Simplification by Cooperating Decision Procedures
ACM Transactions on Programming Languages and Systems (TOPLAS)
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Ridge Regression Learning Algorithm in Dual Variables
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Active exploration for learning rankings from clickthrough data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Structured learning for non-smooth ranking losses
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Multiobjective Optimization: Interactive and Evolutionary Approaches
Multiobjective Optimization: Interactive and Evolutionary Approaches
Direct Zero-Norm Optimization for Feature Selection
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Reactive Search and Intelligent Optimization
Reactive Search and Intelligent Optimization
Efficiency versus convergence of Boolean kernels for on-line learning algorithms
Journal of Artificial Intelligence Research
Satisfiability Modulo Theories: An Appetizer
Formal Methods: Foundations and Applications
Incorporating diversity and density in active learning for relevance feedback
ECIR'07 Proceedings of the 29th European conference on IR research
Interval-valued soft constraint problems
Annals of Mathematics and Artificial Intelligence
A fast linear-arithmetic solver for DPLL(T)
CAV'06 Proceedings of the 18th international conference on Computer Aided Verification
DPLL(T) with exhaustive theory propagation and its application to difference logic
CAV'05 Proceedings of the 17th international conference on Computer Aided Verification
On SAT modulo theories and optimization problems
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
Satisfiability modulo the theory of costs: foundations and applications
TACAS'10 Proceedings of the 16th international conference on Tools and Algorithms for the Construction and Analysis of Systems
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We address the problem of automated discovery of preferred solutions by an interactive optimization procedure. The algorithm iteratively learns a utility function modeling the quality of candidate solutions and uses it to generate novel candidates for the following refinement. We focus on combinatorial utility functions made of weighted conjunctions of Boolean variables. The learning stage exploits the sparsity-inducing property of 1-norm regularization to learn a combinatorial function from the power set of all possible conjunctions up to a certain degree. The optimization stage uses a stochastic local search method to solve a weighted MAX-SAT problem. We show how the proposed approach generalizes to a large class of optimization problems dealing with satisfiability modulo theories. Experimental results demonstrate the effectiveness of the approach in focusing towards the optimal solution and its ability to recover from suboptimal initial choices.