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ORCIDHolger H. Hoos
Person information
- affiliation: RWTH Aachen University, Computer Science Department, Aachen, Germany
- affiliation: Leiden University, LIACS, The Netherlands
- affiliation: University of British Columbia, Vancouver, BC, Canada
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2020 – today
- 2025
[j82]Luciano Caroprese, Francesco Sergio Pisani, Bruno Miguel Veloso, Matthias König, Giuseppe Manco, Holger H. Hoos, João Gama:
Modelling Concept Drift in Dynamic Data Streams for Recommender Systems. Trans. Recomm. Syst. 3(2): 24:1-24:28 (2025)- [c144]Tilman Hoffbauer, Holger H. Hoos, Jakob Bossek:
KernelMatmul: Scaling Gaussian Processes to Large Time Series. AAAI 2025: 17223-17230 - [c143]Konstantin Kaulen, Matthias König, Holger H. Hoos:
Dynamic Algorithm Termination for Branch-and-Bound-based Neural Network Verification. AAAI 2025: 27356-27364 - 2024
- [j81]Julian Dierkes, Emma Cramer, Holger H. Hoos, Sebastian Trimpe:
Combining Automated Optimisation of Hyperparameters and Reward Shape. RLJ 3: 1441-1466 (2024) - [j80]Bram M. Renting, Thomas M. Moerland, Holger H. Hoos, Catholijn M. Jonker:
Towards General Negotiation Strategies with End-to-End Reinforcement Learning. RLJ 5: 2059-2070 (2024) - [j79]Mitra Baratchi
, Can Wang, Steffen Limmer, Jan N. van Rijn, Holger H. Hoos, Thomas Bäck, Markus Olhofer:
Automated machine learning: past, present and future. Artif. Intell. Rev. 57(5): 122 (2024) - [j78]Odd Erik Gundersen, Malte Helmert, Holger H. Hoos:
Improving Reproducibility in AI Research: Four Mechanisms Adopted by JAIR. J. Artif. Intell. Res. 81: 1019-1041 (2024) - [j77]Matthias König, Annelot W. Bosman, Holger H. Hoos, Jan N. van Rijn:
Critically Assessing the State of the Art in Neural Network Verification. J. Mach. Learn. Res. 25: 12:1-12:53 (2024) - [j76]Alex Serban, Koen van der Blom, Holger H. Hoos, Joost Visser:
Software engineering practices for machine learning - Adoption, effects, and team assessment. J. Syst. Softw. 209: 111907 (2024) - [j75]Julia Wasala, Suzanne M. Marselis, Laurens Arp, Holger H. Hoos, Nicolas Longépé, Mitra Baratchi:
AutoSR4EO: An AutoML Approach to Super-Resolution for Earth Observation Images. Remote. Sens. 16(3): 443 (2024) - [c142]Matthias König, Holger H. Hoos, Jan N. van Rijn:
Accelerating Adversarially Robust Model Selection for Deep Neural Networks via Racing. AAAI 2024: 21267-21275 - [c141]Jeroen Rook, Holger H. Hoos, Heike Trautmann:
Multi-objective Ranking using Bootstrap Resampling. GECCO Companion 2024: 155-158 - [c140]Matthias König, Xiyue Zhang, Holger H. Hoos, Marta Kwiatkowska, Jan N. van Rijn:
Automated Design of Linear Bounding Functions for Sigmoidal Nonlinearities in Neural Networks. ECML/PKDD (7) 2024: 383-398 - [c139]Annelot W. Bosman, Anna L. Münz, Holger H. Hoos, Jan N. van Rijn:
A Preliminary Study to Examining Per-class Performance Bias via Robustness Distributions. SAIV 2024: 116-133 - [c138]Hadar Shavit, Holger H. Hoos:
Revisiting SATZilla Features in 2024. SAT 2024: 27:1-27:26 - [i38]Matthias König, Xiyue Zhang, Holger H. Hoos, Marta Kwiatkowska, Jan N. van Rijn:
Automated Design of Linear Bounding Functions for Sigmoidal Nonlinearities in Neural Networks. CoRR abs/2406.10154 (2024) - [i37]Bram M. Renting, Thomas M. Moerland, Holger H. Hoos, Catholijn M. Jonker:
Towards General Negotiation Strategies with End-to-End Reinforcement Learning. CoRR abs/2406.15096 (2024) - [i36]Julian Dierkes, Emma Cramer, Holger H. Hoos, Sebastian Trimpe:
Combining Automated Optimisation of Hyperparameters and Reward Shape. CoRR abs/2406.18293 (2024) - [i35]Thijs Snelleman, Bram M. Renting, Holger H. Hoos, Jan N. van Rijn:
Edge-Based Graph Component Pooling. CoRR abs/2409.11856 (2024) - [i34]Jannis Becktepe, Julian Dierkes, Carolin Benjamins, Aditya Mohan, David Salinas, Raghu Rajan, Frank Hutter, Holger H. Hoos, Marius Lindauer, Theresa Eimer:
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning. CoRR abs/2409.18827 (2024) - 2023
- [j74]Zhou Zhou, Mitra Baratchi, Gangquan Si, Holger H. Hoos, Gang Huang:
Adaptive error bounded piecewise linear approximation for time-series representation. Eng. Appl. Artif. Intell. 126: 106892 (2023) - [j73]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) - [j72]Kevin Baum, Joanna Bryson, Frank Dignum, Virginia Dignum, Marko Grobelnik, Holger H. Hoos, Morten Irgens, Paul Lukowicz, Catelijne Muller, Francesca Rossi, John Shawe-Taylor, Andreas Theodorou, Ricardo Vinuesa:
