AI Seminar by Emir Demirović & Meghyn Bienvenu - National ...
On October 22nd, we will hold the following open AI seminar. The lecturers are Emir Demirović and Meghyn Bienvenu. Anyone is welcome to attend.
Optimal Decision Trees (with Constraints) via Dynamic Programming and Search
Decision trees are an effective and concise way of conveying information, easily understood by virtually everyone. 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 more efficient algorithms than structure-oblivious approaches.
In this talk, there will be an overview of the research on constructing optimal regression/decision trees, i.e., trees that best represent tabular data whilst respecting different types of constraints such as fairness and size. It will be shown that techniques based on dynamic programming and search are able to obtain orders of magnitude improvements in runtime over state-of-the-art approaches.
The framework also supports a range of different objectives and constraints, e.g., fairness, survival analysis, nonlinear metrics such as F1-score. The success of the approach is attributed to a series of specialised techniques that exploit properties unique to decision/regression trees. The talk summarises about half a dozen of research papers and is meant to be accessible to all backgrounds, with plenty of time for discussion.
KR Meets Data Quality
Real-world data notoriously suffers from various forms of imperfections (missing facts, erroneous facts, duplicates, etc.), which can limit its utility and lead to flawed analyses and unreliable decision making. This makes data quality an issue of paramount importance across application domains, and one which can both benefit from research on Knowledge Representation and Reasoning (KR) and serve as a testbed for KR techniques.
Declarative approaches to improving data quality remain highly relevant, due to their better interpretability. In this talk, the synergy between data quality and KR will be illustrated by giving an overview of recent work on querying inconsistent data using repair-based semantics and on rule-based approaches to entity resolution, highlighting the insights gained and directions for future research.
Join us at the seminar on Tuesday, October 22, 2024 from 15:00 to 17:00 in Room #1509 (15F). If you would like to join, please contact by email at inoue[at]nii.ac.jp.
