Holger H. Hoos
70 publications
19 venues
H Index 30
Affiliation
RWTH Aachen University, Computer Science Department, Aachen, GermanyLeiden University, LIACS, The Netherlands
University of British Columbia, Vancouver, BC, Canada
Links
DBLPGoogle Scholar
Twitter- https://hoos.ca/
- http://www.cs.ubc.ca/~hoos/
- 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://orcid.org/0000-0003-0629-0099
- https://www.wikidata.org/entity/Q5880225
- http://isni.org/isni/0000000115851148
- https://id.loc.gov/authorities/names/no2004010667
| Name | Venue | Year | citations |
|---|---|---|---|
Accelerating Adversarially Robust Model Selection for Deep Neural Networks via Racing.
|
AAAI | 2024 | 0 |
| Revisiting SATZilla Features in 2024.
|
SAT | 2024 | 0 |
| Automated Design of Linear Bounding Functions for Sigmoidal Nonlinearities in Neural Networks.
|
ECML/PKDD | 2024 | 0 |
| Critically Assessing the State of the Art in Neural Network Verification.
|
JMLR | 2024 | 0 |
| 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 |