From fear to action: AI governance and opportunities for all. Frontiers Comput. Sci. 5 (2023) - [c137]Matthias König, Annelot W. Bosman, Holger H. Hoos, Jan N. van Rijn:
Critically Assessing the State of the Art in CPU-based Local Robustness Verification. SafeAI@AAAI 2023 - [c136]Lennart Oswald Purucker, Lennart Schneider, Marie Anastacio, Joeran Beel, Bernd Bischl, Holger H. Hoos:
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML. AutoML 2023: 10/1-34 - [i33]Devis Tuia, Konrad Schindler, Begüm Demir, Gustau Camps-Valls, Xiao Xiang Zhu, Mrinalini Kochupillai, Saso Dzeroski, Jan N. van Rijn, Holger H. Hoos, Fabio Del Frate, Mihai Datcu, Jorge-Arnulfo Quiané-Ruiz, Volker Markl, Bertrand Le Saux, Rochelle Schneider:
Artificial intelligence to advance Earth observation: a perspective. CoRR abs/2305.08413 (2023) - [i32]Lennart Purucker, Lennart Schneider, Marie Anastacio, Joeran Beel, Bernd Bischl, Holger H. Hoos:
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML. CoRR abs/2307.08364 (2023) - [i31]Chris Fawcett, Mauro Vallati, Holger H. Hoos, Alfonso Emilio Gerevini:
Competitions in AI - Robustly Ranking Solvers Using Statistical Resampling. CoRR abs/2308.05062 (2023) - [i30]Saso Dzeroski, Holger H. Hoos, Bertrand Le Saux, Leendert van der Torre, Ana Kostovska:
Space and Artificial Intelligence (Dagstuhl Seminar 23461). Dagstuhl Reports 13(11): 72-102 (2023) - 2022
- [j71]Jaco Tetteroo, Mitra Baratchi, Holger H. Hoos:
Automated Machine Learning for COVID-19 Forecasting. IEEE Access 10: 94718-94737 (2022) - [j70]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) - [j69]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) - [j68]Laurens Arp, Mitra Baratchi, Holger H. Hoos:
VPint: value propagation-based spatial interpolation. Data Min. Knowl. Discov. 36(5): 1647-1678 (2022) - [j67]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) - [j66]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) - [j65]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) - [j64]Yasha Pushak, Holger H. Hoos:
AutoML Loss Landscapes. ACM Trans. Evol. Learn. Optim. 2(3): 10:1-10:30 (2022) - [c135]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 - [c134]Bram M. Renting, Holger H. Hoos, Catholijn M. Jonker:
Automated Configuration and Usage of Strategy Portfolios Mixed-Motive Bargaining. AAMAS 2022: 1101-1109 - [c133]Damir Pulatov, Marie Anastacio, Lars Kotthoff, Holger H. Hoos:
Opening the Black Box: Automated Software Analysis for Algorithm Selection. AutoML 2022: 6/1-18 - [c132]Marie Anastacio, Théo Matricon, Holger H. Hoos:
Challenges of Acquiring Compositional Inductive Biases via Meta-Learning. Meta-Knowledge Transfer @ ECML/PKDD 2022: 11-23 - [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
- [j63]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) - [j62]Gianluca Bontempi, Ricardo Chavarriaga, Hans ed Canck, Emanuela Girardi, Holger H. Hoos, Iarla Kilbane-Dawe, Tonio Ball, Ann Nowé, Jose Sousa, Davide Bacciu, Marco Aldinucci, Manlio ed Domenico, Alessandro Saffiotti, Marco Maratea:
The CLAIRE COVID-19 initiative: approach, experiences and recommendations. Ethics Inf. Technol. 23(S1): 127-133 (2021) - [c131]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 - [c130]Alex Serban, Koen van der Blom, Holger H. Hoos, Joost Visser:
Practices for Engineering Trustworthy Machine Learning Applications. WAIN@ICSE 2021: 97-100 - [c129]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 - [c128]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) - [c127]Bram M. Renting, Holger H. Hoos, Catholijn M. Jonker:
Automated Configuration of Negotiation Strategies. AAMAS 2020: 1116-1124 - [c126]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 - [c125]Yasha Pushak, Holger H. Hoos:
Advanced statistical analysis of empirical performance scaling. GECCO 2020: 236-244 - [c124]Yasha Pushak, Holger H. Hoos:
Golden parameter search: exploiting structure to quickly configure parameters in parallel. GECCO 2020: 245-253 - [c123]Marie Anastacio, Holger H. Hoos:
Combining sequential model-based algorithm configuration with default-guided probabilistic sampling. GECCO Companion 2020: 301-302 - [c122]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 - [c121]Marie Anastacio, Holger H. Hoos:
