling approach, which has been integrated with the open source Tiny STM package, together with experimental data related to runs of applications taken from the STAMP benchmark suite demonstrating the effectiveness of our proposal. © 2014 IEEE

Resource Constrained Structured Prediction

We study the problem of structured prediction under test-time budget constraints. We propose a novel approach applicable to a wide range of structured prediction problems in computer vision and natural language processing. Our approach seeks to adaptively generate computationally costly features during test-time in order to reduce the computational cost of prediction while maintaining prediction performance. We show that training the adaptive feature generation system can be reduced to a series of structured learning problems, resulting in efficient training using existing structured learning algorithms. This framework provides theoretical justification for several existing heuristic approaches found in literature. We evaluate our proposed adaptive system on two structured prediction tasks, optical character recognition (OCR) and dependency parsing and show strong performance in reduction of the feature costs without degrading accuracy

Self-Learning Hot Data Prediction: Where Echo State Network Meets NAND Flash Memories

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Well understanding the access behavior of hot data is significant for NAND flash memory due to its crucial impact on the efficiency of garbage collection (GC) and wear leveling (WL), which respectively dominate the performance and life span of SSD. Generally, both GC and WL rely greatly on the recognition accuracy of hot data identification (HDI). However, in this paper, the first time we propose a novel concept of hot data prediction (HDP), where the conventional HDI becomes unnecessary. First, we develop a hybrid optimized echo state network (HOESN), where sufficiently unbiased and continuously shrunk output weights are learnt by a sparse regression based on L2 and L1/2 regularization. Second, quantum-behaved particle swarm optimization (QPSO) is employed to compute reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient and sparsity degree) for further improving prediction accuracy and reliability. Third, in the test on a chaotic benchmark (Rossler), the HOESN performs better than those of six recent state-of-the-art methods. Finally, simulation results about six typical metrics tested on five real disk workloads and on-chip experiment outcomes verified from an actual SSD prototype indicate that our HOESN-based HDP can reliably promote the access performance and endurance of NAND flash memories.Peer reviewe

Aplicaciones en Economía del Aprendizaje Automático

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, leída el 06-05-2022This Thesis examines problems in economics from a Machine Learning perspective. Emphasisis given on the interpretability of Machine Learning algorithms as opposed to blackbox predictions models. Chapter 1 provides an overview of the terminology and Machine Learning methods used throughout this Thesis. This chapter aims to build a roadmap from simple decision tree models to more advanced ensemble boosted algorithms. Other Machine Learning models are also explained. A discussion of the advances in Machine Learning in economics is also provided along with some of the pitfalls that Machine Learning faces. Moreover, an example of how Shapley values from coalition game theory are used to help infer inference from the Machine Learning models' predictions. Chapter 2 analyses the problem of bankruptcy prediction in the Spanish economy and how Machine Learning, not only provides more predictive accuracy, but can also provide adierent interpretation of the results that traditional econometric models cannot. Several financial ratios are constructed and passed to a series of Machine Learning algorithms. Case studies are provided which may aid in better decision-making from financial institutions. A section containing supplementary material based on further analysis is also provided...Este Tesis examina problemas en economía desde la perspectiva de Aprendizaje Mecánico. Se hace hincapié en la interpretabilidad de los algoritmos de Aprendizaje Mecánico en lugar de modelos de predicción de black-box. Capítulo 1 Proporciona el resumen de la terminología y los métodos de Aprendizaje Mecánico utilizados a lo largo de esta tesis. El objetivo de este capítulo es construir la trayectoria desde un simple árbol de decisión hasta algoritmos impulsados por conjuntos más avanzados. También se explican otros modelos de Machine Learning. Asimismo, se proporciona una discusión de los avances en el Aprendizaje Mecánico en economía junto con algunos de los escollos que enfrenta el aprendizaje automático. Además, un ejemplo sobre cómo se utilizan los valores de Shapley de coalición de teoría de juegos y muestran cómo se puede tomar inferencia de los modelos de predicción. Capítulo 2 Analiza el problema de la predicción de quiebra en la economía española y cómo Aprendizaje Mecánico, no sólo proporciona una mayor precisión predictiva, sino que también puede proporcionar una interpretación diferente de los resultados en la que los modelos econométricos tradicionales no pueden. Se construyen una serie de ratios financieros y se pasan a una serie de algoritmos de Aprendizaje Mecánico. Se proporcionan estudios de casos que pueden ayudar a mejorar la toma de decisiones por parte de las instituciones financieras. También se proporciona una sección que contiene material complementario basado en un análisis más detallado...Fac. de Ciencias Económicas y EmpresarialesTRUEunpu