Demo Data from EDBT 2026 / reviewers in the wild / expert
Stuart Andrews
dblp:10/3101
· DBLP profile ↗
3ranked-venue papers
2as first author
0since 2021 · last 2004
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 first-authorHuman-computer interaction and ubiquitous computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
2 papers |
Learning paradigms · 41% Optimization for machine learning · 22% Kernel, tree and ensemble methods · 19% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Learning paradigms
multiple instance learning |
0.1 | 2 | 2003 | Multiple-Instance Learning via Disjunctive Programming Boosting · NIPS 2003 Support Vector Machines for Multiple-Instance Learning · NIPS 2002 |
Robotics › Motion planning and robot control
mixed integer quadratic programming |
0.0 | 1 | 2002 | Support Vector Machines for Multiple-Instance Learning · NIPS 2002 |
Machine learning › Kernel, tree and ensemble methods
support vector machine |
0.0 | 1 | 2002 | Support Vector Machines for Multiple-Instance Learning · NIPS 2002 |
Methods — techniques the papers use, named apart from their topics
linear relaxation · 0.0column generation · 0.0kernel methods · 0.0heuristic optimization · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2004 | JointViewer - An Interactive System for Exploring Orthopedic DataabstractWe present JointViewer, a software tool to aid orthopedics researchers in exploring complex, in-vivo joint kinematics. Given bone-geometry data and bone-motion information, JointViewer models and visualizes bone inter-spacing in the joint. Next, it proposes and displays plausible ligament paths which connect bones together. Both types of models are constructed through a distancefield approach. Users can maneuver the bones in a joint for better viewing, see motion relative to a specific bone, or remove bones from a joint. We demonstrate JointViewer’s effectiveness in three applications: examining normal human wrist kinematics, capturing the effect of injury on forearm kinematics, and exploring the kinematic constraints imposed by ligaments in a pigeon shoulder. In all applications, the system effectively highlights subtle yet important relationships among bones and soft-tissue that in previous standard joint visualizations had gone unnoticed. G. Elisabeta Marai, Çagatay Demiralp, Stuart Andrews, David H. Laidlaw |
IEEE Visualization | 3 |
| 2003 | Multiple-Instance Learning via Disjunctive Programming BoostingabstractLearning from ambiguous training data is highly relevant in many applications. We present a new learning algorithm for classification problems where labels are associated with sets of pattern instead of individual patterns. This encompasses multiple instance learn- ing as a special case. Our approach is based on a generalization of linear programming boosting and uses results from disjunctive programming to generate successively stronger linear relaxations of a discrete non-convex problem. Stuart Andrews, Thomas Hofmann 0001 |
NIPS | 1 |
| 2002 | Support Vector Machines for Multiple-Instance LearningabstractThis paper presents two new formulations of multiple-instance learning as a maximum margin problem. The proposed extensions of the Support Vector Machine (SVM) learning approach lead to mixed integer quadratic programs that can be solved heuristically. Our generalization of SVMs makes a state-of-the-art classification technique, including non-linear classification via kernels, available to an area that up to now has been largely dominated by special purpose methods. We present experimental results on a pharma(cid:173) ceutical data set and on applications in automated image indexing and document categorization. Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann 0001 |
NIPS | 1 |