881 citations · 1.2k across the 9 of their papers we have counts for
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Improving the Performance of Stochastic Local Search for Maximum Vertex Weight Clique Problem Using Programming by Optimization
Yi Chu, Chuan Luo, Holger H. Hoos +2
The maximum vertex weight clique problem (MVWCP) is an important generalization of the maximum clique problem (MCP) that has a wide range of real-world applications. In situations…
Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates
Katharina Eggensperger, Marius Lindauer, Holger H. Hoos +2
The optimization of algorithm (hyper-)parameters is crucial for achieving peak performance across a wide range of domains, ranging from deep neural networks to solvers for hard com…
Evaluating Las Vegas Algorithms - Pitfalls and Remedies
Holger H. Hoos, Thomas Stutzle
Stochastic search algorithms are among the most sucessful approaches for solving hard combinatorial problems. A large class of stochastic search approaches can be cast into the fra…
Reasoning With Conditional Ceteris Paribus Preference Statem
Craig Boutilier, Ronen I. Brafman, Holger H. Hoos +1
In many domains it is desirable to assess the preferences of users in a qualitative rather than quantitative way. Such representations of qualitative preference orderings form an i…
CP-nets: A Tool for Representing and Reasoning withConditional Ceteris Paribus Preference Statements
C. Boutilier, R. I. Brafman, C. Domshlak +2
Information about user preferences plays a key role in automated decision making. In many domains it is desirable to assess such preferences in a qualitative rather than quantitati…