Model-Based Algorithm Configuration with Default-Guided Probabilistic Sampling. PPSN (1) 2020: 95-110 - [c120]Chuan Luo, Holger H. Hoos, Shaowei Cai:
PbO-CCSAT: Boosting Local Search for Satisfiability Using Programming by Optimisation. PPSN (1) 2020: 373-389 - [c119]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) - [c118]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 - [c117]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 - [c116]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) - [c115]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 - [c114]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 - [c113]Holger H. Hoos, Tomás Peitl, Friedrich Slivovsky, Stefan Szeider:
Portfolio-Based Algorithm Selection for Circuit QBFs. CP 2018: 195-209 - [c112]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 - [c111]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 - [c110]Lars Kotthoff, Alexandre Fréchette, Tomasz P. Michalak, Talal Rahwan, Holger H. Hoos, Kevin Leyton-Brown:
Quantifying Algorithmic Improvements over Time. IJCAI 2018: 5165-5171 - [c109]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) - [c108]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 - [c107]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 - [c106]Sam Bayless, Nodir Kodirov, Ivan Beschastnikh, Holger H. Hoos, Alan J. Hu:
Scalable Constraint-based Virtual Data Center Allocation. IJCAI 2017: 546-554 - [c105]Marius Lindauer, Frank Hutter, Holger H. Hoos, Torsten Schaub:
AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract). IJCAI 2017: 5025-5029 - [c104]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) - [c103]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 - [c102]Julieta Martinez, Joris Clement, Holger H. Hoos, James J. Little:
Revisiting Additive Quantization. ECCV (2) 2016: 137-153 - [c101]Julieta Martinez, Holger H. Hoos, James J. Little:
Solving Multi-codebook Quantization in the GPU. ECCV Workshops (1) 2016: 638-650 - [c100]Holger H. Hoos:
Taming the Complexity Monster or: How I learned to Stop Worrying and Love Hard Problems. GECCO 2016: 3-4 - [c99]Sam Bayless, Holger H. Hoos, Alan J. Hu:
Scalable, high-quality, SAT-based multi-layer escape routing. ICCAD 2016: 22 - [c98]Chris Cameron, Holger H. Hoos, Kevin Leyton-Brown:
Bias in Algorithm Portfolio Performance Evaluation. IJCAI 2016: 712-719 - [c97]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 - [c96]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 - [c95]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) - [c94]Katharina Eggensperger, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Efficient Benchmarking of Hyperparameter Optimizers via Surrogates. AAAI 2015: 1114-1120 - [c93]Sam Bayless, Noah Bayless, Holger H. Hoos, Alan J. Hu:
SAT Modulo Monotonic Theories. AAAI 2015: 3702-3709 - [c92]Marius Lindauer, Holger H. Hoos, Frank Hutter, Torsten Schaub:
AutoFolio: Algorithm Configuration for Algorithm Selection. AAAI Workshop: Algorithm Configuration 2015 - [c91]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 - [c90]Zongxu Mu, Holger H. Hoos:
Empirical Scaling Analyser: An Automated System for Empirical Analysis of Performance Scaling. GECCO (Companion) 2015: 771-772 - [c89]Mattia Rizzini, Chris Fawcett, Mauro Vallati, Alfonso Emilio Gerevini, Holger H. Hoos:
Portfolio Methods for Optimal Planning: An Empirical Analysis. ICTAI 2015: 494-501 - [c88]Zongxu Mu, Holger H. Hoos:
On the Empirical Time Complexity of Random 3-SAT at the Phase Transition. IJCAI 2015: 367-373 - [c87]Frank Hutter, Lin Xu, Holger H. Hoos, Kevin Leyton-Brown:
Algorithm Runtime Prediction: Methods and Evaluation (Extended Abstract). IJCAI 2015: 4197-4201 - [c86]Marius Lindauer, Holger H. Hoos, Frank Hutter:
From Sequential Algorithm Selection to Parallel Portfolio Selection. LION 2015: 1-16 - [c85]Sepp Hartung, Holger H. Hoos:
Programming by Optimisation Meets Parameterised Algorithmics: A Case Study for Cluster Editing. LION 2015: 43-58 - [c84]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 - [c83]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]Frank Hutter, Lin Xu, Holger H. Hoos, Kevin Leyton-Brown:
Algorithm runtime prediction: Methods & evaluation. Artif. Intell. 206: 79-111 (2014) - [j32]Kieran O'Neill, Adrin Jalali, Nima Aghaeepour, Holger H. Hoos, Ryan Remy Brinkman:
