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ORCIDHolger H. Hoos
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- affiliation: Leiden University, LIACS, The Netherlands
- affiliation: University of British Columbia, Vancouver, Canada
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2020 – today
- 2023
[j71]Yi Chu
, Chuan Luo, Holger H. Hoos, Haihang You:
Improving the performance of stochastic local search for maximum vertex weight clique problem using programming by optimization. Expert Syst. Appl. 213(Part): 118913 (2023)- 2022
- [j70]Jaco Tetteroo, Mitra Baratchi, Holger H. Hoos:
Automated Machine Learning for COVID-19 Forecasting. IEEE Access 10: 94718-94737 (2022) - [j69]Anna L. D. Latour, Behrouz Babaki, Daniël Fokkinga, Marie Anastacio, Holger H. Hoos, Siegfried Nijssen:
Exact stochastic constraint optimisation with applications in network analysis. Artif. Intell. 304: 103650 (2022) - [j68]Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams:
Automating data science. Commun. ACM 65(3): 76-87 (2022) - [j67]Laurens Arp, Mitra Baratchi, Holger H. Hoos:
VPint: value propagation-based spatial interpolation. Data Min. Knowl. Discov. 36(5): 1647-1678 (2022) - [j66]Holger H. Hoos, Laetitia Jourdan, Marie-Eléonore Kessaci, Thomas Stützle, Nadarajen Veerapen:
Preface to the Special Cluster on Stochastic Local Search: Recent Developments and Trends. Int. Trans. Oper. Res. 29(5): 2735-2736 (2022) - [j65]Matthias König, Holger H. Hoos, Jan N. van Rijn:
Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio. Mach. Learn. 111(12): 4565-4584 (2022) - [j64]Koen van der Blom, Holger H. Hoos, Chuan Luo, Jeroen G. Rook:
Sparkle: Toward Accessible Meta-Algorithmics for Improving the State of the Art in Solving Challenging Problems. IEEE Trans. Evol. Comput. 26(6): 1351-1364 (2022) - [j63]Yasha Pushak, Holger H. Hoos:
AutoML Loss Landscapes. ACM Trans. Evol. Learn. Optim. 2(3): 10:1-10:30 (2022) - [c133]Reyhan Aydogan, Tim Baarslag, Katsuhide Fujita, Holger H. Hoos, Catholijn M. Jonker, Yasser Mohammad, Bram M. Renting:
The 13th International Automated Negotiating Agent Competition Challenges and Results. ACAN@IJCAI 2022: 87-101 - [c132]Bram M. Renting, Holger H. Hoos, Catholijn M. Jonker:
Automated Configuration and Usage of Strategy Portfolios Mixed-Motive Bargaining. AAMAS 2022: 1101-1109 - [c131]Damir Pulatov, Marie Anastacio, Lars Kotthoff, Holger H. Hoos:
Opening the Black Box: Automated Software Analysis for Algorithm Selection. AutoML 2022: 6/1-18 - [i29]Bram M. Renting, Holger H. Hoos, Catholijn M. Jonker:
Automated Configuration and Usage of Strategy Portfolios for Bargaining. CoRR abs/2212.10228 (2022) - 2021
- [j62]Gilles Ottervanger, Mitra Baratchi, Holger H. Hoos:
MultiETSC: automated machine learning for early time series classification. Data Min. Knowl. Discov. 35(6): 2602-2654 (2021) - [c130]Théo Matricon, Marie Anastacio, Nathanaël Fijalkow, Laurent Simon, Holger H. Hoos:
Statistical Comparison of Algorithm Performance Through Instance Selection. CP 2021: 43:1-43:21 - [c129]Alex Serban, Koen van der Blom, Holger H. Hoos, Joost Visser:
Practices for Engineering Trustworthy Machine Learning Applications. WAIN@ICSE 2021: 97-100 - [c128]Bruno Veloso, Luciano Caroprese, Matthias König, Sónia Teixeira, Giuseppe Manco, Holger H. Hoos, João Gama:
Hyper-parameter Optimization for Latent Spaces. ECML/PKDD (3) 2021: 249-264 - [c127]Zhendong Lei, Shaowei Cai, Chuan Luo, Holger H. Hoos:
