The tuning of learning algorithm parameters has become more and more important during the last years. With the fast growth of computational power and available memory databases have grown dramatically. This is very challenging for the tuning of parameters arising in machine learning, since the training can become very time-consuming for large datasets. For this reason efficient tuning methods are required, which are able to improve the predictions of the learning algorithms. In this thesis we incorporate model-assisted optimization techniques, for performing efficient optimization on noisy datasets with very limited budgets. Under this umbrella we also combine learning algorithms with methods for feature construction and selection. We propo...
Hyperparameter optimization is crucial for achieving peak performance with many machine learning alg...
Bayesian optimization has recently emerged in the machine learning community as a very effective aut...
Machine learning is a buzz word that has inundated popular culture in the last few years. This is a ...
This thesis explores one of the most fundamental questions in Machine Learning, namely, how should t...
While the amount of data that we are able to collect keeps growing, the use of Machine Learning and ...
The rapidly evolving landscape of multicore architectures makes the construction of efficient librar...
While the amount of data that we are able to collect keeps growing, the use of Machine Learning and ...
Training machine learning models requires users to select many tuning parameters. For example, a pop...
Hyper-parameters tuning is a key step to find the optimal machine learning parameters. Determining t...
The interplay between optimization and machine learning is one of the most important developments in...
This thesis addresses many open challenges in hyperparameter tuning of machine learning algorithms. ...
Hybrid models, which combine multiple machine learning algorithms or optimization techniques, have s...
Tuning hyperparameters of machine learning models is important for their performance. Bayesian optim...
Data-driven approaches to the design of control policies for robotic systems have the potential to r...
Recent years have witnessed huge advances in machine learning (ML) and its applications, especially ...
Hyperparameter optimization is crucial for achieving peak performance with many machine learning alg...
Bayesian optimization has recently emerged in the machine learning community as a very effective aut...
Machine learning is a buzz word that has inundated popular culture in the last few years. This is a ...
This thesis explores one of the most fundamental questions in Machine Learning, namely, how should t...
While the amount of data that we are able to collect keeps growing, the use of Machine Learning and ...
The rapidly evolving landscape of multicore architectures makes the construction of efficient librar...
While the amount of data that we are able to collect keeps growing, the use of Machine Learning and ...
Training machine learning models requires users to select many tuning parameters. For example, a pop...
Hyper-parameters tuning is a key step to find the optimal machine learning parameters. Determining t...
The interplay between optimization and machine learning is one of the most important developments in...
This thesis addresses many open challenges in hyperparameter tuning of machine learning algorithms. ...
Hybrid models, which combine multiple machine learning algorithms or optimization techniques, have s...
Tuning hyperparameters of machine learning models is important for their performance. Bayesian optim...
Data-driven approaches to the design of control policies for robotic systems have the potential to r...
Recent years have witnessed huge advances in machine learning (ML) and its applications, especially ...
Hyperparameter optimization is crucial for achieving peak performance with many machine learning alg...
Bayesian optimization has recently emerged in the machine learning community as a very effective aut...
Machine learning is a buzz word that has inundated popular culture in the last few years. This is a ...