Enhanced flowType/RchyOptimyx: a Bioconductor pipeline for discovery in high-dimensional cytometry data. Bioinform. 30(9): 1329-1330 (2014) - [j31]Kevin Leyton-Brown, Holger H. Hoos, Frank Hutter, Lin Xu:
Understanding the empirical hardness of NP-complete problems. Commun. ACM 57(5): 98-107 (2014) - [j30]Holger H. Hoos, Thomas Stützle:
On the empirical scaling of run-time for finding optimal solutions to the travelling salesman problem. Eur. J. Oper. Res. 238(1): 87-94 (2014) - [j29]Holger H. Hoos, Marius Lindauer, Torsten Schaub:
claspfolio 2: Advances in Algorithm Selection for Answer Set Programming. Theory Pract. Log. Program. 14(4-5): 569-585 (2014) - [j28]Lucas Majerowicz, Ariel Shamir, Alla Sheffer, Holger H. Hoos:
Filling Your Shelves: Synthesizing Diverse Style-Preserving Artifact Arrangements. IEEE Trans. Vis. Comput. Graph. 20(11): 1507-1518 (2014) - [c82]Chris Fawcett, Mauro Vallati, Frank Hutter, Jörg Hoffmann, Holger H. Hoos, Kevin Leyton-Brown:
Improved Features for Runtime Prediction of Domain-Independent Planners. ICAPS 2014 - [c81]Katharina Eggensperger, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Surrogate Benchmarks for Hyperparameter Optimization. MetaSel@ECAI 2014: 24-31 - [c80]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
An Efficient Approach for Assessing Hyperparameter Importance. ICML 2014: 754-762 - [c79]Frank Hutter, Manuel López-Ibáñez, Chris Fawcett, Marius Lindauer, Holger H. Hoos, Kevin Leyton-Brown, Thomas Stützle:
AClib: A Benchmark Library for Algorithm Configuration. LION 2014: 36-40 - [c78]Daniel Geschwender, Frank Hutter, Lars Kotthoff, Yuri Malitsky, Holger H. Hoos, Kevin Leyton-Brown:
Algorithm Configuration in the Cloud: A Feasibility Study. LION 2014: 41-46 - [c77]Sam Bayless, Dave A. D. Tompkins, Holger H. Hoos:
Evaluating Instance Generators by Configuration. LION 2014: 47-61 - [i14]Holger H. Hoos, Roland Kaminski, Marius Lindauer, Torsten Schaub:
Solver Scheduling via Answer Set Programming. CoRR abs/1401.1024 (2014) - [i13]Frank Hutter, Thomas Stützle, Kevin Leyton-Brown, Holger H. Hoos:
ParamILS: An Automatic Algorithm Configuration Framework. CoRR abs/1401.3492 (2014) - [i12]Holger H. Hoos, Marius Lindauer, Torsten Schaub:
claspfolio 2: Advances in Algorithm Selection for Answer Set Programming. CoRR abs/1405.1520 (2014) - [i11]Sam Bayless, Noah Bayless, Holger H. Hoos, Alan J. Hu:
SAT Modulo Monotonic Theories. CoRR abs/1406.0043 (2014) - [i10]Julieta Martinez, Holger H. Hoos, James J. Little:
Stacked Quantizers for Compositional Vector Compression. CoRR abs/1411.2173 (2014) - 2013
- [j27]Nima Aghaeepour, Holger H. Hoos:
Ensemble-based prediction of RNA secondary structures. BMC Bioinform. 14: 139 (2013) - [c76]Sam Bayless, Celina G. Val, Thomas Ball, Holger H. Hoos, Alan J. Hu:
Efficient modular SAT solving for IC3. FMCAD 2013: 149-156 - [c75]James Styles, Holger H. Hoos:
Ordered racing protocols for automatically configuring algorithms for scaling performance. GECCO 2013: 551-558 - [c74]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
An evaluation of sequential model-based optimization for expensive blackbox functions. GECCO (Companion) 2013: 1209-1216 - [c73]Chris Thornton, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. KDD 2013: 847-855 - [c72]Holger H. Hoos, Benjamin Kaufmann, Torsten Schaub, Marius Schneider:
Robust Benchmark Set Selection for Boolean Constraint Solvers. LION 2013: 138-152 - [c71]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Identifying Key Algorithm Parameters and Instance Features Using Forward Selection. LION 2013: 364-381 - [c70]James Styles, Holger H. Hoos:
Using Racing to Automatically Configure Algorithms for Scaling Performance. LION 2013: 382-388 - [c69]Mauro Vallati, Chris Fawcett, Alfonso Gerevini, Holger H. Hoos, Alessandro Saetti:
Automatic Generation of Efficient Domain-Optimized Planners from Generic Parametrized Planners. SOCS 2013: 184-192 - [i9]Craig Boutilier, Ronen I. Brafman, Holger H. Hoos, David Poole:
Reasoning With Conditional Ceteris Paribus Preference Statem. CoRR abs/1301.6681 (2013) - [i8]Holger H. Hoos, Thomas Stützle:
Evaluating Las Vegas Algorithms - Pitfalls and Remedies. CoRR abs/1301.7383 (2013) - [i7]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Bayesian Optimization With Censored Response Data. CoRR abs/1310.1947 (2013) - 2012