Efficient Local Search for Pseudo Boolean Optimization. SAT 2021: 332-348 - [p9]Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown:
Automated Configuration and Selection of SAT Solvers. Handbook of Satisfiability 2021: 481-507 - [p8]Andreas Dengel, Oren Etzioni, Nicole DeCario, Holger H. Hoos, Li Fei-Fei, Junichi Tsujii, Paolo Traverso:
Next Big Challenges in Core AI Technology. Reflections on Artificial Intelligence for Humanity 2021: 90-115 - [i28]Alex Serban, Koen van der Blom, Holger H. Hoos, Joost Visser:
Practices for Engineering Trustworthy Machine Learning Applications. CoRR abs/2103.00964 (2021) - [i27]Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams:
Automating Data Science: Prospects and Challenges. CoRR abs/2105.05699 (2021) - [i26]Mikhail Evchenko, Joaquin Vanschoren, Holger H. Hoos, Marc Schoenauer, Michèle Sebag:
Frugal Machine Learning. CoRR abs/2111.03731 (2021) - 2020
- [j61]Sam Bayless, Nodir Kodirov, Syed M. Iqbal, Ivan Beschastnikh, Holger H. Hoos, Alan J. Hu:
Scalable constraint-based virtual data center allocation. Artif. Intell. 278 (2020) - [j60]Yasha Pushak, Zongxu Mu, Holger H. Hoos:
Empirical scaling analyzer: An automated system for empirical analysis of performance scaling. AI Commun. 33(2): 93-111 (2020) - [j59]Zeynep Akata, Dan Balliet, Maarten de Rijke, Frank Dignum, Virginia Dignum, Guszti Eiben, Antske Fokkens, Davide Grossi, Koen V. Hindriks, Holger H. Hoos, Hayley Hung, Catholijn M. Jonker, Christof Monz, Mark A. Neerincx, Frans A. Oliehoek, Henry Prakken, Stefan Schlobach, Linda C. van der Gaag, Frank van Harmelen, Herke van Hoof, Birna van Riemsdijk, Aimee van Wynsberghe, Rineke Verbrugge, Bart Verheij, Piek Vossen, Max Welling:
A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence. Computer 53(8): 18-28 (2020) - [j58]Holger H. Hoos, Laetitia Jourdan, Marie-Eléonore Kessaci, Thomas Stützle, Nadarajen Veerapen:
Special issue on "Stochastic Local Search: Recent developments and trends". Int. Trans. Oper. Res. 27(1): 697-698 (2020) - [j57]Holger H. Hoos, Laetitia Jourdan, Marie-Eléonore Kessaci, Thomas Stützle, Nadarajen Veerapen:
Special issue on "Stochastic Local Search: Recent developments and trends". Int. Trans. Oper. Res. 27(2): 1263-1264 (2020) - [j56]Holger H. Hoos, Laetitia Jourdan, Marie-Eléonore Kessaci, Thomas Stützle, Nadarajen Veerapen:
Special issue on "Stochastic Local Search: Recent developments and trends". Int. Trans. Oper. Res. 27(3): 1806-1807 (2020) - [j55]Holger H. Hoos, Laetitia Jourdan, Marie-Eléonore Kessaci, Thomas Stützle, Nadarajen Veerapen:
Special issue on "Stochastic Local Search: Recent developments and trends". Int. Trans. Oper. Res. 27(4): 2253-2254 (2020) - [j54]Holger H. Hoos, Laetitia Jourdan, Marie-Eléonore Kessaci, Thomas Stützle, Nadarajen Veerapen:
Special issue on "Stochastic Local Search: Recent developments and trends". Int. Trans. Oper. Res. 27(5): 2685-2686 (2020) - [j53]Lindsey Burggraaff, Eelke B. Lenselink, Willem Jespers, Jesper E. van Engelen, Brandon J. Bongers, Marina Gorostiola González, Rongfang Liu, Holger H. Hoos, Herman W. T. van Vlijmen, Adriaan P. IJzerman, Gerard J. P. van Westen:
Successive Statistical and Structure-Based Modeling to Identify Chemically Novel Kinase Inhibitors. J. Chem. Inf. Model. 60(9): 4283-4295 (2020) - [j52]Jesper E. van Engelen, Holger H. Hoos:
A survey on semi-supervised learning. Mach. Learn. 109(2): 373-440 (2020) - [c126]Bram M. Renting, Holger H. Hoos, Catholijn M. Jonker:
Automated Configuration of Negotiation Strategies. AAMAS 2020: 1116-1124 - [c125]Alex Serban, Koen van der Blom, Holger H. Hoos, Joost Visser:
Adoption and Effects of Software Engineering Best Practices in Machine Learning. ESEM 2020: 3:1-3:12 - [c124]Yasha Pushak, Holger H. Hoos:
Advanced statistical analysis of empirical performance scaling. GECCO 2020: 236-244 - [c123]Yasha Pushak, Holger H. Hoos:
Golden parameter search: exploiting structure to quickly configure parameters in parallel. GECCO 2020: 245-253 - [c122]Marie Anastacio, Holger H. Hoos:
Combining sequential model-based algorithm configuration with default-guided probabilistic sampling. GECCO Companion 2020: 301-302 - [c121]Sara Tari, Holger H. Hoos, Julie Jacques, Marie-Eléonore Kessaci, Laetitia Jourdan:
Automatic Configuration of a Multi-objective Local Search for Imbalanced Classification. PPSN (1) 2020: 65-77 - [c120]Marie Anastacio, Holger H. Hoos:
Model-Based Algorithm Configuration with Default-Guided Probabilistic Sampling. PPSN (1) 2020: 95-110 - [c119]Chuan Luo, Holger H. Hoos, Shaowei Cai:
PbO-CCSAT: Boosting Local Search for Satisfiability Using Programming by Optimisation. PPSN (1) 2020: 373-389 - [c118]Jesper E. van Engelen, Holger H. Hoos:
Semi-supervised Co-ensembling for AutoML. TAILOR 2020: 229-250 - [i25]Yi Chu, Chuan Luo, Holger H. Hoos, Qingwei Lin, Haihang You:
Improving the Performance of Stochastic Local Search for Maximum Vertex Weight Clique Problem Using Programming by Optimization. CoRR abs/2002.11909 (2020) - [i24]Bram M. Renting, Holger H. Hoos, Catholijn M. Jonker:
Automated Configuration of Negotiation Strategies. CoRR abs/2004.00094 (2020) - [i23]Alex Serban, Koen van der Blom, Holger H. Hoos, Joost Visser:
Adoption and Effects of Software Engineering Best Practices in Machine Learning. CoRR abs/2007.14130 (2020)
2010 – 2019
- 2019
- [j51]Holger H. Hoos, Frank Neumann, Heike Trautmann:
Foreword. Evol. Comput. 27(1): 1-2 (2019) - [j50]Pascal Kerschke, Holger H. Hoos, Frank Neumann, Heike Trautmann:
Automated Algorithm Selection: Survey and Perspectives. Evol. Comput. 27(1): 3-45 (2019) - [j49]Aymeric Blot, Marie-Eléonore Marmion, Laetitia Jourdan, Holger H. Hoos:
Automatic Configuration of Multi-Objective Local Search Algorithms for Permutation Problems. Evol. Comput. 27(1): 147-171 (2019) - [j48]Holger H. Hoos, Laetitia Jourdan, Marie-Eléonore Kessaci, Thomas Stützle, Nadarajen Veerapen:
Special issue on "Stochastic Local Search: Recent developments and trends". Int. Trans. Oper. Res. 26(6): 2580-2581 (2019) - [c117]Camille Pageau, Aymeric Blot, Holger H. Hoos, Marie-Eléonore Kessaci, Laetitia Jourdan:
Configuration of a Dynamic MOLS Algorithm for Bi-objective Flowshop Scheduling. EMO 2019: 565-577 - [c116]Chuan Luo, Holger H. Hoos, Shaowei Cai, Qingwei Lin, Hongyu Zhang, Dongmei Zhang:
Local Search with Efficient Automatic Configuration for Minimum Vertex Cover. IJCAI 2019: 1297-1304 - [c115]Can Wang, Thomas Bäck, Holger H. Hoos, Mitra Baratchi, Steffen Limmer, Markus Olhofer:
Automated Machine Learning for Short-term Electric Load Forecasting. SSCI 2019: 314-321 - [p7]Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown:
Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA. Automated Machine Learning 2019: 81-95 - 2018
