Holger H. Hoos
66 publications
19 venues
h-Index 30
Google's h-Index 75
Affiliation
RWTH Aachen University, Computer Science Department, Aachen, GermanyLeiden University, LIACS, The Netherlands
University of British Columbia, Vancouver, BC, Canada
Links
DBLP
ORCID
Twitter- https://hoos.ca/
- http://www.cs.ubc.ca/~hoos/
- https://scholar.google.com/citations?user=l6c85tMAAAAJ
- https://dl.acm.org/profile/81100549996
- https://zbmath.org/authors/?q=ai:hoos.holger-h
- https://mathscinet.ams.org/mathscinet/MRAuthorID/624612
- https://mathgenealogy.org/id.php?id=79366
- https://en.wikipedia.org/wiki/Holger_H._Hoos
- https://www.wikidata.org/entity/Q5880225
- http://isni.org/isni/0000000115851148
- https://id.loc.gov/authorities/names/no2004010667
Additional Information
Achievements
2023 - Research.com Computer Science in Netherlands Leader Award2020 - ACM FellowFor contributions to automated algorithm selection and configuration for optimization and machine learning
2015 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI)For significant contributions to the field of automated reasoning and the development of widely used methods for algorithm selection and configuration
Google Citations
31993| Name | Venue | Year | citations |
|---|---|---|---|
Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio.
|
MLJ | 2022 | 0 |
| VPint: value propagation-based spatial interpolation.
|
DMKD | 2022 | 1 |
| Automated Configuration and Usage of Strategy Portfolios Mixed-Motive Bargaining.
|
AAMAS | 2022 | 0 |
| Exact stochastic constraint optimisation with applications in network analysis.
|
Artificial Intelligence | 2022 | 0 |
| MultiETSC: automated machine learning for early time series classification.
|
DMKD | 2021 | 1 |
| Hyper-parameter Optimization for Latent Spaces.
|
ECML/PKDD | 2021 | 1 |
| Efficient Local Search for Pseudo Boolean Optimization.
|
SAT | 2021 | 2 |
| Statistical Comparison of Algorithm Performance Through Instance Selection.
|
CP | 2021 | 1 |
| Automated Configuration of Negotiation Strategies.
|
AAMAS | 2020 | 2 |
| A survey on semi-supervised learning.
|
MLJ | 2020 | 0 |
| Scalable constraint-based virtual data center allocation.
|
Artificial Intelligence | 2020 | 0 |
| Local Search with Efficient Automatic Configuration for Minimum Vertex Cover.
|
IJCAI | 2019 | 22 |
| LSQ++: Lower Running Time and Higher Recall in Multi-codebook Quantization.
|
ECCV | 2018 | 25 |
| Portfolio-Based Algorithm Selection for Circuit QBFs.
|
CP | 2018 | 15 |
| Quantifying Algorithmic Improvements over Time.
|
IJCAI | 2018 | 6 |
| Efficient benchmarking of algorithm configurators via model-based surrogates.
|
MLJ | 2018 | 0 |
| Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA.
|
JMLR | 2017 | 575 |
| Scalable Constraint-based Virtual Data Center Allocation.
|
IJCAI | 2017 | 17 |
| Automatic construction of parallel portfolios via algorithm configuration.
|
Artificial Intelligence | 2017 | 27 |
| AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract).
|
IJCAI | 2017 | 8 |
| Efficient Parameter Importance Analysis via Ablation with Surrogates.
|
AAAI | 2017 | 31 |
| The Configurable SAT Solver Challenge (CSSC).
|
Artificial Intelligence | 2017 | 0 |
| Bias in Algorithm Portfolio Performance Evaluation.
|
IJCAI | 2016 | 7 |
| Revisiting Additive Quantization.
|
ECCV | 2016 | 49 |
| Using the Shapley Value to Analyze Algorithm Portfolios.
|
AAAI | 2016 | 30 |
| ASlib: A benchmark library for algorithm selection.
|
Artificial Intelligence | 2016 | 0 |
| SATenstein: Automatically building local search SAT solvers from components.
|
Artificial Intelligence | 2016 | 0 |
| Algorithm Runtime Prediction: Methods and Evaluation (Extended Abstract).
|
IJCAI | 2015 | 60 |
| Efficient Benchmarking of Hyperparameter Optimizers via Surrogates.