- [j26]Nima Aghaeepour, Pratip K. Chattopadhyay, Anuradha Ganesan, Kieran O'Neill, Habil Zare, Adrin Jalali, Holger H. Hoos, Mario Roederer, Ryan Remy Brinkman:
Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays. Bioinform. 28(7): 1009-1016 (2012) - [j25]Monir Hajiaghayi, Anne Condon, Holger H. Hoos:
Analysis of energy-based algorithms for RNA secondary structure prediction. BMC Bioinform. 13: 22 (2012) - [j24]Holger H. Hoos:
Programming by optimization. Commun. ACM 55(2): 70-80 (2012) - [c68]Lin Xu, Holger H. Hoos, Kevin Leyton-Brown:
Predicting Satisfiability at the Phase Transition. AAAI 2012: 584-590 - [c67]Holger H. Hoos, Roland Kaminski, Torsten Schaub, Marius Schneider:
aspeed: ASP-based Solver Scheduling. ICLP (Technical Communications) 2012: 176-187 - [c66]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Parallel Algorithm Configuration. LION 2012: 55-70 - [c65]Marius Schneider, Holger H. Hoos:
Quantifying Homogeneity of Instance Sets for Algorithm Configuration. LION 2012: 190-204 - [c64]James Styles, Holger H. Hoos, Martin Müller:
Automatically Configuring Algorithms for Scaling Performance. LION 2012: 205-219 - [c63]Lin Xu, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Evaluating Component Solver Contributions to Portfolio-Based Algorithm Selectors. SAT 2012: 228-241 - [p2]Holger H. Hoos:
Automated Algorithm Configuration and Parameter Tuning. Autonomous Search 2012: 37-71 - [i6]Chris Thornton, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Auto-WEKA: Automated Selection and Hyper-Parameter Optimization of Classification Algorithms. CoRR abs/1208.3719 (2012) - [i5]Frank Hutter, Lin Xu, Holger H. Hoos, Kevin Leyton-Brown:
Algorithm Runtime Prediction: The State of the Art. CoRR abs/1211.0906 (2012) - 2011
- [j23]Therese Biedl, Stephane Durocher, Holger H. Hoos, Shuang Luan, Jared Saia, Maxwell Young:
A note on improving the performance of approximation algorithms for radiation therapy. Inf. Process. Lett. 111(7): 326-333 (2011) - [c62]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Sequential Model-Based Optimization for General Algorithm Configuration. LION 2011: 507-523 - [c61]Christopher Nell, Chris Fawcett, Holger H. Hoos, Kevin Leyton-Brown:
HAL: A Framework for the Automated Analysis and Design of High-Performance Algorithms. LION 2011: 600-615 - [c60]Dave A. D. Tompkins, Adrian Balint, Holger H. Hoos:
Captain Jack: New Variable Selection Heuristics in Local Search for SAT. SAT 2011: 302-316 - [i4]Craig Boutilier, Ronen I. Brafman, Carmel Domshlak, Holger H. Hoos, David Poole:
CP-nets: A Tool for Representing and Reasoning withConditional Ceteris Paribus Preference Statements. CoRR abs/1107.0023 (2011) - [i3]Holger H. Hoos, Wayne J. Pullan:
Dynamic Local Search for the Maximum Clique Problem. CoRR abs/1109.5717 (2011) - [i2]Lin Xu, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
SATzilla: Portfolio-based Algorithm Selection for SAT. CoRR abs/1111.2249 (2011) - 2010
- [j22]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Tradeoffs in the empirical evaluation of competing algorithm designs. Ann. Math. Artif. Intell. 60(1-2): 65-89 (2010) - [c59]Lin Xu, Holger H. Hoos, Kevin Leyton-Brown:
Hydra: Automatically Configuring Algorithms for Portfolio-Based Selection. AAAI 2010: 210-216 - [c58]Holger H. Hoos:
Computer-Aided Algorithm Design: Automated Tuning, Configuration, Selection, and Beyond. ICAPS 2010: 268-269 - [c57]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Automated Configuration of Mixed Integer Programming Solvers. CPAIOR 2010: 186-202 - [c56]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown, Kevin P. Murphy:
Time-Bounded Sequential Parameter Optimization. LION 2010: 281-298 - [c55]Dave A. D. Tompkins, Holger H. Hoos:
Dynamic Scoring Functions with Variable Expressions: New SLS Methods for Solving SAT. SAT 2010: 278-292 - [p1]Frank Hutter, Thomas Bartz-Beielstein, Holger H. Hoos, Kevin Leyton-Brown, Kevin P. Murphy:
Sequential Model-Based Parameter Optimization: an Experimental Investigation of Automated and Interactive Approaches. Experimental Methods for the Analysis of Optimization Algorithms 2010: 363-414
2000 – 2009
- 2009
- [j21]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown, Thomas Stützle:
ParamILS: An Automatic Algorithm Configuration Framework. J. Artif. Intell. Res. 36: 267-306 (2009) - [c54]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown, Kevin P. Murphy:
An experimental investigation of model-based parameter optimisation: SPO and beyond. GECCO 2009: 271-278 - [c53]Ashiqur R. KhudaBukhsh, Lin Xu, Holger H. Hoos, Kevin Leyton-Brown:
SATenstein: Automatically Building Local Search SAT Solvers from Components. IJCAI 2009: 517-524 - [e3]Thomas Stützle, Mauro Birattari, Holger H. Hoos:
Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics, Second International Workshop, SLS 2009, Brussels, Belgium, September 3-4, 2009. Proceedings. Lecture Notes in Computer Science 5752, Springer 2009, ISBN 978-3-642-03750-4 [contents] - [i1]Therese Biedl, Stephane Durocher, Holger H. Hoos, Shuang Luan, Jared Saia, Maxwell Young:
Fixed-Parameter Tractability and Improved Approximations for Segment Minimization. CoRR abs/0905.4930 (2009) - 2008
- [j20]Mirela Andronescu, Vera Bereg, Holger H. Hoos, Anne Condon:
RNA STRAND: The RNA Secondary Structure and Statistical Analysis Database. BMC Bioinform. 9 (2008) - [j19]Lin Xu, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
SATzilla: Portfolio-based Algorithm Selection for SAT. J. Artif. Intell. Res. 32: 565-606 (2008) - 2007
- [j18]Holger H. Hoos, Thomas Stützle:
Preface. Ann. Oper. Res. 156(1): 1-4 (2007) - [j17]Rosalía Aguirre-Hernández, Holger H. Hoos, Anne Condon:
Computational RNA secondary structure design: empirical complexity and improved methods. BMC Bioinform. 8 (2007) - [j16]Alena Shmygelska, Holger H. Hoos:
An adaptive bin framework search method for a beta-sheet protein homopolymer model. BMC Bioinform. 8 (2007) - [j15]Chris Thachuk, Alena Shmygelska, Holger H. Hoos:
A replica exchange Monte Carlo algorithm for protein folding in the HP model. BMC Bioinform. 8 (2007) - [j14]Michael Huggett, Holger H. Hoos, Ronald A. Rensink:
Cognitive Principles for Information Management: The Principles of Mnemonic Associative Knowledge (P-MAK). Minds Mach. 17(4): 445-485 (2007) - [c52]Frank Hutter, Holger H. Hoos, Thomas Stützle:
Automatic Algorithm Configuration Based on Local Search. AAAI 2007: 1152-1157 - [c51]Lin Xu, Holger H. Hoos, Kevin Leyton-Brown:
Hierarchical Hardness Models for SAT. CP 2007: 696-711 - [c50]Lin Xu, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
: The Design and Analysis of an Algorithm Portfolio for SAT. CP 2007: 712-727 - [c49]Frank Hutter, Domagoj Babic, Holger H. Hoos, Alan J. Hu:
Boosting Verification by Automatic Tuning of Decision Procedures. FMCAD 2007: 27-34 - [c48]Camilo Rostoker, Alan Wagner, Holger H. Hoos:
A Parallel Workflow for Real-time Correlation and Clustering of High-Frequency Stock Market Data. IPDPS 2007: 1-10 - [c47]Mirela Andronescu, Anne Condon, Holger H. Hoos, David H. Mathews, Kevin P. Murphy:
Efficient parameter estimation for RNA secondary structure prediction. ISMB/ECCB (Supplement of Bioinformatics) 2007: 19-28 - [c46]Mauro Brunato, Holger H. Hoos, Roberto Battiti:
On Effectively Finding Maximal Quasi-cliques in Graphs. LION 2007: 41-55 - [e2]Thomas Stützle, Mauro Birattari, Holger H. Hoos:
Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics, International Workshop, SLS 2007, Brussels, Belgium, September 6-8, 2007, Proceedings. Lecture Notes in Computer Science 4638, Springer 2007, ISBN 978-3-540-74445-0 [contents] - [r3]Holger H. Hoos, Thomas Stützle:
Empirical Analysis of Randomized Algorithms. Handbook of Approximation Algorithms and Metaheuristics 2007 - [r2]Holger H. Hoos, Thomas Stützle:
Stochastic Local Search. Handbook of Approximation Algorithms and Metaheuristics 2007 - 2006
- [j13]Wayne J. Pullan, Holger H. Hoos:
Dynamic Local Search for the Maximum Clique Problem. J. Artif. Intell. Res. 25: 159-185 (2006) - [j12]Holger H. Hoos:
Die Logik des Lebens. Künstliche Intell. 20(4): 15-21 (2006) - [c45]Dave A. D. Tompkins, Holger H. Hoos:
On the Quality and Quantity of Random Decisions in Stochastic Local Search for SAT. Canadian AI 2006: 146-158 - [c44]Frank Hutter, Youssef Hamadi, Holger H. Hoos, Kevin Leyton-Brown:
Performance Prediction and Automated Tuning of Randomized and Parametric Algorithms. CP 2006: 213-228 - [r1]Holger H. Hoos, Edward P. K. Tsang:
Local Search Methods. Handbook of Constraint Programming 2006: 135-167 - 2005
