no code implementations • 9 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.
no code implementations • 15 May 2023 • Devis Tuia, Konrad Schindler, Begüm Demir, Gustau Camps-Valls, Xiao Xiang Zhu, Mrinalini Kochupillai, Sašo Džeroski, Jan N. van Rijn, Holger H. Hoos, Fabio Del Frate, Mihai Datcu, Jorge-Arnulfo Quiané-Ruiz, Volker Markl, Bertrand Le Saux, Rochelle Schneider
Earth observation (EO) is a prime instrument for monitoring land and ocean processes, studying the dynamics at work, and taking the pulse of our planet.
no code implementations • 5 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.
no code implementations • 12 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.
no code implementations • 31 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.
no code implementations • 27 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.
no code implementations • 28 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.
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.
no code implementations • 11 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.
no code implementations • 30 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.
2 code implementations • 8 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.
no code implementations • 15 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.
no code implementations • 5 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.
1 code implementation • 18 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.
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.