arXiv subjects

Philip Beaucamp

Publications and source records attributed to Philip Beaucamp.

3 recordsLinked to original sources

arXiv2026

Understanding How Humans Inject Knowledge into Machine Learning Workflows through Visual Analytics

Visual analytics (VA) plays an increasingly important role in supporting machine learning (ML) workflows. In the field of visualization, such approaches and techniques are referred to as VIS4ML. While ML models are mostly learned automatically, the corresponding ML workflows receive a variety of human inputs, such as data labelling, feature engineering, model architecture designing, hyper-parameter tuning, and so on. In this work, we surveyed over 200 VIS4ML papers to gain an understanding of how humans inject their knowledge into ML workflows through interactive visualization. We collected a corpus of VIS4ML papers from the IEEE VIS conferences in the past decade. We developed a coding scheme to facilitate the literature research from four perspectives: characteristics of ML, visualization, interaction, and actions. The analysis of the coded dataset allows us to observe different pathways that transfer human knowledge to ML workflows via interactive visualization. Building on the analysis, we explain the phenomena of VIS4ML using the conceptual model that views VA as model building and the information-theoretic cost-benefit analysis that reasons VA as for optimizing ML workflows. This work provides unequivocal evidence showing the merits of using VA in ML workflows. The full list of surveyed papers, along with all analysis results and figures, is available at https://vis4ml4hd.github.io/ml-knowledge-inject-va/.

cs.HC↗

arXiv2026

Optimizing Visual Analytics Workflows: From Theory to Practice

The principle of visual analytics (VA) is to provide integrated workflows where human-centric processes (e.g., visualization and interaction) and machine-centric processes (e.g., statistics and algorithms) complement each other. To implement this principle in practice, it is necessary to reason about the trade-offs among different processes and make optimal use of them in a workflow. Building on an existing ontology of the methodology for analyzing such trade-offs information-theoretically and for optimizing VA workflows systematically, we investigate ways to transform this methodology from theory to practice. In particular, we adopted the action research method. Through case studies in different application domains, VA researchers with different background knowledge and experiences offered their answers to several hypotheses about using the methodology in practice and proposed ways forward. In this paper, we present our collective analysis, the strengths and feasibility of this theory-based methodology, as well as the obstacles to its broad deployment in practice. To address these challenges, we outline a roadmap to remove such obstacles.

cs.HC↗

arXiv2025

Data Virtualization for Machine Learning

Nowadays, machine learning (ML) teams have multiple concurrent ML workflows for different applications. Each workflow typically involves many experiments, iterations, and collaborative activities and commonly takes months and sometimes years from initial data wrangling to model deployment. Organizationally, there is a large amount of intermediate data to be stored, processed, and maintained. \emph{Data virtualization} becomes a critical technology in an infrastructure to serve ML workflows. In this paper, we present the design and implementation of a data virtualization service, focusing on its service architecture and service operations. The infrastructure currently supports six ML applications, each with more than one ML workflow. The data virtualization service allows the number of applications and workflows to grow in the coming years.

cs.SE↗