133 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Stability-penalty-adaptive Follow-the-regularized-leader: Sparsity, Game-dependency, and Best-of-both-worlds
Adaptivity to the difficulties of a problem is a key property in sequential
decision-making problems to broaden the applicability of algorithms.
Follow-the-Regularized-Leader (FTRL) has recently emerged as one of the most
promising approaches for obtaining various types of adaptivity in bandit
problems. Aiming to further generalize this adaptivity, we develop a generic
adaptive learning rate, called Stability-Penalty-Adaptive (SPA) learning rate
for FTRL. This learning rate yields a regret bound jointly depending on
stability and penalty of the algorithm, into which the regret of FTRL is
typically decomposed. With this result, we establish several algorithms with
three types of adaptivity: sparsity, game-dependency, and Best-of-Both-Worlds
(BOBW). Sparsity frequently appears in real-world problems. However, existing
sparse multi-armed bandit algorithms with -arms assume that the sparsity
level is known in advance, which is often not the case in real-world
scenarios. To address this problem, with the help of the new learning rate
framework, we establish -agnostic algorithms with regret bounds of
in the adversarial regime for rounds, which matches
the existing lower bound up to a logarithmic factor. Meanwhile, BOBW algorithms
aim to achieve a near-optimal regret in both the stochastic and adversarial
regimes. Leveraging the new adaptive learning rate framework and a novel
analysis to bound the variation in FTRL output in response to changes in a
regularizer, we establish the first BOBW algorithm with a sparsity-dependent
bound. Additionally, we explore partial monitoring and demonstrate that the
proposed learning rate framework allows us to achieve a game-dependent bound
and the BOBW simultaneously.Comment: 30 page
Advanced Battery Technologies: New Applications and Management Systems
In recent years, lithium-ion batteries (LIBs) have been increasingly contributing to the development of novel engineering systems with energy storage requirements. LIBs are playing an essential role in our society, as they are being used in a wide variety of applications, ranging from consumer electronics, electric mobility, renewable energy storage, biomedical applications, or aerospace systems. Despite the remarkable achievements and applicability of LIBs, there are several features within this technology that require further research and improvements. In this book, a collection of 10 original research papers addresses some of those key features, including: battery testing methodologies, state of charge and state of health monitoring, and system-level power electronics applications. One key aspect to emphasize when it comes to this book is the multidisciplinary nature of the selected papers. The presented research was developed at university departments, institutes and organizations of different disciplines, including Electrical Engineering, Control Engineering, Computer Science or Material Science, to name a few examples. The overall result is a book that represents a coherent collection of multidisciplinary works within the prominent field of LIBs
Promoting Statistical Practice and Collaboration in Developing Countries
"Rarely, but just often enough to rebuild hope, something happens to confound my pessimism about the recent unprecedented happenings in the world. This book is the most recent instance, and I think that all its readers will join me in rejoicing at the good it seeks to do. It is an example of the kind of international comity and collaboration that we could and should undertake to solve various societal problems. This book is a beautiful example of the power of the possible. [It] provides a blueprint for how the LISA 2020 model can be replicated in other fields. Civil engineers, or accountants, or nurses, or any other profession could follow this outline to share expertise and build capacity and promote progress in other countries. It also contains some tutorials for statistical literacy across several fields. The details would change, of course, but ideas are durable, and the generalizations seem pretty straightforward. This book shows every other profession where and how to stand in order to move the world. I urge every researcher to get a copy!" —David Banks from the Foreword Promoting Statistical Practice and Collaboration in Developing Countries provides new insights into the current issues and opportunities in international statistics education, statistical consulting, and collaboration, particularly in developing countries around the world. The book addresses the topics discussed in individual chapters from the perspectives of the historical context, the present state, and future directions of statistical training and practice, so that readers may fully understand the challenges and opportunities in the field of statistics and data science, especially in developing countries. Features • Reference point on statistical practice in developing countries for researchers, scholars, students, and practitioners • Comprehensive source of state-of-the-art knowledge on creating statistical collaboration laboratories within the field of data science and statistics • Collection of innovative statistical teaching and learning techniques in developing countries Each chapter consists of independent case study contributions on a particular theme that are developed with a common structure and format. The common goal across the chapters is to enhance the exchange of diverse educational and action-oriented information among our intended audiences, which include practitioners, researchers, students, and statistics educators in developing countries
Survey of quantitative investment strategies in the Russian stock market : Special interest in tactical asset allocation
Russia’s financial markets have been an uncharted area when it comes to exploring the performance of investment strategies based on modern portfolio theory. In this thesis, we focus on the country’s stock market and study whether profitable investments can be made while at the same time taking uncertainties, risks, and dependencies into account. We also pay particular interest in tactical asset allocation. The benefit of this approach is that we can utilize time series forecasting methods to produce trading signals in addition to optimization methods.
We use two datasets in our empirical applications. The first one consists of nine sectoral indices covering the period from 2008 to 2017, and the other includes altogether 42 stocks listed on the Moscow Exchange covering the years 2011 – 2017. The strategies considered have been divided into five sections. In the first part, we study classical and robust mean-risk portfolios and the modeling of transaction costs. We find that the expected return should be maximized per unit expected shortfall while simultaneously requiring that each asset contributes equally to the portfolio’s tail risk. Secondly, we show that using robust covariance estimators can improve the risk-adjusted returns of minimum variance portfolios. Thirdly, we note that robust optimization techniques are best suited for conservative investors due to the low volatility allocations they produce.
