4 citations · 4 across the 9 of their papers we have counts for
10 papers
Forest-Guided Semantic Transport for Label-Supervised Manifold Alignment
Adrien Aumon, Myriam Lizotte, Guy Wolf +2
Label-supervised manifold alignment bridges the gap between unsupervised and correspondence-based paradigms by leveraging shared label information to align multimodal datasets. How…
Revisiting Forest Proximities via Sparse Leaf-Incidence Kernels
Adrien Aumon, Guy Wolf, Kevin R. Moon +1
Decision forests induce supervised similarities through the partition structure of their trees. Yet forest proximity computation is still often treated as a quadratic operation in…
The Generalized Proximity Forest
Ben Shaw, Adam Rustad, Sofia Pelagalli Maia +2
Recent work has demonstrated the utility of Random Forest (RF) proximities for various supervised machine learning tasks, including outlier detection, missing data imputation, and…
Freeze, Diffuse, Decode: Task-Aware Adaptation of Transformer Embeddings for Antimicrobial Peptide Design
Pankhil Gawade, Adam Izdebski, Myriam Lizotte +4
Pretrained transformers provide rich, general-purpose embeddings, which are transferred to downstream tasks. However, current transfer strategies: fine-tuning and probing, either d…
Random Forest Autoencoders for Guided Representation Learning
Adrien Aumon, Shuang Ni, Myriam Lizotte +3
Extensive research has produced robust methods for unsupervised data visualization. Yet supervised visualization$\unicode{x2013}$where expert labels guide representations$\unicode{…
Forest Proximities for Time Series
Ben Shaw, Jake Rhodes, Soukaina Filali Boubrahimi +1
RF-GAP has recently been introduced as an improved random forest proximity measure. In this paper, we present PF-GAP, an extension of RF-GAP proximities to proximity forests, an ac…
Enhancing Supervised Visualization through Autoencoder and Random Forest Proximities for Out-of-Sample Extension
Shuang Ni, Adrien Aumon, Guy Wolf +2
The value of supervised dimensionality reduction lies in its ability to uncover meaningful connections between data features and labels. Common dimensionality reduction methods emb…
Supervised Manifold Learning via Random Forest Geometry-Preserving Proximities
Jake S. Rhodes
Manifold learning approaches seek the intrinsic, low-dimensional data structure within a high-dimensional space. Mainstream manifold learning algorithms, such as Isomap, UMAP, -…
Geometry- and Accuracy-Preserving Random Forest Proximities
Jake S. Rhodes, Adele Cutler, Kevin R. Moon
Random forests are considered one of the best out-of-the-box classification and regression algorithms due to their high level of predictive performance with relatively little tunin…
Supervised Visualization for Data Exploration
Jake S. Rhodes, Adele Cutler, Guy Wolf +1
Dimensionality reduction is often used as an initial step in data exploration, either as preprocessing for classification or regression or for visualization. Most dimensionality re…