Efficiently updates posterior tree distributions over meta-trees.
problem Updating posterior distributions over meta-trees efficiently.
method Batch updating method for posterior tree distributions.
result More efficient batch updating method.
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.
168,742 papers · 148 categories
Trend · papers per month
48 results for “Tree Method”
Boosting meta-trees improve decision tree performance.
problem Overfitting in decision trees.
method Boosting approach to construct multiple meta-trees.
result Ensembles of meta-trees prevent overfitting.
Optimal survival trees ensemble reduces tree count and improves predictive performance.
problem Improving predictive performance in survival analysis.
method Grows a forest of optimal survival trees by ranking and selecting the best trees based on out-of-bag error.
result Reduces the number of trees in the ensemble while improving predictive performance.
Improved tree selection methods enhance OTE's performance.
problem Optimal trees ensemble (OTE) underperforms with larger training data.
method Two modified methods: OOB and sub-bagging.
result Improved predictive accuracy compared to OTE and other methods.
Develops a new method for decision trees using categorical variable structure.
problem Lack of structure in treating categorical variables as predictors.
method Introduces a mathematical framework to represent categorical structure and generalizes decision trees to utilize this structure.
result Improves prediction accuracy on weather data using the new method.
Paper studies ensemble probabilistic regression trees for smooth approximations.
problem Smooth approximations of regression functions.
method Ensemble versions of probabilistic regression trees.
result Ensemble probabilistic regression trees are consistent and perform well.
Optimal Survival Trees improve accuracy in medical data analysis.
problem Analyzing censored outcomes in medical data.
method Mixed-integer optimization and local search techniques.
result Improves accuracy in large datasets compared to existing methods.
Evolutionary algorithms improve decision tree ensembles.
problem Improving predictive performance of decision trees.
method Real-valued vector representation of decision trees, evolutionary algorithms (Differential evolution, Evolution strategies).
result Proposed methods outperform classical decision tree induction algorithms.
New framework uses tree ensembles for contextual bandits.
problem Optimizing decisions in dynamic environments with contextual information.
method Adapts Upper Confidence Bound and Thompson Sampling to tree ensemble methods.
result Tree ensemble methods outperform traditional methods in regret minimization and runtime.
This paper introduces TNTK to study infinite soft tree ensembles.
problem Understanding the behavior of infinite soft tree ensembles.
method Introduced Tree Neural Tangent Kernel (TNTK) to analyze infinite soft tree ensembles.
result Identified several non-trivial properties of infinite soft tree ensembles.
A new tree method for tensor data improves regression accuracy.
problem Efficiently modeling tensor data for regression problems.
method Scalar-output regression tree models for scalar-on-tensor problems, and tensor-on-tensor problems using additive tree ensemble approaches.
result The tensor-input tree (TT) method outperforms tensor-input GP models in efficiency and accuracy.
This paper improves Bayesian decision tree learning using HMC.
problem Bayesian decision tree learning is challenging due to a large parameter space.
method Develops and compares HMC-based algorithms for exploring Bayesian decision tree posteriors.
result HMC-based methods outperform existing methods in predictive accuracy and tree complexity.
Stochastic Gradient Treesstat.ML
We present an algorithm for learning decision trees using stochastic gradient information as the source of supervision. In contrast to previous approaches to gradient-based tree learning, our method operates in the incremental learning setting rather than the batch learning setting, and does not make use of soft splits…
A novel gradient-based method for decision trees optimizing arbitrary differential loss functionscs.LG
A novel gradient-based method optimizes decision trees for complex tasks.
problem Training decision trees with arbitrary differentiable loss functions.
method Gradient-based optimization using first and second derivatives of loss functions.
result Improves accuracy and flexibility in decision tree optimization.
Study reviews tree-based methods and introduces new ensemble strategies.
problem Improving the efficiency and performance of tree-based machine learning models.
method Review of tree-based methods, introduction of ISLE framework, ARM model combination strategy, and modified ISLEs.
result Performance evaluation of modified ISLEs on real data sets.
New MC-Tree method combines Monte Carlo and binomial tree for option pricing and CVA.
problem Combining Monte Carlo and binomial tree methods for accurate and efficient option pricing and CVA calculations.
method MC-Tree method that mixes Monte Carlo and binomial tree parameters, using maximum entropy distributions for compound densities.
result MC-Tree method provides accurate and efficient option pricing and CVA calculations.
Kauri is a novel unsupervised binary tree for clustering that outperforms existing methods.
problem Learning a tree end-to-end for clustering without labels is an open challenge.
method Greedy maximization of the kernel KMeans objective without centroids.
result Kauri often outperforms existing unsupervised clustering methods, especially with non-linear kernels.
