87 research outputs found
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning
Recently, Meta-Black-Box Optimization with Reinforcement Learning
(MetaBBO-RL) has showcased the power of leveraging RL at the meta-level to
mitigate manual fine-tuning of low-level black-box optimizers. However, this
field is hindered by the lack of a unified benchmark. To fill this gap, we
introduce MetaBox, the first benchmark platform expressly tailored for
developing and evaluating MetaBBO-RL methods. MetaBox offers a flexible
algorithmic template that allows users to effortlessly implement their unique
designs within the platform. Moreover, it provides a broad spectrum of over 300
problem instances, collected from synthetic to realistic scenarios, and an
extensive library of 19 baseline methods, including both traditional black-box
optimizers and recent MetaBBO-RL methods. Besides, MetaBox introduces three
standardized performance metrics, enabling a more thorough assessment of the
methods. In a bid to illustrate the utility of MetaBox for facilitating
rigorous evaluation and in-depth analysis, we carry out a wide-ranging
benchmarking study on existing MetaBBO-RL methods. Our MetaBox is open-source
and accessible at: https://github.com/GMC-DRL/MetaBox.Comment: Accepted at NuerIPS 202
Using Automated Algorithm Configuration for Parameter Control
Dynamic Algorithm Configuration (DAC) tackles the question of how to
automatically learn policies to control parameters of algorithms in a
data-driven fashion. This question has received considerable attention from the
evolutionary community in recent years. Having a good benchmark collection to
gain structural understanding on the effectiveness and limitations of different
solution methods for DAC is therefore strongly desirable. Following recent work
on proposing DAC benchmarks with well-understood theoretical properties and
ground truth information, in this work, we suggest as a new DAC benchmark the
controlling of the key parameter in the
~Genetic Algorithm for solving OneMax problems. We
conduct a study on how to solve the DAC problem via the use of (static)
automated algorithm configuration on the benchmark, and propose techniques to
significantly improve the performance of the approach. Our approach is able to
consistently outperform the default parameter control policy of the benchmark
derived from previous theoretical work on sufficiently large problem sizes. We
also present new findings on the landscape of the parameter-control search
policies and propose methods to compute stronger baselines for the benchmark
via numerical approximations of the true optimal policies.Comment: To appear in the Proc. of the ACM/SIGEVO Conference on Foundations of
Genetic Algorithms (FOGA XVII
Democratizing machine learning
Modelle des maschinellen Lernens sind zunehmend in der Gesellschaft verankert, oft in Form von automatisierten Entscheidungsprozessen. Ein wesentlicher Grund dafür ist die verbesserte Zugänglichkeit von Daten, aber auch von Toolkits für maschinelles Lernen, die den Zugang zu Methoden des maschinellen Lernens für Nicht-Experten ermöglichen.
Diese Arbeit umfasst mehrere Beiträge zur Demokratisierung des Zugangs zum maschinellem Lernen, mit dem Ziel, einem breiterem Publikum Zugang zu diesen Technologien zu er- möglichen. Die Beiträge in diesem Manuskript stammen aus mehreren Bereichen innerhalb dieses weiten Gebiets. Ein großer Teil ist dem Bereich des automatisierten maschinellen Lernens (AutoML) und der Hyperparameter-Optimierung gewidmet, mit dem Ziel, die oft mühsame Aufgabe, ein optimales Vorhersagemodell für einen gegebenen Datensatz zu finden, zu vereinfachen. Dieser Prozess besteht meist darin ein für vom Benutzer vorgegebene Leistungsmetrik(en) optimales Modell zu finden. Oft kann dieser Prozess durch Lernen aus vorhergehenden Experimenten verbessert oder beschleunigt werden.
In dieser Arbeit werden drei solcher Methoden vorgestellt, die entweder darauf abzielen, eine feste Menge möglicher Hyperparameterkonfigurationen zu erhalten, die wahrscheinlich gute Lösungen für jeden neuen Datensatz enthalten, oder Eigenschaften der Datensätze zu nutzen, um neue Konfigurationen vorzuschlagen.
Darüber hinaus wird eine Sammlung solcher erforderlichen Metadaten zu den Experimenten vorgestellt, und es wird gezeigt, wie solche Metadaten für die Entwicklung und als Testumgebung für neue Hyperparameter- Optimierungsmethoden verwendet werden können. Die weite Verbreitung von ML-Modellen in vielen Bereichen der Gesellschaft erfordert gleichzeitig eine genauere Untersuchung der Art und Weise, wie aus Modellen abgeleitete automatisierte Entscheidungen die Gesellschaft formen, und ob sie möglicherweise Individuen oder einzelne Bevölkerungsgruppen benachteiligen. In dieser Arbeit wird daher ein AutoML-Tool vorgestellt, das es ermöglicht, solche Überlegungen in die Suche nach einem optimalen Modell miteinzubeziehen. Diese Forderung nach Fairness wirft gleichzeitig die Frage auf, ob die Fairness eines Modells zuverlässig geschätzt werden kann, was in einem weiteren Beitrag in dieser Arbeit untersucht wird. Da der Zugang zu Methoden des maschinellen Lernens auch stark vom Zugang zu Software und Toolboxen abhängt, sind mehrere Beiträge in Form von Software Teil dieser Arbeit. Das R-Paket mlr3pipelines ermöglicht die Einbettung von Modellen in sogenan- nte Machine Learning Pipelines, die Vor- und Nachverarbeitungsschritte enthalten, die im maschinellen Lernen und AutoML häufig benötigt werden. Das mlr3fairness R-Paket hingegen ermöglicht es dem Benutzer, Modelle auf potentielle Benachteiligung hin zu über- prüfen und diese durch verschiedene Techniken zu reduzieren. Eine dieser Techniken, multi-calibration wurde darüberhinaus als seperate Software veröffentlicht.Machine learning artifacts are increasingly embedded in society, often in the form of automated decision-making processes. One major reason for this, along with methodological improvements, is the increasing accessibility of data but also machine learning toolkits that enable access to machine learning methodology for non-experts. The core focus of this thesis is exactly this – democratizing access to machine learning in order to enable a wider audience to benefit from its potential.