- [j47]Andrea F. Bocchese, Chris Fawcett, Mauro Vallati, Alfonso Emilio Gerevini, Holger H. Hoos:
Performance robustness of AI planners in the 2014 International Planning Competition. AI Commun. 31(6): 445-463 (2018) - [j46]Pascal Kerschke, Lars Kotthoff, Jakob Bossek, Holger H. Hoos, Heike Trautmann:
Leveraging TSP Solver Complementarity through Machine Learning. Evol. Comput. 26(4) (2018) - [j45]Zongxu Mu, Jérémie Dubois-Lacoste, Holger H. Hoos, Thomas Stützle:
On the empirical scaling of running time for finding optimal solutions to the TSP. J. Heuristics 24(6): 879-898 (2018) - [j44]Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown:
Efficient benchmarking of algorithm configurators via model-based surrogates. Mach. Learn. 107(1): 15-41 (2018) - [c114]Nodir Kodirov, Sam Bayless, Fabian Ruffy, Ivan Beschastnikh, Holger H. Hoos, Alan J. Hu:
VNF chain allocation and management at data center scale. ANCS 2018: 125-140 - [c113]Nodir Kodirov, Sam Bayless, Fabian Ruffy, Ivan Beschastnikh, Holger H. Hoos, Alan J. Hu:
VNF chain abstraction for cloud service providers. ANCS 2018: 165-166 - [c112]Holger H. Hoos, Tomás Peitl, Friedrich Slivovsky, Stefan Szeider:
Portfolio-Based Algorithm Selection for Circuit QBFs. CP 2018: 195-209 - [c111]Julieta Martinez, Shobhit Zakhmi, Holger H. Hoos, James J. Little:
LSQ++: Lower Running Time and Higher Recall in Multi-codebook Quantization. ECCV (16) 2018: 508-523 - [c110]Aymeric Blot, Holger H. Hoos, Marie-Eléonore Kessaci, Laetitia Jourdan:
Automatic Configuration of Bi-Objective Optimisation Algorithms: Impact of Correlation Between Objectives. ICTAI 2018: 571-578 - [c109]Lars Kotthoff, Alexandre Fréchette, Tomasz P. Michalak, Talal Rahwan, Holger H. Hoos, Kevin Leyton-Brown:
Quantifying Algorithmic Improvements over Time. IJCAI 2018: 5165-5171 - [c108]Yasha Pushak, Holger H. Hoos:
Algorithm Configuration Landscapes: - More Benign Than Expected? PPSN (2) 2018: 271-283 - [p6]Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown:
Selection and Configuration of Parallel Portfolios. Handbook of Parallel Constraint Reasoning 2018: 583-615 - [p5]Holger H. Hoos, Thomas Stützle:
Empirical Analysis of Randomised Algorithms. Handbook of Approximation Algorithms and Metaheuristics (1) 2018: 225-242 - [p4]Holger H. Hoos, Thomas Stützle:
Stochastic Local Search. Handbook of Approximation Algorithms and Metaheuristics (1) 2018: 297-307 - [i22]Pascal Kerschke, Holger H. Hoos, Frank Neumann, Heike Trautmann:
Automated Algorithm Selection: Survey and Perspectives. CoRR abs/1811.11597 (2018) - [i21]Tijl De Bie, Luc De Raedt, Holger H. Hoos, Padhraic Smyth:
Automating Data Science (Dagstuhl Seminar 18401). Dagstuhl Reports 8(9): 154-181 (2018) - 2017
- [j43]Frank Hutter, Marius Lindauer, Adrian Balint, Sam Bayless, Holger H. Hoos, Kevin Leyton-Brown:
The Configurable SAT Solver Challenge (CSSC). Artif. Intell. 243: 1-25 (2017) - [j42]Marius Lindauer, Holger H. Hoos, Kevin Leyton-Brown, Torsten Schaub:
Automatic construction of parallel portfolios via algorithm configuration. Artif. Intell. 244: 272-290 (2017) - [j41]Mattia Rizzini, Chris Fawcett, Mauro Vallati, Alfonso Emilio Gerevini, Holger H. Hoos:
Static and Dynamic Portfolio Methods for Optimal Planning: An Empirical Analysis. Int. J. Artif. Intell. Tools 26(1): 1760006:1-1760006:27 (2017) - [j40]Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown:
Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA. J. Mach. Learn. Res. 18: 25:1-25:5 (2017) - [c107]Andre Biedenkapp, Marius Lindauer, Katharina Eggensperger, Frank Hutter, Chris Fawcett, Holger H. Hoos:
Efficient Parameter Importance Analysis via Ablation with Surrogates. AAAI 2017: 773-779 - [c106]Aymeric Blot, Alexis Pernet, Laetitia Jourdan, Marie-Éléonore Kessaci-Marmion, Holger H. Hoos:
Automatically Configuring Multi-objective Local Search Using Multi-objective Optimisation. EMO 2017: 61-76 - [c105]Sam Bayless, Nodir Kodirov, Ivan Beschastnikh, Holger H. Hoos, Alan J. Hu:
Scalable Constraint-based Virtual Data Center Allocation. IJCAI 2017: 546-554 - [c104]Marius Lindauer, Frank Hutter, Holger H. Hoos, Torsten Schaub:
AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract). IJCAI 2017: 5025-5029 - [c103]Leslie Pérez Cáceres, Manuel López-Ibáñez, Holger H. Hoos, Thomas Stützle:
An Experimental Study of Adaptive Capping in irace. LION 2017: 235-250 - [e4]Pavel Brazdil, Joaquin Vanschoren, Frank Hutter, Holger H. Hoos:
Proceedings of the International Workshop on Automatic Selection, Configuration and Composition of Machine Learning Algorithms co-located with the European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases, AutoML@PKDD/ECML 2017, Skopje, Macedonia, September 22, 2017. CEUR Workshop Proceedings 1998, CEUR-WS.org 2017 [contents] - [i20]Chris Cameron, Holger H. Hoos, Kevin Leyton-Brown, Frank Hutter:
OASC-2017: *Zilla Submission. OASC 2017: 15-18 - [i19]Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown:
Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates. CoRR abs/1703.10342 (2017) - [i18]Chris Fawcett, Lars Kotthoff, Holger H. Hoos:
Hot-Rodding the Browser Engine: Automatic Configuration of JavaScript Compilers. CoRR abs/1707.04245 (2017) - 2016
- [j39]Ashiqur R. KhudaBukhsh, Lin Xu, Holger H. Hoos, Kevin Leyton-Brown:
SATenstein: Automatically building local search SAT solvers from components. Artif. Intell. 232: 20-42 (2016) - [j38]Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren:
ASlib: A benchmark library for algorithm selection. Artif. Intell. 237: 41-58 (2016) - [j37]Chris Fawcett, Holger H. Hoos:
Analysing differences between algorithm configurations through ablation. J. Heuristics 22(4): 431-458 (2016) - [c102]Alexandre Fréchette, Lars Kotthoff, Tomasz P. Michalak, Talal Rahwan, Holger H. Hoos, Kevin Leyton-Brown:
Using the Shapley Value to Analyze Algorithm Portfolios. AAAI 2016: 3397-3403 - [c101]Julieta Martinez, Joris Clement, Holger H. Hoos, James J. Little:
Revisiting Additive Quantization. ECCV (2) 2016: 137-153 - [c100]Julieta Martinez, Holger H. Hoos, James J. Little:
Solving Multi-codebook Quantization in the GPU. ECCV Workshops (1) 2016: 638-650 - [c99]Holger H. Hoos:
Taming the Complexity Monster or: How I learned to Stop Worrying and Love Hard Problems. GECCO 2016: 3-4 - [c98]Sam Bayless, Holger H. Hoos, Alan J. Hu:
Scalable, high-quality, SAT-based multi-layer escape routing. ICCAD 2016: 22 - [c97]Chris Cameron, Holger H. Hoos, Kevin Leyton-Brown:
Bias in Algorithm Portfolio Performance Evaluation. IJCAI 2016: 712-719 - [c96]Aymeric Blot, Holger H. Hoos, Laetitia Jourdan, Marie-Éléonore Kessaci-Marmion, Heike Trautmann:
MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework. LION 2016: 32-47 - [c95]Zongxu Mu, Holger H. Hoos, Thomas Stützle:
