# Into the Void: Mapping the Unseen Gaps in High Dimensional Data

arXiv: https://arxiv.org/abs/2501.15273 · HTML: https://mlsanity.com/paper/2501.15273/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 58 references that are in the map.
- First posted on arXiv 25 Jan 2025.

## Builds on (most cited first, 11 of 11)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Causal Reasoning and Large Language Models: Opening a New Frontier for Causality](https://mlsanity.com/paper/2305.00050/) (2023)
- [Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits](https://mlsanity.com/paper/2002.02518/) (2020)
- [Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning](https://mlsanity.com/paper/2008.10740/) (2020)
- [Real-time Network Intrusion Detection via Decision Transformers](https://mlsanity.com/paper/2312.07696/) (2023)
- [Feature Learning for Nonlinear Dimensionality Reduction toward Maximal Extraction of Hidden Patterns](https://mlsanity.com/paper/2206.13891/) (2022)
- [The Subspace Voyager: Exploring High-Dimensional Data along a Continuum of Salient 3D Subspaces](https://mlsanity.com/paper/1603.04781/) (2016)
- [An Explainable AI Approach to Large Language Model Assisted Causal Model Auditing and Development](https://mlsanity.com/paper/2312.16211/) (2023)
- [Neural Optimization Machine: A Neural Network Approach for Optimization](https://mlsanity.com/paper/2208.03897/) (2022)
- [A Population-based Hybrid Approach to Hyperparameter Optimization for Neural Networks](https://mlsanity.com/paper/2011.11062/) (2020)
- [ScatterUQ: Interactive Uncertainty Visualizations for Multiclass Deep Learning Problems](https://mlsanity.com/paper/2308.04588/) (2023)

## Built on by (most cited first, 1 of 1)

- [Understanding Differentiable Embeddings Through Differential and Integral Geometry](https://mlsanity.com/paper/2608.06809/) (2026)

## Abstract (arXiv)

We present a comprehensive pipeline, augmented by a visual analytics system named ``GapMiner'', that is aimed at exploring and exploiting untapped opportunities within the empty areas of high-dimensional datasets. Our approach begins with an initial dataset and then uses a novel Empty Space Search Algorithm (ESA) to identify the center points of these uncharted voids, which are regarded as reservoirs containing potentially valuable novel configurations. Initially, this process is guided by user interactions facilitated by GapMiner. GapMiner visualizes the Empty Space Configurations (ESC) identified by the search within the context of the data, enabling domain experts to explore and adjust ESCs using a linked parallel-coordinate display. These interactions enhance the dataset and contribute to the iterative training of a connected deep neural network (DNN). As the DNN trains, it gradually assumes the task of identifying high-potential ESCs, diminishing the need for direct user involvement. Ultimately, once the DNN achieves adequate accuracy, it autonomously guides the exploration of optimal configurations by predicting performance and refining configurations, using a combination of gradient ascent and improved empty-space searches. Domain users were actively engaged throughout the development of our system. Our findings demonstrate that our methodology consistently produces substantially superior novel configurations compared to conventional randomization-based methods. We illustrate the effectiveness of our method through several case studies addressing various objectives, including parameter optimization, adversarial learning, and reinforcement learning.