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Lars Kotthoff
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- affiliation: University of Wyoming, WY, USA
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2020 – today
- 2024
- [c39]Md Shahriar Iqbal, Jianhai Su, Lars Kotthoff, Pooyan Jamshidi:
FlexiBO: A Decoupled Cost-Aware Multi-objective Optimization Approach for Deep Neural Networks (Abstract Reprint). AAAI 2024: 22700 - [c38]Geoff Sutcliffe
, Christian B. Suttner, Lars Kotthoff
An Empirical Assessment of Progress in Automated Theorem Proving. IJCAR (1) 2024: 53-74 - 2023
- [j18]Md Shahriar Iqbal, Jianhai Su
FlexiBO: A Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural Networks. J. Artif. Intell. Res. 77: 645-682 (2023) - [c37]Haniye Kashgarani
Automatic Parallel Portfolio Selection. ECAI 2023: 1215-1222 - [i30]Anamaria Crisan, Lars Kotthoff, Marc Streit, Kai Xu:
Human-Centered Approaches for Provenance in Automated Data Science (Dagstuhl Seminar 23372). Dagstuhl Reports 13(9): 116-136 (2023) - 2022
- [j17]Julia Moosbauer
Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers. IEEE Trans. Evol. Comput. 26(6): 1336-1350 (2022) - [c36]Damir Pulatov, Marie Anastacio, Lars Kotthoff, Holger H. Hoos:
Opening the Black Box: Automated Software Analysis for Algorithm Selection. AutoML 2022: 6/1-18 - [c35]Lars Kotthoff, Sourin Dey, Jake Heil, Vivek Jain, Todd Muller, Alexander Tyrrell, Hud Wahab, Patrick Johnson:
Optimizing Laser-Induced Graphene Production. PAIS@ECAI 2022: 31-44 - [i29]Mehdi Nourelahi, Lars Kotthoff, Peijie Chen, Anh Nguyen:
How explainable are adversarially-robust CNNs? CoRR abs/2205.13042 (2022) - 2021
- [j16]Stefano Bistarelli, Lars Kotthoff, Francesco Santini, Carlo Taticchi:
Summary Report for the Third International Competition On Computational Models of Argumentation. AI Mag. 42(3): 70-73 (2021) - [j15]Martin Binder, Florian Pfisterer, Michel Lang
mlr3pipelines - Flexible Machine Learning Pipelines in R. J. Mach. Learn. Res. 22: 184:1-184:7 (2021) - [c34]Haniye Kashgarani, Lars Kotthoff:
Is Algorithm Selection Worth It? Comparing Selecting Single Algorithms and Parallel Execution. MetaDL@AAAI 2021: 58-64 - [i28]Lars Kotthoff, Sourin Dey, Vivek Jain, Alexander Tyrrell, Hud Wahab, Patrick Johnson:
Modeling and Optimizing Laser-Induced Graphene. CoRR abs/2107.14257 (2021) - [i27]Lars Kotthoff, Hud Wahab, Patrick Johnson:
Bayesian Optimization in Materials Science: A Survey. CoRR abs/2108.00002 (2021) - [i26]Julia Moosbauer, Martin Binder, Lennart Schneider, Florian Pfisterer, Marc Becker, Michel Lang, Lars Kotthoff, Bernd Bischl:
Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers. CoRR abs/2111.14756 (2021) - 2020
- [c33]Damir Pulatov, Lars Kotthoff:
Opening the Black Box: Automatically Characterizing Software for Algorithm Selection (Student Abstract). AAAI 2020: 13899-13900 - [c32]Stefano Bistarelli, Lars Kotthoff, Francesco Santini, Carlo Taticchi:
A First Overview of ICCMA'19. AI³@AI*IA 2020: 90-102 - [i25]Md Shahriar Iqbal, Jianhai Su, Lars Kotthoff, Pooyan Jamshidi
FlexiBO: Cost-Aware Multi-Objective Optimization of Deep Neural Networks. CoRR abs/2001.06588 (2020)
2010 – 2019
- 2019
- [j14]Marius Lindauer
The algorithm selection competitions 2015 and 2017. Artif. Intell. 272: 86-100 (2019) - [j13]Michel Lang
mlr3: A modern object-oriented machine learning framework in R. J. Open Source Softw. 4(44): 1903 (2019) - [c31]Joeran Beel, Lars Kotthoff:
Preface: The 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR). AMIR@ECIR 2019: 1-9 - [c30]Lars Kotthoff:
Hands-On Session with ASlib. AMIR@ECIR 2019: 71 - [c29]Jöran Beel, Lars Kotthoff