The Impact of Automated Algorithm Configuration on the Scaling Behaviour of State-of-the-Art Inexact TSP Solvers. LION 2016: 157-172 - [c94]Lin Xu, Ashiqur R. KhudaBukhsh, Holger H. Hoos, Kevin Leyton-Brown:
Quantifying the Similarity of Algorithm Configurations. LION 2016: 203-217 - [i17]Holger H. Hoos, Frank Neumann, Heike Trautmann:
Automated Algorithm Selection and Configuration (Dagstuhl Seminar 16412). Dagstuhl Reports 6(10): 33-74 (2016) - 2015
- [j36]Marius Lindauer, Holger H. Hoos, Frank Hutter, Torsten Schaub:
AutoFolio: An Automatically Configured Algorithm Selector. J. Artif. Intell. Res. 53: 745-778 (2015) - [j35]Holger H. Hoos, Thomas Stützle:
On the empirical time complexity of finding optimal solutions vs proving optimality for Euclidean TSP instances. Optim. Lett. 9(6): 1247-1254 (2015) - [j34]Holger H. Hoos, Roland Kaminski, Marius Lindauer, Torsten Schaub:
aspeed: Solver scheduling via answer set programming. Theory Pract. Log. Program. 15(1): 117-142 (2015) - [c93]Katharina Eggensperger, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Efficient Benchmarking of Hyperparameter Optimizers via Surrogates. AAAI 2015: 1114-1120 - [c92]Sam Bayless, Noah Bayless, Holger H. Hoos, Alan J. Hu:
SAT Modulo Monotonic Theories. AAAI 2015: 3702-3709 - [c91]Marius Lindauer, Holger H. Hoos, Frank Hutter, Torsten Schaub:
AutoFolio: Algorithm Configuration for Algorithm Selection. AAAI Workshop: Algorithm Configuration 2015 - [c90]Jérémie Dubois-Lacoste, Holger H. Hoos, Thomas Stützle:
On the Empirical Scaling Behaviour of State-of-the-art Local Search Algorithms for the Euclidean TSP. GECCO 2015: 377-384 - [c89]Zongxu Mu, Holger H. Hoos:
Empirical Scaling Analyser: An Automated System for Empirical Analysis of Performance Scaling. GECCO (Companion) 2015: 771-772 - [c88]Mattia Rizzini, Chris Fawcett, Mauro Vallati, Alfonso Emilio Gerevini, Holger H. Hoos:
Portfolio Methods for Optimal Planning: An Empirical Analysis. ICTAI 2015: 494-501 - [c87]Zongxu Mu, Holger H. Hoos:
On the Empirical Time Complexity of Random 3-SAT at the Phase Transition. IJCAI 2015: 367-373 - [c86]Frank Hutter, Lin Xu, Holger H. Hoos, Kevin Leyton-Brown:
Algorithm Runtime Prediction: Methods and Evaluation (Extended Abstract). IJCAI 2015: 4197-4201 - [c85]Marius Lindauer, Holger H. Hoos, Frank Hutter:
From Sequential Algorithm Selection to Parallel Portfolio Selection. LION 2015: 1-16 - [c84]Sepp Hartung, Holger H. Hoos:
Programming by Optimisation Meets Parameterised Algorithmics: A Case Study for Cluster Editing. LION 2015: 43-58 - [c83]Lars Kotthoff, Pascal Kerschke, Holger H. Hoos, Heike Trautmann:
Improving the State of the Art in Inexact TSP Solving Using Per-Instance Algorithm Selection. LION 2015: 202-217 - [c82]Frederick Tung, Julieta Martinez, Holger H. Hoos, James J. Little:
Bank of Quantization Models: A Data-Specific Approach to Learning Binary Codes for Large-Scale Retrieval Applications. WACV 2015: 566-571 - [p3]Holger H. Hoos, Thomas Stützle:
Stochastic Local Search Algorithms: An Overview. Handbook of Computational Intelligence 2015: 1085-1105 - [i16]Frank Hutter, Marius Lindauer, Adrian Balint, Sam Bayless, Holger H. Hoos, Kevin Leyton-Brown:
The Configurable SAT Solver Challenge (CSSC). CoRR abs/1505.01221 (2015) - [i15]Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren:
ASlib: A Benchmark Library for Algorithm Selection. CoRR abs/1506.02465 (2015) - 2014
- [j33]