In the second part, we employ statistical factor models to estimate higher-order comoments and demonstrate the benefit of the proposed method in constructing risk-optimal and expected utility-maximizing portfolios. In the third part, we utilize the Almgren–Chriss framework and sort the expected returns according to the assumed momentum anomaly. We discover that this method produces stable allocations performing exceptionally well in the market upturn. In the fourth part, we show that forecasts produced by VECM and GARCH models can be used profitably in optimizations based on the Black–Litterman, copula opinion pooling, and entropy pooling models. In the final part, we develop a wealth protection strategy capable of timing market changes thanks to the return predictions based on an ARIMA model.
Therefore, it can be stated that it has been possible to make safe and profitable investments in the Russian stock market even when reasonable transaction costs have been taken into account. We also argue that market inefficiencies could have been exploited by structuring statistical arbitrage and other tactical asset allocation-related strategies.Venäjän rahoitusmarkkinat ovat olleet kartoittamatonta aluetta tutkittaessa moderniin portfolioteoriaan pohjautuvien sijoitusstrategioiden käyttäytymistä. Tässä tutkielmassa keskitymme maan osakemarkkinoihin ja tarkastelemme, voidaanko taloudellisesti kannattavia sijoituksia tehdä otettaessa samalla huomioon epävarmuudet, riskit ja riippuvuudet. Kiinnitämme erityistä huomiota myös taktiseen varojen kohdentamiseen. Tämän lähestymistavan etuna on, että optimointimenetelmien lisäksi voimme hyödyntää aikasarjaennustamisen menetelmiä kaupankäyntisignaalien tuottamiseksi.
Empiirisissä sovelluksissa käytämme kahta data-aineistoa. Ensimmäinen koostuu yhdeksästä teollisuusindeksistä kattaen ajanjakson 2008–2017, ja toinen sisältää 42 Moskovan pörssiin listattua osaketta kattaen vuodet 2011–2017. Tarkasteltavat strategiat on puolestaan jaoteltu viiteen osioon. Ensimmäisessä osassa tarkastelemme klassisia ja robusteja riski-tuotto -portfolioita sekä kaupankäyntikustannusten mallintamista. Havaitsemme, että odotettua tuottoa on syytä maksimoida suhteessa odotettuun vajeeseen edellyttäen samalla, että jokainen osake lisää sijoitussalkun häntäriskiä yhtä suurella osuudella. Toiseksi osoitamme, että minimivarianssiportfolioiden riskikorjattuja tuottoja voidaan parantaa robusteilla kovarianssiestimaattoreilla. Kolmanneksi toteamme robustien optimointitekniikoiden soveltuvan parhaiten konservatiivisille sijoittajille niiden tuottamien matalan volatiliteetin allokaatioiden ansiosta.
Toisessa osassa hyödynnämme tilastollisia faktorimalleja korkeampien yhteismomenttien estimoinnissa ja havainnollistamme ehdotetun metodin hyödyllisyyttä riskioptimaalisten sekä odotettua hyötyä maksimoivien salkkujen rakentamisessa. Kolmannessa osassa käytämme Almgren–Chrissin viitekehystä ja asetamme odotetut tuotot suuruusjärjestykseen oletetun momentum-anomalian mukaisesti. Havaitsemme, että menetelmä tuottaa vakaita allokaatioita menestyen erityisen hyvin noususuhdanteessa. Neljännessä osassa osoitamme, että VECM- että GARCH-mallien tuottamia ennusteita voidaan hyödyntää kannattavasti niin Black–Littermanin malliin kuin kopulanäkemysten ja entropian poolaukseenkin perustuvissa optimoinneissa. Viimeisessä osassa laadimme varallisuuden suojausstrategian, joka kykenee ajoittamaan markkinoiden muutoksia ARIMA-malliin perustuvien tuottoennusteiden ansiosta.
Voidaan siis todeta, että Venäjän osakemarkkinoilla on ollut mahdollista tehdä turvallisia ja tuottavia sijoituksia myös silloin kun kohtuulliset kaupankäyntikustannukset on huomioitu. Toiseksi väitämme, että markkinoiden tehottomuutta on voitu hyödyntää suunnittelemalla tilastolliseen arbitraasiin ja muihin taktiseen varojen allokointiin pohjautuvia strategioita
Comparative Analysis of Student Learning: Technical, Methodological and Result Assessing of PISA-OECD and INVALSI-Italian Systems .
PISA is the most extensive international survey promoted by the OECD in the field of education, which measures the skills of fifteen-year-old students from more than 80 participating countries every three years. INVALSI are written tests carried out every year by all Italian students in some key moments of the school cycle, to evaluate the levels of some fundamental skills in Italian, Mathematics and English. Our comparison is made up to 2018, the last year of the PISA-OECD survey, even if INVALSI was carried out for the last edition in 2022. Our analysis focuses attention on the common part of the reference populations, which are the 15-year-old students of the 2nd class of secondary schools of II degree, where both
sources give a similar picture of the students
Hierarchical Learning Algorithms for Multi-scale Expert Problems
In this paper, we study the multi-scale expert problem, where the rewards of different experts vary in different reward ranges. The performance of existing algorithms for the multi-scale expert problem degrades linearly proportional to the maximum reward range of any expert or the best expert and does not capture the non-uniform heterogeneity in the reward ranges among experts. In this work, we propose learning algorithms that construct a hierarchical tree structure based on the heterogeneity of the reward range of experts and then determine differentiated learning rates based on the reward upper bounds and cumulative empirical feedback over time. We then characterize the regret of the proposed algorithms as a function of non-uniform reward ranges and show that their regrets outperform prior algorithms when the rewards of experts exhibit non-uniform heterogeneity in different ranges. Last, our numerical experiments verify our algorithms' efficiency compared to previous algorithms
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