Decision trees in a formal world: machine learning (with constraints), controller verification, and unsatisfiability proofs for graph problems
Speaker:
Assistant professor
Abstract:
Decision trees are an effective and concise way of conveying information, easily understood by virtually everyone regardless of the topic. Given the recent interest in explainable AI and related fields, decision trees stand out as a popular choice. From the algorithmic side, the unique structure of decision trees is interesting since it may be exploited to obtain much more efficient algorithms than structure-oblivious approaches.
In this talk, I will give an overview of the research we have been doing on leveraging the decision tree structure from three disjoint angles: 1) machine learning with constraints, where the goal is construct the optimal regression/decision tree representing tabular data whilst potentially respecting different types of constraints such as fairness, 2) controller/policy verification, where the aim is to verify that a decision tree controller satisfies desired properties in continuous time, and 3) explaining the unsatisfiability of a combinatorial optimisation problem on a graph, by representing proofs of unsatisfiability as a tree using graph-specific concepts. We show that for each of these problems, exploiting the decision tree structure is important in obtain orders of magnitude runtime improvements and/or interpretability.
The talk summarises about half a dozen of our papers (AAAI’21/24, JMLR’22, NeurIPS’22/23, ICML’23/24) and is meant to be accessible to all backgrounds, with plenty of time for discussion!
Biography:
Emir Demirovic is an assistant professor at TU Delft (Netherlands). He leads the Constraint Solving (“ConSol”) research group, which advances combinatorial optimisation algorithms for a wide range of (real-world) problems, and co-directs the explainable AI in transportation lab (“XAIT”) as part of the Delft AI Labs. Prior to his appointment at TU Delft, Emir worked at the University of Melbourne, Vienna University of Technology, National Institute of Informatics (Tokyo), and at a production planning and scheduling company.
The focus point of Emir’s current work is solving techniques based on constraint programming, optimising decision trees, and explainable methods for combinatorial optimisation. He is also interested in industrial applications, robust/resilient optimisation, and the integration of optimisation and machine learning. He publishes in leading AI conferences (e.g., AAAI, NeurIPS) and specialised venues (e.g., CP, CPAIOR), attends scientific events such as Dagstuhl seminars, Lorentz workshops, and the Simons-Berkeley programme, and frequently organises incoming and outgoing visits, e.g., EPFL, ANITI/CNRS, CUHK, Monash University, TU Wien.
Enquiries:
Professor LEE Ho Man Jimmy (jlee@cse.cuhk.edu.hk)
Mr. WONG O Bong (obong@cse.cuhk.edu.hk)
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