Proposal for the 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR). ECIR (2) 2019: 383-388 - [c28]Md Shahriar Iqbal, Lars Kotthoff, Pooyan Jamshidi:
Transfer Learning for Performance Modeling of Deep Neural Network Systems. OpML 2019: 43-46 - [p6]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 - [e6]Frank Hutter, Lars Kotthoff, Joaquin Vanschoren
Automated Machine Learning - Methods, Systems, Challenges. The Springer Series on Challenges in Machine Learning, Springer 2019, ISBN 978-3-030-05317-8 [contents] - [e5]Jöran Beel, Lars Kotthoff:
Proceedings of the 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval co-located with the 41st European Conference on Information Retrieval (ECIR 2019), Cologne, Germany, April 14, 2019. CEUR Workshop Proceedings 2360, CEUR-WS.org 2019 [contents] - [d1]Michel Lang
mlr3: A modern object-oriented machine learning framework in R. Zenodo, 2019 - [i24]Md Shahriar Iqbal, Lars Kotthoff, Pooyan Jamshidi:
Transfer Learning for Performance Modeling of Deep Neural Network Systems. CoRR abs/1904.02838 (2019) - 2018
- [j12]Pascal Kerschke
Leveraging TSP Solver Complementarity through Machine Learning. Evol. Comput. 26(4) (2018) - [c27]Stefano Bistarelli, Lars Kotthoff, Francesco Santini, Carlo Taticchi:
Containerisation and Dynamic Frameworks in ICCMA'19. SAFA@COMMA 2018: 4-9 - [c26]Lars Kotthoff
Quantifying Algorithmic Improvements over Time. IJCAI 2018: 5165-5171 - [c25]Hans Degroote, Patrick De Causmaecker, Bernd Bischl, Lars Kotthoff:
A Regression-Based Methodology for Online Algorithm Selection. SOCS 2018: 37-45 - [i23]Marius Lindauer, Jan N. van Rijn, Lars Kotthoff:
The Algorithm Selection Competition Series 2015-17. CoRR abs/1805.01214 (2018) - 2017
- [j11]Lars Kotthoff
The ICON Challenge on Algorithm Selection. AI Mag. 38(2): 91-93 (2017) - [j10]Christian Bessiere, Luc De Raedt
The Inductive Constraint Programming Loop. IEEE Intell. Syst. 32(5): 44-52 (2017) - [j9]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) - [i22]Marius Lindauer
Open Algorithm Selection Challenge 2017: Setup and Scenarios. OASC 2017: 1-7 - [i21]Chris Fawcett, Lars Kotthoff, Holger H. Hoos:
Hot-Rodding the Browser Engine: Automatic Configuration of JavaScript Compilers. CoRR abs/1707.04245 (2017) - 2016
- [j8]Bernd Bischl, Pascal Kerschke
ASlib: A benchmark library for algorithm selection. Artif. Intell. 237: 41-58 (2016) - [j7]Bernd Bischl, Michel Lang, Lars Kotthoff, Julia Schiffner, Jakob Richter, Erich Studerus, Giuseppe Casalicchio, Zachary M. Jones:
mlr: Machine Learning in R. J. Mach. Learn. Res. 17: 170:1-170:5 (2016) - [c24]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 - [c23]Hans Degroote, Bernd Bischl, Lars Kotthoff, Patrick De Causmaecker:
Reinforcement Learning for Automatic Online Algorithm Selection - an Empirical Study. ITAT 2016: 93-101 - [c22]Lars Kotthoff
Portfolios of Subgraph Isomorphism Algorithms. LION 2016: 107-122 - [p5]Lars Kotthoff
Algorithm Selection for Combinatorial Search Problems: A Survey. Data Mining and Constraint Programming 2016: 149-190 - [p4]Barry Hurley, Lars Kotthoff
Advanced Portfolio Techniques. Data Mining and Constraint Programming 2016: 191-225 - [p3]Christian Bessiere, Luc De Raedt
The Inductive Constraint Programming Loop. Data Mining and Constraint Programming 2016: 303-309 - [p2]Mirco Nanni, Lars Kotthoff
ICON Loop Carpooling Show Case. Data Mining and Constraint Programming 2016: 310-324 - [p1]Barry Hurley, Lars Kotthoff
ICON Loop Health Show Case. Data Mining and Constraint Programming 2016: 325-333 - [e4]Frank Hutter, Lars Kotthoff, Joaquin Vanschoren:
Proceedings of the 2016 Workshop on Automatic Machine Learning, AutoML 2016, co-located with 33rd International Conference on Machine Learning (ICML 2016), New York City, NY, USA, June 24, 2016. JMLR Workshop and Conference Proceedings 64, JMLR.org 2016 [contents] - [e3]Christian Bessiere, Luc De Raedt, Lars Kotthoff, Siegfried Nijssen, Barry O'Sullivan, Dino Pedreschi:
Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach. Lecture Notes in Computer Science 10101, Springer 2016, ISBN 978-3-319-50136-9 [contents] - [i20]Julia Schiffner, Bernd Bischl, Michel Lang, Jakob Richter, Zachary M. Jones, Philipp Probst, Florian Pfisterer, Mason Gallo, Dominik Kirchhoff, Tobias Kühn, Janek Thomas, Lars Kotthoff:
mlr Tutorial. CoRR abs/1609.06146 (2016) - 2015
- [j6]Lars Kotthoff:
On Algorithm Selection, with an application to combinatorial search problems. Constraints An Int. J. 20(4): 481-482 (2015) - [c21]Lars Kotthoff
Find Your Way Back: Mobility Profile Mining with Constraints. CP 2015: 638-653 - [c20]Lars Kotthoff
Improving the State of the Art in Inexact TSP Solving Using Per-Instance Algorithm Selection. LION 2015: 202-217 - [e2]Joaquin Vanschoren, Pavel Brazdil, Christophe G. Giraud-Carrier, Lars Kotthoff:
Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2015 (ECMLPKDD 2015), Porto, Portugal, September 7th, 2015. CEUR Workshop Proceedings 1455, CEUR-WS.org 2015 [contents] - [i19]Neil P. Chue Hong, Tom Crick, Ian P. Gent, Lars Kotthoff, Kenji Takeda:
Top Tips to Make Your Research Irreproducible. CoRR abs/1504.00062 (2015) - [i18]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) - [i17]Christian Bessiere, Luc De Raedt, Tias Guns, Lars Kotthoff, Mirco Nanni, Siegfried Nijssen, Barry O'Sullivan, Anastasia Paparrizou, Dino Pedreschi, Helmut Simonis:
The Inductive Constraint Programming Loop. CoRR abs/1510.03317 (2015) - [i16]Lars Kotthoff:
ICON Challenge on Algorithm Selection. CoRR abs/1511.04326 (2015) - 2014
- [j5]Lars Kotthoff
Algorithm Selection for Combinatorial Search Problems: A Survey. AI Mag. 35(3): 48-60 (2014) - [j4]Thomas W. Kelsey
Qualitative modelling via constraint programming. Constraints An Int. J. 19(2): 163-173 (2014) - [j3]Lars Kotthoff
Reliability of computational experiments on virtualised hardware. J. Exp. Theor. Artif. Intell. 26(1): 33-49 (2014) - [c19]Ian P. Gent
Discriminating Instance Generation for Automated Constraint Model Selection. CP 2014: 356-365 - [c18]Barry Hurley, Lars Kotthoff
Proteus: A Hierarchical Portfolio of Solvers and Transformations. CPAIOR 2014: 301-317 - [c17]Tom W. Kelsey
Web-Scale Distributed eScience AI Search across Disconnected and Heterogeneous Infrastructures. eScience 2014: 39-46 - [c16]Lars Kotthoff:
Towards an Algorithm Selection Standard: Data Format and Tools. MetaSel@ECAI 2014: 1 - [c15]Peter George Johnson, Tina Balke, Lars Kotthoff
Integrating Optimisation And Agent-Based Modelling. ECMS 2014: 775-781 - [c14]Lars Kotthoff
Ranking Algorithms by Performance. LION 2014: 16-20 - [c13]Daniel Geschwender, Frank Hutter, Lars Kotthoff
Algorithm Configuration in the Cloud: A Feasibility Study. LION 2014: 41-46 - [c12]Ian P. Gent, Lars Kotthoff
Recomputation.org: Experiences of Its First Year and Lessons Learned. UCC 2014: 968-973 - [e1]Joaquin Vanschoren, Pavel Brazdil, Carlos Soares, Lars Kotthoff:
Proceedings of the International Workshop on Meta-learning and Algorithm Selection co-located with 21st European Conference on Artificial Intelligence, MetaSel@ECAI 2014, Prague, Czech Republic, August 19, 2014. CEUR Workshop Proceedings 1201, CEUR-WS.org 2014 [contents] - [i15]Sylwester Arabas, Michael R. Bareford, Ian P. Gent, Benjamin M. Gorman, Masih Hajiarabderkani, Tristan Henderson, Luke Hutton
An Open and Reproducible Paper on Openness and Reproducibility of Papers in Computational Science. CoRR abs/1408.2123 (2014) - 2013
- [j2]Vikas Agrawal, Christopher Archibald, Mehul Bhatt
