Search Results for author: Holger H. Hoos

Found 15 papers, 4 papers with code

Competitions in AI -- Robustly Ranking Solvers Using Statistical Resampling

no code implementations9 Aug 2023 Chris Fawcett, Mauro Vallati, Holger H. Hoos, Alfonso E. Gerevini

To address this problem, we introduce a novel approach to statistically meaningful analysis of competition results based on resampling performance data.

Frugal Machine Learning

no code implementations5 Nov 2021 Mikhail Evchenko, Joaquin Vanschoren, Holger H. Hoos, Marc Schoenauer, Michèle Sebag

Machine learning, already at the core of increasingly many systems and applications, is set to become even more ubiquitous with the rapid rise of wearable devices and the Internet of Things.

Activity Recognition BIG-bench Machine Learning

Automating Data Science: Prospects and Challenges

no code implementations12 May 2021 Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams

Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process.

AutoML BIG-bench Machine Learning

Automated Configuration of Negotiation Strategies

no code implementations31 Mar 2020 Bram M. Renting, Holger H. Hoos, Catholijn M. Jonker

By empowering automated negotiating agents using automated algorithm configuration, we obtain a flexible negotiation agent that can be configured automatically for a rich space of opponents and negotiation scenarios.

Improving the Performance of Stochastic Local Search for Maximum Vertex Weight Clique Problem Using Programming by Optimization

no code implementations27 Feb 2020 Yi Chu, Chuan Luo, Holger H. Hoos, QIngwei Lin, Haihang You

The maximum vertex weight clique problem (MVWCP) is an important generalization of the maximum clique problem (MCP) that has a wide range of real-world applications.

Automated Algorithm Selection: Survey and Perspectives

no code implementations28 Nov 2018 Pascal Kerschke, Holger H. Hoos, Frank Neumann, Heike Trautmann

The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning.


LSQ++: Lower running time and higher recall in multi-codebook quantization

1 code implementation ECCV 2018 Julieta Martinez, Shobhit Zakhmi, Holger H. Hoos, James J. Little

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.


Hot-Rodding the Browser Engine: Automatic Configuration of JavaScript Compilers

no code implementations11 Jul 2017 Chris Fawcett, Lars Kotthoff, Holger H. Hoos

Modern software systems in many application areas offer to the user a multitude of parameters, switches and other customisation hooks.

Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates

no code implementations30 Mar 2017 Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown

In our experiments, we construct and evaluate surrogate benchmarks for hyperparameter optimization as well as for AC problems that involve performance optimization of solvers for hard combinatorial problems, drawing training data from the runs of existing AC procedures.

Benchmarking Hyperparameter Optimization

Stacked Quantizers for Compositional Vector Compression

2 code implementations8 Nov 2014 Julieta Martinez, Holger H. Hoos, James J. Little

Recently, Babenko and Lempitsky introduced Additive Quantization (AQ), a generalization of Product Quantization (PQ) where a non-independent set of codebooks is used to compress vectors into small binary codes.


ParamILS: An Automatic Algorithm Configuration Framework

no code implementations15 Jan 2014 Frank Hutter, Thomas Stuetzle, Kevin Leyton-Brown, Holger H. Hoos

The identification of performance-optimizing parameter settings is an important part of the development and application of algorithms.

Hyperparameter Optimization

Algorithm Runtime Prediction: Methods & Evaluation

no code implementations5 Nov 2012 Frank Hutter, Lin Xu, Holger H. Hoos, Kevin Leyton-Brown

We also comprehensively describe new and existing features for predicting algorithm runtime for propositional satisfiability (SAT), travelling salesperson (TSP) and mixed integer programming (MIP) problems.

Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms

1 code implementation18 Aug 2012 Chris Thornton, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown

Many different machine learning algorithms exist; taking into account each algorithm's hyperparameters, there is a staggeringly large number of possible alternatives overall.

Bayesian Optimization BIG-bench Machine Learning +3

Sequential Model-Based Optimization for General Algorithm Configuration

1 code implementation LION 2011 2011 Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown

State-of-the-art algorithms for hard computational problems often expose many parameters that can be modified to improve empirical performance.

Hyperparameter Optimization

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