Contributions in this manuscript stem from several different areas within this broader area. A major section is dedicated to the field of automated machine learning (AutoML) with the goal to abstract away the tedious task of obtaining an optimal predictive model for a given dataset. This process mostly consists of finding said optimal model, often through hyperparameter optimization, while the user in turn only selects the appropriate performance metric(s) and validates the resulting models. This process can be improved or sped up by learning from previous experiments.
Three such methods one with the goal to obtain a fixed set of possible hyperparameter configurations that likely contain good solutions for any new dataset and two using dataset characteristics to propose new configurations are presented in this thesis.
It furthermore presents a collection of required experiment metadata and how such meta-data can be used for the development and as a test bed for new hyperparameter optimization methods. The pervasion of models derived from ML in many aspects of society simultaneously calls for increased scrutiny with respect to how such models shape society and the eventual biases they exhibit. Therefore, this thesis presents an AutoML tool that allows incorporating fairness considerations into the search for an optimal model. This requirement for fairness simultaneously poses the question of whether we can reliably estimate a model’s fairness, which is studied in a further contribution in this thesis. Since access to machine learning methods also heavily depends on access to software and toolboxes, several contributions in the form of software are part of this thesis. The mlr3pipelines R package allows for embedding models in so-called machine learning pipelines that include pre- and postprocessing steps often required in machine learning and AutoML. The mlr3fairness R package on the other hand enables users to audit models for potential biases as well as reduce those biases through different debiasing techniques. One such technique, multi-calibration is published as a separate software package, mcboost
Safe and Sound: Proceedings of the 27th Annual International Conference on Auditory Display
Complete proceedings of the 27th International Conference on Auditory Display (ICAD2022), June 24-27. Online virtual conference
Ecology and Conservation of Parrots in Their Native and Non-Native Ranges
This book focuses on parrots, which are among the most fascinating, attractive, and threatened birds, combining and synthesizing recent research on the biology, ecology, and conservation of both native and non-native parrot populations across the world
Food for All
This book is a historical review of international food and agriculture since the founding of the international organizations following the Second World War, including the World Bank and the Food and Agriculture Organization of the United Nations (FAO), the World Food Programme (WFP) and into the 1970s, when CGIAR was established and the International Fund for Agricultural Development (IFAD) was created to recycle petrodollars. The book concurrently focuses on the structural transformation of developing countries in Asia and Africa, with some making great strides in small farmer development and in achieving structural transformation of their economies. Some have also achieved Sustainable Development Goals (SDGs), particularly SDG2, but most have not. Not only are some countries, particularly in South Asia and sub-Saharan Africa, lagging behind, but they face new challenges of climate change, competition from emerging countries, population pressure, urbanization, environmental decay, dietary transition, and now pandemics. Lagging developing countries need huge investments in human capital, and physical and institutional infrastructure, to take advantage of rapid change in technologies, but the role of international assistance in financial transfers has diminished. The COVID-19 pandemic has not only set many poorer countries back but starkly revealed the weaknesses of past strategies. Transformative changes are needed in developing countries with international cooperation to achieve better outcomes. Will the change in US leadership bring new opportunities for multilateral cooperation
Evaluation of the ingestive behaviour of the dairy cow under two systems of rotation with slope
The ingestive behaviour of grazing animals is modulated by the vegetation characteristics, topography and the type of stocking method. This research was carried out in 2019, at the Rumipamba CADER-UCE. It aimed to evaluate the impact of two contrasting stocking methods of dairy cows grazing a pasture with an average of slope >8.5%. Four dairy cows were set to graze a 0.4 ha paddock for 5 days for continuous stocking methods, while for the electric fence
methods the dairy cows were restricted to 0.2 ha and the fence was moved uphill every 3 hours, repeating this process four times a day. Cow were equipped with activity sensors for 12 h per day. The whole procedure was repeated 2 times after realizing an equalization cuts and both paddocks, a rest time of 30 days and a random reassignment of paddocks to one of the treatments. The cows showed a difference in terms of the percentage of grazing P=0.0072,
being higher with the electric fence (55% of the measurement time). From rising-plate-meter estimates of available biomass along the grazing periods, we calculated despite similar forage allowances (electric fence = 48.06 kg DM/cow/d and continuous = 48.21 DM/cow/d) a higher forage intake was obtained in the electric fence treatment (17.5 kg DM/cow/d) compared the continuous stocking (15.7 kg DM/cow/d) (P=0.006). In terms of milk production animals
grazing under the differences electrical fence stocking method tended (P=0.0985) to produce more milk (17.39 kg/d) than those grazing in the continuous system (15.16 kg/d) due to the influence of the slope (P=0.05), while for milk quality the protein content was higher for the electric fence (33.7 g/l) than the continuous method (30.5 g/l) (P=0.039). None of the other milk properties differed between methods (P>0.05)
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