The AAAI-13 Conference Workshops. AI Mag. 34(4): 9- (2013) - [c11]Ozgur Akgun
Automated Symmetry Breaking and Model Selection in Conjure. CP 2013: 107-116 - [c10]Deepak Mehta, Barry O'Sullivan
Lazy Branching for Constraint Satisfaction. ICTAI 2013: 1012-1019 - [i14]Lars Kotthoff:
LLAMA: Leveraging Learning to Automatically Manage Algorithms. CoRR abs/1306.1031 (2013) - [i13]Barry Hurley, Lars Kotthoff, Yuri Malitsky, Barry O'Sullivan:
Proteus: A Hierarchical Portfolio of Solvers and Transformations. CoRR abs/1306.5606 (2013) - [i12]Lars Kotthoff:
Ranking Algorithms by Performance. CoRR abs/1311.4319 (2013) - 2012
- [b1]Lars Kotthoff:
On algorithm selection, with an application to combinatorial search problems. University of St Andrews, UK, 2012 - [j1]Lars Kotthoff
An evaluation of machine learning in algorithm selection for search problems. AI Commun. 25(3): 257-270 (2012) - [c9]Andreas Distler, Christopher Jefferson
The Semigroups of Order 10. CP 2012: 883-899 - [c8]Lars Kotthoff
Hybrid Regression-Classification Models for Algorithm Selection. ECAI 2012: 480-485 - [c7]Dharini Balasubramaniam
An automated approach to generating efficient constraint solvers. ICSE 2012: 661-671 - [i11]Lars Kotthoff, Tom W. Kelsey, Martin McCaffery:
A framework for large-scale distributed AI search across disconnected heterogeneous infrastructures. CoRR abs/1209.3487 (2012) - [i10]Thomas W. Kelsey, Lars Kotthoff, Christopher Jefferson, Stephen A. Linton, Ian Miguel, Peter Nightingale, Ian P. Gent:
Qualitative Modelling via Constraint Programming: Past, Present and Future. CoRR abs/1209.3916 (2012) - [i9]Lars Kotthoff:
Algorithm Selection for Combinatorial Search Problems: A Survey. CoRR abs/1210.7959 (2012) - 2011
- [c6]Ian P. Gent, Lars Kotthoff:
Reliability of Computational Experiments on Virtualised Hardware. AI for Data Center Management and Cloud Computing 2011 - [c5]Lars Kotthoff, Ian P. Gent, Ian Miguel:
A Preliminary Evaluation of Machine Learning in Algorithm Selection for Search Problems. SOCS 2011: 84-91 - [c4]Dharini Balasubramaniam
Dominion: An Architecture-Driven Approach to Generating Efficient Constraint Solvers. WICSA 2011: 228-231 - [c3]Tom W. Kelsey
Exact Closest String as a Constraint Satisfaction Problem. ICCS 2011: 1062-1071 - [i8]Ian P. Gent, Lars Kotthoff:
Reliability of Computational Experiments on Virtualised Hardware. CoRR abs/1110.6288 (2011) - [i7]Ian P. Gent, Christopher Jefferson, Lars Kotthoff, Ian Miguel:
Modelling Constraint Solver Architecture Design as a Constraint Problem. CoRR abs/1110.6290 (2011) - 2010
- [c2]Lars Kotthoff
Ensemble Classification for Constraint Solver Configuration. CP 2010: 321-329 - [c1]Ian P. Gent
Learning When to Use Lazy Learning in Constraint Solving. ECAI 2010: 873-878 - [i6]Lars Kotthoff:
Constraint solvers: An empirical evaluation of design decisions. CoRR abs/1002.0134 (2010) - [i5]Lars Kotthoff:
Dominion -- A constraint solver generator. CoRR abs/1002.0136 (2010) - [i4]Tom W. Kelsey, Lars Kotthoff:
The Exact Closest String Problem as a Constraint Satisfaction Problem. CoRR abs/1005.0089 (2010) - [i3]Lars Kotthoff, Ian P. Gent, Ian Miguel:
Using machine learning to make constraint solver implementation decisions. CoRR abs/1005.3502 (2010) - [i2]Ian P. Gent, Lars Kotthoff, Ian Miguel, Peter Nightingale:
Machine learning for constraint solver design -- A case study for the alldifferent constraint. CoRR abs/1008.4326 (2010) - [i1]Lars Kotthoff, Neil C. A. Moore:
Distributed solving through model splitting. CoRR abs/1008.4328 (2010)
Coauthor Index
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last updated on 2024-10-15 20:47 CEST by the dblp team
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