Michal Rolinek

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Constraint Satisfaction Problem (CSP) is a fundamental algorithmic problem that appears in many areas of Computer Science. It can be equivalently stated as computing a homomorphism R → Γ between two relational structures, e.g. between two directed graphs. Analyzing its complexity has been a prominent research direction, especially for the fixed template(More)
We consider the problem of minimizing the continuous valued total variation subject to different unary terms on trees and propose fast direct algorithms based on dynamic programming to solve these problems. We treat both the convex and the non-convex case and derive worst case complexities that are equal or better than existing methods. We show applications(More)
The main result of our paper is a generalization of the classical blossom algorithm for finding perfect matchings that can efficiently solve Boolean CSPs where each variable appears in exactly two constraints and all constraints are even ∆-matroid relations (represented by lists of tuples). As a consequence of this, we settle the complexity classification(More)
The accuracy of information retrieval systems is often measured using complex non-decomposable loss functions such as the average precision (AP) or the normalized discounted cumulative gain (NDCG). Given a set of positive (relevant) and negative (non-relevant) samples, the parameters of a retrieval system can be estimated using a rank SVM framework, which(More)
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