17 citations · 20 across the 9 of their papers we have counts for
12 papers
Search Strategies for Optimal Classification and Regression Trees
Jacobus G. M. van der Linden, Mim van den Bos, Emir Demirović
Optimal decision trees (ODTs) are compact, interpretable machine learning models that globally optimize a given objective, but their scalability remains challenging. While recent w…
Domain-Independent Dynamic Programming with Constraint Propagation
Imko Marijnissen, J. Christopher Beck, Emir Demirović +1
There are two prevalent model-based paradigms for combinatorial problems: 1) state-based representations, such as heuristic search, dynamic programming (DP), and decision diagrams,…
Using Certifying Constraint Solvers for Generating Step-wise Explanations
Ignace Bleukx, Maarten Flippo, Bart Bogaerts +2
In the field of Explainable Constraint Solving, it is common to explain to a user why a problem is unsatisfiable. A recently proposed method for this is to compute a sequence of ex…
SORTeD Rashomon Sets of Sparse Decision Trees: Anytime Enumeration
Elif Arslan, Jacobus G. M. van der Linden, Serge Hoogendoorn +2
Sparse decision tree learning provides accurate and interpretable predictive models that are ideal for high-stakes applications by finding the single most accurate tree within a (s…
Optimal Classification Trees for Continuous Feature Data Using Dynamic Programming with Branch-and-Bound
Catalin E. Brita, Jacobus G. M. van der Linden, Emir Demirović
Computing an optimal classification tree that provably maximizes training performance within a given size limit, is NP-hard, and in practice, most state-of-the-art methods do not s…
How To Discover Short, Shorter, and the Shortest Proofs of Unsatisfiability: A Branch-and-Bound Approach for Resolution Proof Length Minimization
Konstantin Sidorov, Koos van der Linden, Gonçalo Homem de Almeida Correia +2
Modern software for propositional satisfiability problems gives a powerful automated reasoning toolkit, capable of outputting not only a satisfiable/unsatisfiable signal but also a…
Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance
Jacobus G. M. van der Linden, Daniël Vos, Mathijs M. de Weerdt +2
Recently there has been a surge of interest in optimal decision tree (ODT) methods that globally optimize accuracy directly, in contrast to traditional approaches that locally opti…
In Search of Trees: Decision-Tree Policy Synthesis for Black-Box Systems via Search
Emir Demirović, Christian Schilling, Anna Lukina
Decision trees, owing to their interpretability, are attractive as control policies for (dynamical) systems. Unfortunately, constructing, or synthesising, such policies is a challe…
Optimal Survival Trees: A Dynamic Programming Approach
Tim Huisman, Jacobus G. M. van der Linden, Emir Demirović
Survival analysis studies and predicts the time of death, or other singular unrepeated events, based on historical data, while the true time of death for some instances is unknown.…
Optimal Decision Trees for Nonlinear Metrics
Emir Demirović, Peter J. Stuckey
Nonlinear metrics, such as the F1-score, Matthews correlation coefficient, and Fowlkes-Mallows index, are often used to evaluate the performance of machine learning models, in part…
MurTree: Optimal Classification Trees via Dynamic Programming and Search
Emir Demirović, Anna Lukina, Emmanuel Hebrard +5
Decision tree learning is a widely used approach in machine learning, favoured in applications that require concise and interpretable models. Heuristic methods are traditionally us…
Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems
Jaynta Mandi, Emir Demirović, Peter. J Stuckey +1
Combinatorial optimization assumes that all parameters of the optimization problem, e.g. the weights in the objective function is fixed. Often, these weights are mere estimates and…