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Chapter and Conference Paper
The 13th International Automated Negotiating Agent Competition Challenges and Results
An international competition for negotiating agents has been organized for years to facilitate research in agent-based negotiation and to encourage the design of negotiating agents that can operate in various ...
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Article
Open AccessSpeeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio
Despite their great success in recent years, neural networks have been found to be vulnerable to adversarial attacks. These attacks are often based on slight perturbations of given inputs that cause them to be...
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Article
Open AccessMultiETSC: automated machine learning for early time series classification
Early time series classification (EarlyTSC) involves the prediction of a class label based on partial observation of a given time series. Most EarlyTSC algorithms consider the trade-off between accuracy and ea...
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Chapter and Conference Paper
Semi-supervised Co-ensembling for AutoML
Automated machine learning (AutoML) is an increasingly popular approach for selecting learning algorithms and for configuring their hyperparameters in an effective, principled and fully automated way. So far, ...
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Chapter and Conference Paper
Hyper-parameter Optimization for Latent Spaces
We present an online optimization method for time-evolving data streams that can automatically adapt the hyper-parameters of an embedding model. More specifically, we employ the Nelder-Mead algorithm, which us...
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Article
Open AccessA survey on semi-supervised learning
Semi-supervised learning is the branch of machine learning concerned with using labelled as well as unlabelled data to perform certain learning tasks. Conceptually situated between supervised and unsupervised ...
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Chapter and Conference Paper
Configuration of a Dynamic MOLS Algorithm for Bi-objective Flowshop Scheduling
In this work, we propose a dynamic multi-objective local search (MOLS) algorithm whose parameters are modified while it is running and a protocol for automatically configuring this algorithm. Our approach appl...
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Chapter
Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA
Many different machine learning algorithms exist; taking into account each algorithm’s hyperparameters, there is a staggeringly large number of possible alternatives overall. We consider the problem of simulta...
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Article
On the empirical scaling of running time for finding optimal solutions to the TSP
We study the empirical scaling of the running time required by state-of-the-art exact and inexact TSP algorithms for finding optimal solutions to Euclidean TSP instances as a function of instance size. In part...
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Chapter and Conference Paper
Portfolio-Based Algorithm Selection for Circuit QBFs
Quantified Boolean Formulas (QBFs) are a generalization of propositional formulae that admits succinct encodings of verification and synthesis problems. Given that modern QBF solvers are based on different arc...
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Article
Efficient benchmarking of algorithm configurators via model-based surrogates
The optimization of algorithm (hyper-)parameters is crucial for achieving peak performance across a wide range of domains, ranging from deep neural networks to solvers for hard combinatorial problems. However,...
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Chapter and Conference Paper
LSQ++: Lower Running Time and Higher Recall in Multi-codebook Quantization
Multi-codebook quantization (MCQ) is the task of expressing a set of vectors as accurately as possible in terms of discrete entries in multiple bases. Work in MCQ is heavily focused on lowering quantization er...
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Chapter and Conference Paper
Automatically Configuring Multi-objective Local Search Using Multi-objective Optimisation
Automatic algorithm configuration (AAC) is becoming an increasingly crucial component in the design of high-performance solvers for many challenging combinatorial optimisation problems. This raises the questio...
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Article
Analysing differences between algorithm configurations through ablation
Developers of high-performance algorithms for hard computational problems increasingly take advantage of automated parameter tuning and algorithm configuration tools, and consequently often create solvers with...
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Chapter and Conference Paper
MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework
Automated algorithm configuration procedures play an increasingly important role in the development and application of algorithms for a wide range of computationally challenging problems. Until very recently, ...
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Chapter and Conference Paper
Quantifying the Similarity of Algorithm Configurations
A natural way of attacking a new, computationally challenging problem is to find a novel way of combining design elements introduced in existing algorithms. For example, this approach was made systematic in SATen...
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Chapter and Conference Paper
Revisiting Additive Quantization
We revisit Additive Quantization (AQ), an approach to vector quantization that uses multiple, full-dimensional, and non-orthogonal codebooks. Despite its elegant and simple formulation, AQ has failed to achiev...
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Chapter and Conference Paper
Solving Multi-codebook Quantization in the GPU
We focus on the problem of vector compression using multi-codebook quantization (MCQ). MCQ is a generalization of k-means where the centroids arise from the combinatorial sums of entries in multiple codebooks, an...
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Chapter and Conference Paper
The Impact of Automated Algorithm Configuration on the Scaling Behaviour of State-of-the-Art Inexact TSP Solvers
Automated algorithm configuration is a powerful and increasingly widely used approach for improving the performance of algorithms for computationally hard problems. In this work, we investigate the impact of a...
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Article
On the empirical time complexity of finding optimal solutions vs proving optimality for Euclidean TSP instances
We investigate the empirical performance of the long-standing state-of-the-art exact TSP solver Concorde on various classes of Euclidean TSP instances and show that, surprisingly, the time spent until the firs...