- [j11]Alena Shmygelska, Holger H. Hoos:
An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem. BMC Bioinform. 6: 30 (2005) - [c43]Frank Hutter, Holger H. Hoos, Thomas Stützle:
Efficient Stochastic Local Search for MPE Solving. IJCAI 2005: 169-174 - [e1]Holger H. Hoos, David G. Mitchell:
Theory and Applications of Satisfiability Testing, 7th International Conference, SAT 2004, Vancouver, BC, Canada, May 10-13, 2004, Revised Selected Papers. Lecture Notes in Computer Science 3542, Springer 2005, ISBN 3-540-27829-X [contents] - 2004
- [b3]Holger H. Hoos, Thomas Stützle:
Stochastic Local Search: Foundations & Applications. Elsevier / Morgan Kaufmann 2004, ISBN 1-55860-872-9 - [j10]Craig Boutilier, Ronen I. Brafman, Carmel Domshlak, Holger H. Hoos, David Poole:
Preference-Based Constrained Optimization with CP-Nets. Comput. Intell. 20(2): 137-157 (2004) - [j9]Holger H. Hoos, David Bainbridge:
Editors' Notes. Comput. Music. J. 28(2): 4-5 (2004) - [j8]Craig Boutilier, Ronen I. Brafman, Carmel Domshlak, Holger H. Hoos, David Poole:
CP-nets: A Tool for Representing and Reasoning with Conditional Ceteris Paribus Preference Statements. J. Artif. Intell. Res. 21: 135-191 (2004) - [c42]Eugene Nudelman, Kevin Leyton-Brown, Holger H. Hoos, Alex Devkar, Yoav Shoham:
Understanding Random SAT: Beyond the Clauses-to-Variables Ratio. CP 2004: 438-452 - [c41]Dave A. D. Tompkins, Holger H. Hoos:
Warped Landscapes and Random Acts of SAT Solving. AI&M 2004 - [c40]Jürgen Kilian, Holger H. Hoos:
MusicBLAST - Gapped Sequence Alignment for MIR. ISMIR 2004 - [c39]Holger H. Hoos, Kevin Smyth, Thomas Stützle:
Search Space Features Underlying the Performance of Stochastic Local Search Algorithms for MAX-SAT. PPSN 2004: 51-60 - [c38]Dave A. D. Tompkins, Holger H. Hoos:
UBCSAT: An Implementation and Experimentation Environment for SLS Algorithms for SAT & MAX-SAT. SAT 2004 - [c37]Dave A. D. Tompkins, Holger H. Hoos:
UBCSAT: An Implementation and Experimentation Environment for SLS Algorithms for SAT and MAX-SAT. SAT (Selected Papers 2004: 306-320 - 2003
- [j7]Andrew Tae-Jun Kwon, Holger H. Hoos, Raymond T. Ng:
Inference of Transcriptional Regulation Relationships from Gene Expression Data. Bioinform. 19(8): 905-912 (2003) - [j6]Mirela Andronescu, Rosalía Aguirre-Hernández, Anne Condon, Holger H. Hoos:
RNAsoft: a suite of RNA secondary structure prediction and design software tools. Nucleic Acids Res. 31(13): 3416-3422 (2003) - [c36]Michael Pavlin, Holger H. Hoos, Thomas Stützle:
Stochastic Local Search for Multiprocessor Scheduling for Minimum Total Tardiness. AI 2003: 96-113 - [c35]Kevin Smyth, Holger H. Hoos, Thomas Stützle:
Iterated Robust Tabu Search for MAX-SAT. AI 2003: 129-144 - [c34]Dave A. D. Tompkins, Holger H. Hoos:
Scaling and Probabilistic Smoothing: Dynamic Local Search for Unweighted MAX-SAT. AI 2003: 145-159 - [c33]Alena Shmygelska, Holger H. Hoos:
An Improved Ant Colony Optimisation Algorithm for the 2D HP Protein Folding Problem. AI 2003: 400-417 - [c32]Dan C. Tulpan, Holger H. Hoos:
Hybrid Randomised Neighbourhoods Improve Stochastic Local Search for DNA Code Design. AI 2003: 418-433 - [c31]Ian P. Gent, Holger H. Hoos, Andrew G. D. Rowley, Kevin Smyth:
Using Stochastic Local Search to Solve Quantified Boolean Formulae. CP 2003: 348-362 - [c30]Andrew Tae-Jun Kwon, Holger H. Hoos, Raymond T. Ng:
Inference of Transcriptional Regulation Relationships from Gene Expression Data. SAC 2003: 135-140 - 2002
- [c29]Holger H. Hoos:
An Adaptive Noise Mechanism for WalkSAT. AAAI/IAAI 2002: 655-660 - [c28]Holger H. Hoos:
A Mixture-Model for the Behaviour of SLS Algorithms for SAT. AAAI/IAAI 2002: 661-667 - [c27]Alena Shmygelska, Rosalía Aguirre-Hernández, Holger H. Hoos:
An Ant Colony Optimization Algorithm for the 2D HP Protein Folding Problem. Ant Algorithms 2002: 40-53 - [c26]Frank Hutter, Dave A. D. Tompkins, Holger H. Hoos:
Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT. CP 2002: 233-248 - [c25]Christine E. Heitsch, Anne Condon, Holger H. Hoos:
From RNA Secondary Structure to Coding Theory: A Combinatorial Approach. DNA 2002: 215-228 - [c24]Dan C. Tulpan, Holger H. Hoos, Anne Condon:
Stochastic Local Search Algorithms for DNA Word Design. DNA 2002: 229-241 - [c23]Jürgen Kilian, Holger H. Hoos:
Voice Separation - A Local Optimization Approach. ISMIR 2002 - 2001