This work analyzes tree-based methods from a ranking perspective, providing insights and new statistics.
problem Understanding the effectiveness of tree-based methods in finite-sample settings, especially symbolic feature selection.
method Local ranking perspective, finite-sample analysis, oracle bounds, posterior contraction results, concordant divergence statistics.
result New insights and statistics for evaluating symbolic feature mappings.
Ensembles of decision trees perform well on many problems, but are not interpretable. In contrast to existing approaches in interpretability that focus on explaining relationships between features and predictions, we propose an alternative approach to interpret tree ensemble classifiers by surfacing representative poin…
Multistage Defer Trees improve model accuracy while maintaining interpretability.
problem Balancing model accuracy and interpretability, especially in noisy domains.
method A sequence of sparse decision trees that defer predictions to the next tree or a black box.
result Matches the performance of complex tree-based ensembles while using only one or a few sparse trees.
Paper proposes a VB method for TS-SBP mixture models with reduced computational cost.
problem Efficiently learning tree-structured stick-breaking process mixture models.
method Utilizes Bayes coding algorithm for context tree models to calculate sums over all possible trees.
result Proposes a learning algorithm with less computational cost for TS-SBP mixture of Gaussians.
Flexible tree ensemble learning framework supports arbitrary loss functions and multi-task learning.
problem Limited modeling capabilities of existing tree ensemble learning toolkits.
method Differentiable tree ensembles with tensor-based formulation for efficient training.
result Our framework leads to 100x more compact and 23% more expressive tree ensembles.
Towards Scalable Meta-Learning of near-optimal Interpretable Models via Synthetic Model Generationscs.LG
Meta-learning interpretable decision trees with synthetic data.
problem Lack of efficient, scalable methods for generating synthetic data for decision tree meta-learning.
method Synthetic generation of near-optimal decision trees using the MetaTree transformer architecture.
result Meta-learning of decision trees achieves performance comparable to real-world data or optimal decision trees, with significant computational cost reduction.
Oblique BART improves tree-based predictions.
problem Axis-aligned decision rules in BART can be suboptimal.
method Developed an oblique version of BART using data-adaptive hyperplane partitions.
result Oblique BART outperformed axis-aligned BART and other tree methods on benchmarks.
Develops a new method to recover large latent tree models efficiently.
problem Inference of large latent tree structures from terminal node observations.
method Spectral Top-Down Recovery (STDR) using Fiedler vector partitioning.
result Proves statistical consistency and sample complexity for accurate tree recovery.
FoLDTree: A ULDA-Based Decision Tree Framework for Efficient Oblique Splits and Feature Selectioncs.LG
FoLDTree improves oblique decision trees with ULDA, enhancing accuracy and feature selection.
problem Axis-orthogonal splits limit traditional decision trees' performance on oblique decision boundaries.
method Integrates ULDA into decision tree structure for efficient oblique splits, feature selection, and handling missing values.
result FoLDTree outperforms other methods in accuracy and feature selection, comparable to random forest.
Develops a variational method for ultrametric phylogenetic trees.
problem Accurate and efficient approximation of posterior distributions over trees in Bayesian phylogenetics.
method Variational Bayesian approach based on coalescent times of a single-linkage clustering.
result Achieves competitive accuracy with significantly fewer gradient evaluations.
A cluster tree provides a highly-interpretable summary of a density function by representing the hierarchy of its high-density clusters. It is estimated using the empirical tree, which is the cluster tree constructed from a density estimator. This paper addresses the basic question of quantifying our uncertainty by ass…
A new classification method based on Minimum Spanning Trees
problem Improving classification in supervised learning
method Proposing a classification algorithm based on Minimum Spanning Trees
result The proposed method is effective and computationally efficient
The Tree-SNE Tree Existsstat.ML
A new method, tree-SNE, solves the scale problem in t-SNE.
problem Clustering and visualizing high-dimensional data, especially MNIST digits.
method Revisits t-SNE idea to create a 2+1 dimensional embedding with a scale parameter.
result The optimal embedding depends continuously on the scale parameter for all initial conditions.
Two new methods improve monotonic constraint enforcement in regression and classification trees.
problem Improving monotonic constraints in regression and classification trees.
method Proposed two new methods: one yields better results than LightGBM, the other yields even better results but is slower.
result The best method consistently beats the current implementation of LightGBM, achieving up to 1% loss reduction.
JSRT improves regression tree performance by incorporating global node information.
problem Regression tree performance relies on local node means, ignoring global node information.
method Proposes JSRT by integrating global mean information from different nodes.
result Demonstrates superior performance and efficiency compared to other regression tree methods.
