arXiv subjects

Norbou Buchler

Publications and source records attributed to Norbou Buchler.

4 recordsLinked to original sources

arXiv2025

Set Visualizations for Comparing and Evaluating Machine Learning Models

Machine learning practitioners often need to compare multiple models to select the best one for their application. However, current methods of comparing models fall short because they rely on aggregate metrics that can be difficult to interpret or do not provide enough information to understand the differences between models. To better support the comparison of models, we propose set visualizations of model outputs to enable easier model-to-model comparison. We outline the requirements for using sets to compare machine learning models and demonstrate how this approach can be applied to various machine learning tasks. We also introduce SetMLVis, an interactive system that utilizes set visualizations to compare object detection models. Our evaluation shows that SetMLVis outperforms traditional visualization techniques in terms of task completion and reduces cognitive workload for users. Supplemental materials can be found at https://osf.io/afksu/?view_only=bb7f259426ad425f81d0518a38c597be.

cs.HC↗

arXiv2018

EXTRA: Explaining Team Recommendation in Networks

State-of-the-art in network science of teams offers effective recommendation methods to answer questions like who is the best replacement, what is the best team expansion strategy, but lacks intuitive ways to explain why the optimization algorithm gives the specific recommendation for a given team optimization scenario. To tackle this problem, we develop an interactive prototype system, EXTRA, as the first step towards addressing such a sense-making challenge, through the lens of the underlying network where teams embed, to explain the team recommendation results. The main advantages are (1) Algorithm efficacy: we propose an effective and fast algorithm to explain random walk graph kernel, the central technique for networked team recommendation; (2) Intuitive visual explanation: we present intuitive visual analysis of the recommendation results, which can help users better understand the rationality of the underlying team recommendation algorithm.

cs.SI↗

arXiv2015

Kinetic and Cyber

We compare and contrast situation awareness in cyber warfare and in conventional, kinetic warfare. Situation awareness (SA) has a far longer history of study and applications in such areas as control of complex enterprises and in conventional warfare, than in cyber warfare. Far more is known about the SA in conventional military conflicts, or adversarial engagements, than in cyber ones. By exploring what is known about SA in conventional, also commonly referred to as kinetic, battles, we may gain insights and research directions relevant to cyber conflicts. We discuss the nature of SA in conventional (often called kinetic) conflict, review what is known about this kinetic SA (KSA), and then offer a comparison with what is currently understood regarding the cyber SA (CSA). We find that challenges and opportunities of KSA and CSA are similar or at least parallel in several important ways. With respect to similarities, in both kinetic and cyber worlds, SA strongly impacts the outcome of the mission. Also similarly, cognitive biases are found in both KSA and CSA. As an example of differences, KSA often relies on commonly accepted, widely used organizing representation - map of the physical terrain of the battlefield. No such common representation has emerged in CSA, yet.

cs.CY↗

arXiv2014

Replacing the Irreplaceable: Fast Algorithms for Team Member Recommendation

In this paper, we study the problem of Team Member Replacement: given a team of people embedded in a social network working on the same task, find a good candidate who can fit in the team after one team member becomes unavailable. We conjecture that a good team member replacement should have good skill matching as well as good structure matching. We formulate this problem using the concept of graph kernel. To tackle the computational challenges, we propose a family of fast algorithms by (a) designing effective pruning strategies, and (b) exploring the smoothness between the existing and the new team structures. We conduct extensive experimental evaluations on real world datasets to demonstrate the effectiveness and efficiency. Our algorithms (a) perform significantly better than the alternative choices in terms of both precision and recall; and (b) scale sub-linearly.

cs.SI↗