- [j5]Yves Lespérance, Gerd Wagner, William P. Birmingham, Kurt D. Bollacker, Alexander Nareyek, J. Paul Walser, David W. Aha, Timothy W. Finin, Benjamin N. Grosof, Nathalie Japkowicz, Robert Holte, Lise Getoor, Carla P. Gomes, Holger H. Hoos, Alan C. Schultz, Miroslav Kubat, Tom M. Mitchell, Jörg Denzinger, Yolanda Gil, Karen L. Myers, Claudio Bettini, Angelo Montanari:
AAAI 2000 Workshop Reports. AI Mag. 22(1): 127-136 (2001) - [j4]Thomas Stützle, Holger H. Hoos:
Ameisenalgorithmen zur Lösung kombinatorischer Optimierungsprobleme. Künstliche Intell. 15(1): 45-51 (2001) - [c22]Craig Boutilier, Holger H. Hoos:
Bidding Languages for Combinatorial Auctions. IJCAI 2001: 1211-1217 - [c21]Holger H. Hoos, Kai Renz, Marko Görg:
GUIDO/MIR - an Experimental Musical Information Retrieval System based on GUIDO Music Notation. ISMIR 2001: 41-50 - [c20]Kai Renz, Holger H. Hoos:
Web Delivery of Music using the GUIDO NoteServer. WEDELMUSIC 2001: 193-194 - 2000
- [j3]Thomas Stützle, Holger H. Hoos:
MAX-MIN Ant System. Future Gener. Comput. Syst. 16(8): 889-914 (2000) - [j2]Holger H. Hoos, Thomas Stützle:
Local Search Algorithms for SAT: An Empirical Evaluation. J. Autom. Reason. 24(4): 421-481 (2000) - [c19]Holger H. Hoos, Craig Boutilier:
Solving Combinatorial Auctions Using Stochastic Local Search. AAAI/IAAI 2000: 22-29 - [c18]Jürgen Kilian, Holger H. Hoos:
VISCO - Visual SALIERI Components. ICMC 2000
1990 – 1999
- 1999
- [b2]Holger H. Hoos:
Stochastic local search - methods, models, applications. DISKI 215, Infix 1999, ISBN 978-3-89601-215-9, pp. I-X, 1-219 - [j1]Holger H. Hoos, Thomas Stützle:
Towards a Characterisation of the Behaviour of Stochastic Local Search Algorithms for SAT. Artif. Intell. 112(1-2): 213-232 (1999) - [c17]Ian P. Gent, Holger H. Hoos, Patrick Prosser, Toby Walsh:
Morphing: Combining Structure and Randomness. AAAI/IAAI 1999: 654-660 - [c16]Holger H. Hoos:
On the Run-time Behaviour of Stochastic Local Search Algorithms for SAT. AAAI/IAAI 1999: 661-666 - [c15]Holger H. Hoos, Keith Hamel, Kai Renz:
Using Advanced GUIDO as a Notation Interchange Format. ICMC 1999 - [c14]Holger H. Hoos:
SAT-Encodings, Search Space Structure, and Local Search Performance. IJCAI 1999: 296-303 - [c13]Ronen I. Brafman, Holger H. Hoos:
To Encode or Not to Encode - Linear Planning. IJCAI 1999: 988-995 - [c12]Holger H. Hoos, Thomas Stützle:
Systematic vs. Local Search for SAT. KI 1999: 289-293 - [c11]Craig Boutilier, Ronen I. Brafman, Holger H. Hoos, David Poole:
Reasoning With Conditional Ceteris Paribus Preference Statements. UAI 1999: 71-80 - 1998
- [b1]Holger H. Hoos:
Stochastic local search - methods, models, applications. TU Darmstadt, 1998, pp. I-VI, 1-218 - [c10]Holger H. Hoos, Thomas Stützle:
Some Surprising Regularities in the Behaviour of Stochastic Local Search. CP 1998: 470 - [c9]Holger H. Hoos, Keith Hamel, Kai Renz, Jürgen Kilian:
The GUIDO Notation Format: A Novel Approach for Adequately Representing Score-Level Music. ICMC 1998 - [c8]Holger H. Hoos, Jürgen Kilian, Kai Renz, Thomas Helbich:
SALIERI: A General, Interactive Computer Music System. ICMC 1998 - [c7]Kai Renz, Holger H. Hoos:
An HTTP Interface to Salieri. ICMC 1998 - [c6]Kai Renz, Holger H. Hoos:
A WEB-based Approach to Music Notation using GUIDO. ICMC 1998 - [c5]Holger H. Hoos, Thomas Stützle:
Evaluating Las Vegas Algorithms: Pitfalls and Remedies. UAI 1998: 238-245 - 1997
- [c4]Thomas Stützle, Holger H. Hoos:
Improvements on the Ant-System: Introducing the MAX-MIN Ant System. ICANNGA 1997: 245-249 - [c3]Thomas Stützle, Holger H. Hoos:
MAX-MIN Ant System and local search for the traveling salesman problem. ICEC 1997: 309-314 - 1996
- [c2]Holger H. Hoos:
Solving Hard Combinatorial Problems with GSAT - A Case Study. KI 1996: 107-119 - 1994
- [c1]Antje Beeringer, Gerd Aschemann, Holger H. Hoos, Michael Metzger, Andreas Weiss:
GSAT versus Simulated Annealing. ECAI 1994: 130-134
Coauthor Index
[p9] [p7] [j44] [c110] [p6] [j43] [j42] [j40] [i20] [i19] [j39] [j38] [c103] [c98] [c95] [c94] [c87] [i16] [i15] [j33] [j31] [c82] [c81] [c80] [c79] [c78] [i13] [c74] [c73] [c71] [i7] [c68] [c66] [c63] [i6] [i5] [c62] [c61] [i2] [j22] [c59] [c57] [c56] [p1] [j21] [c54] [c53] [j19] [c51] [c50] [c44] [c42]
Laetitia Vermeulen-Jourdan
aka: Laetitia Jourdan
aka: Laetitia Jourdan
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