|
AAAI | 2015 | 106 |
| AutoFolio: An Automatically Configured Algorithm Selector.
|
JAIR | 2015 | 115 |
| On the Empirical Time Complexity of Random 3-SAT at the Phase Transition.
|
IJCAI | 2015 | 17 |
| SAT Modulo Monotonic Theories.
|
AAAI | 2015 | 0 |
| Improved Features for Runtime Prediction of Domain-Independent Planners.
|
ICAPS | 2014 | 51 |
| An Efficient Approach for Assessing Hyperparameter Importance.
|
ICML | 2014 | 329 |
| Algorithm runtime prediction: Methods & evaluation.
|
Artificial Intelligence | 2014 | 0 |
| Automatic Generation of Efficient Domain-Optimized Planners from Generic Parametrized Planners.
|
SOCS | 2013 | 27 |
| Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms.
|
KDD | 2013 | 0 |
| Evaluating Component Solver Contributions to Portfolio-Based Algorithm Selectors.
|
SAT | 2012 | 103 |
| Predicting Satisfiability at the Phase Transition.
|
AAAI | 2012 | 22 |
| Captain Jack: New Variable Selection Heuristics in Local Search for SAT.
|
SAT | 2011 | 48 |
| Automated Configuration of Mixed Integer Programming Solvers.
|
CPAIOR | 2010 | 186 |
| Computer-Aided Algorithm Design: Automated Tuning, Configuration, Selection, and Beyond.
|
ICAPS | 2010 | 0 |
| Dynamic Scoring Functions with Variable Expressions: New SLS Methods for Solving SAT.
|
SAT | 2010 | 25 |
| Hydra: Automatically Configuring Algorithms for Portfolio-Based Selection.
|
AAAI | 2010 | 197 |
| ParamILS: An Automatic Algorithm Configuration Framework.
|
JAIR | 2009 | 992 |
| SATenstein: Automatically Building Local Search SAT Solvers from Components.
|
IJCAI | 2009 | 205 |
| SATzilla: Portfolio-based Algorithm Selection for SAT.
|
JAIR | 2008 | 915 |
| Hierarchical Hardness Models for SAT.
|
CP | 2007 | 55 |
| Automatic Algorithm Configuration Based on Local Search.
|
AAAI | 2007 | 325 |
| : The Design and Analysis of an Algorithm Portfolio for SAT.
|
CP | 2007 | 142 |
| Performance Prediction and Automated Tuning of Randomized and Parametric Algorithms.
|
CP | 2006 | 202 |
| Dynamic Local Search for the Maximum Clique Problem.
|
JAIR | 2006 | 0 |
| Efficient Stochastic Local Search for MPE Solving.
|
IJCAI | 2005 | 51 |
| Understanding Random SAT: Beyond the Clauses-to-Variables Ratio.
|
CP | 2004 | 208 |
| UBCSAT: An Implementation and Experimentation Environment for SLS Algorithms for SAT & MAX-SAT.
|
SAT | 2004 | 197 |
| CP-nets: A Tool for Representing and Reasoning with Conditional Ceteris Paribus Preference Statements.
|
JAIR | 2004 | 0 |
| Using Stochastic Local Search to Solve Quantified Boolean Formulae.
|
CP | 2003 | 38 |
| Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT.
|
CP | 2002 | 240 |
| Bidding Languages for Combinatorial Auctions.
|
IJCAI | 2001 | 278 |
| To Encode or Not to Encode - Linear Planning.
|
IJCAI | 1999 | 10 |
| Towards a Characterisation of the Behaviour of Stochastic Local Search Algorithms for SAT.
|
Artificial Intelligence | 1999 | 100 |
| Reasoning With Conditional Ceteris Paribus Preference Statements.
|
UAI | 1999 | 330 |
| SAT-Encodings, Search Space Structure, and Local Search Performance.
|
IJCAI | 1999 | 86 |
| Some Surprising Regularities in the Behaviour of Stochastic Local Search.
|
CP | 1998 | 7 |
| Evaluating Las Vegas Algorithms: Pitfalls and Remedies.
|
UAI | 1998 | 174 |
| GSAT versus Simulated Annealing.
|
ECAI | 1994 | 25 |