On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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8 results for “tree-embeddings”

Positive crossratios, barycenters, trees and applications to maximal representationsmath.GT

Maximal representations are studied using tree embeddings and geodesic currents.

problem Maximal representations of surface groups in symplectic groups.

method Metric properties, geodesic currents, and tree embeddings.

result Translation length can be computed as intersection with a geodesic current.

maximal representations surface groups symplectic groups metric properties geodesic currents

2021-03-31abs ↗pdf ↗

Deep tree-ensembles for multi-output predictioncs.LG

Proposes a deep tree-ensemble model for multi-output prediction.

problem Lack of efficient solutions for multi-output prediction.

method Integrates tree-embeddings into deep tree-ensembles for structured output prediction.

result Superior performance in multi-label classification and multi-target regression tasks.

deep tree-ensembles multi-output prediction tree-embeddings structured output multi-label classification

2020-11-03abs ↗pdf ↗

On oriented planar trees with three leavesmath.GT

The paper explores metrics on tree moduli spaces and a new topological group.

problem Continuous metrics on tree moduli spaces.

method Proposes and analyzes continuous piecewise-smooth metrics.

result Observes a new abelian topological group structure.

metrics tree moduli spaces Riemannian topological group

2020-05-11abs ↗pdf ↗

Halin graphs with positive Lin-Lu-Yau curvaturemath.CO

Study classifies Halin graphs with positive curvature.

problem Classifying Halin graphs with specific curvature.

method Analyzing generalized Halin graphs formed by connecting tree leaves.

result Identified all generalized Halin graphs with positive Lin-Lu-Yau curvature.

Halin graphs curvature tree embeddings planar graphs

2025-05-07abs ↗pdf ↗

Sparse Additive Model Pruning for Order-Based Causal Structure Learningstat.ML

New pruning method for sparse additive models speeds up causal structure learning.

problem Efficiently prune spurious edges from fully-connected DAG induced by estimated topological order.

method Sparse additive models combined with randomized tree embedding and group-wise sparse regression.

result Significantly faster than existing pruning methods while maintaining comparable accuracy.

causal discovery sparse additive models pruning order-based causal structure learning randomized tree embedding

2026-02-17abs ↗pdf ↗

Decision Tree Embedding by Leaf-Meansstat.ML

DTE uses tree leaf means to embed data, balancing accuracy and speed.

problem High variance in decision tree splits and computational inefficiency of ensembles.

method DTE constructs an interpretable feature representation using leaf means of a trained tree.

result DTE strikes a balance between accuracy and computational efficiency, outperforming ensembles.

Decision Trees Random Forest Feature Embedding Linear Discriminant Analysis Tree Partitioning

2025-12-01abs ↗pdf ↗

Towards Scalable Spectral Clustering via Spectrum-Preserving Sparsificationcs.LG

The eigendeomposition of nearest-neighbor (NN) graph Laplacian matrices is the main computational bottleneck in spectral clustering. In this work, we introduce a highly-scalable, spectrum-preserving graph sparsification algorithm that enables to build ultra-sparse NN (u-NN) graphs with guaranteed preservation of the or…

2017-10-12abs ↗pdf ↗

An Empirical Study of Self-supervised Learning with Wasserstein Distancestat.ML

This study investigates self-supervised learning with Wasserstein distance on tree structures.

problem Improving self-supervised learning methods using Wasserstein distance.

method Utilized Tree-Wasserstein distance (TWD) and Jeffrey divergence regularization for training.

result A simple combination of softmax function and Tree-Wasserstein distance outperforms cosine similarity-based methods.

self-supervised learning Wasserstein distance Tree-Wasserstein distance Jeffrey divergence regularization

2023-10-16abs ↗pdf ↗