Enhanced ODT with Feature Concatenation boosts learning efficiency.
problem Insufficient learning efficiency of ODT due to linear projections not being transmitted to child nodes.
method Feature Concatenation ( exttt{FC-ODT}) to transmit linear projections along decision paths.
result Experiments show exttt{FC-ODT} outperforms state-of-the-art decision trees with a limited tree depth.
In this paper, we propose the distributed tree kernels (DTK) as a novel method to reduce time and space complexity of tree kernels. Using a linear complexity algorithm to compute vectors for trees, we embed feature spaces of tree fragments in low-dimensional spaces where the kernel computation is directly done with dot…
We introduce the concept of community trees that summarizes topological structures within a network. A community tree is a tree structure representing clique communities from the clique percolation method (CPM). The community tree also generates a persistent diagram. Community trees and persistent diagrams reveal topol…
A new supervised tree-Wasserstein distance improves document classification.
problem Measuring document similarity efficiently and accurately.
method Rewriting Wasserstein distance on tree metric, using contrastive loss for optimization.
result The Supervised Tree-Wasserstein (STW) distance improves document classification accuracy.
Paper improves tree probability estimation using stochastic optimization and variance reduction.
problem Improving tree probability estimation in phylogenetic inference.
method Introduces computationally efficient methods for training SBNs and variance reduction for optimization.
result Methods outperform previous baseline methods in tree topology probability estimation and Bayesian phylogenetic inference.
New method learns binary decision trees efficiently.
problem Learning binary decision trees for data partitioning.
method Argmin differentiation for discrete and continuous parameters.
result Produces competitive binary trees with fast training.
Bayesian Rose Treescs.LG
Hierarchical structure is ubiquitous in data across many domains. There are many hierarchical clustering methods, frequently used by domain experts, which strive to discover this structure. However, most of these methods limit discoverable hierarchies to those with binary branching structure. This limitation, while com…
We introduce a new spatial data structure for high dimensional data called the \emph{approximate principal direction tree} (APD tree) that adapts to the intrinsic dimension of the data. Our algorithm ensures vector-quantization accuracy similar to that of computationally-expensive PCA trees with similar time-complexity…
TREX explains tree ensembles by identifying key training examples.
problem Identifying which training examples most influence tree ensemble predictions.
method TREX builds a surrogate model using a kernel that captures tree ensemble structure, approximating the original model.
result TREX provides accurate and effective explanations for tree ensembles.
SMART: A Flexible Approach to Regression using Spline-Based Multivariate Adaptive Regression Treesstat.ML
SMART combines decision trees and MARS for better regression modeling.
problem High variance in decision trees for continuous relationships, poor performance in MARS for discontinuities.
method SMART uses a decision tree to identify subsets with distinct continuous relationships, then applies MARS to fit these relationships independently.
result SMART improves regression performance over state-of-the-art methods in capturing discontinuities and continuous relationships.
Thin Tree Positionmath.CO
We introduce a method for creating a special type of tree, called a tree position, from a weighted graph. Leaves of the tree correspond to vertices of the original graph, and the tree edges contain information which can be used to partition these vertices. By repeatedly applying reducing operations to the tree position…
SNJ recovers latent tree models from similarity matrices.
problem Reconstructing latent tree models from observed data.
method Spectral Neighbor Joining (SNJ) method.
result SNJ is consistent and requires fewer samples for accurate tree recovery.
Optimal decision trees learned via dynamic programming and search.
problem Finding the best classification trees that are both accurate and compact.
method Dynamic programming and search techniques.
result Our algorithm learns optimal classification trees significantly faster and scales better than existing methods.
Contextual Decision Treesstat.ML
A new method for decision tree selection in recommendation systems.
problem Feature-based selection of a single tree from an ensemble for dynamic interpretation.
method A multi-armed contextual bandit recommendation framework that trains a system on top of Random Forests to identify the most relevant tree.
result The dynamic method outperforms an independent CART tree and is comparable to Random Forest in predictive performance.
Proposes a simple neural network model similar to gradient boosted decision trees.
problem Building a neural network equivalent to gradient boosted decision trees.
method Converts an ensemble of decision trees to a neural network, relaxes properties, and trains a simple neural network model.
result The proposed Hammock model achieves similar performance to gradient boosted decision trees.
Paper introduces methods to handle missing data in probabilistic regression trees.
problem Handling missing data in probabilistic regression trees.
method Three approaches: uniform probability, partial observation, and dimension-reduced smoothing.
result Preserves interpretability while extending applicability to incomplete datasets.