NTI models text structure without syntactic trees, improving NLP tasks.
problem Lack of robust syntactic parsing in recursive models.
method Bottom-up tree construction with attention mechanism.
result NTI outperforms state-of-the-art models on NLP tasks.
Tree Index evaluates cluster quality by creating decision trees from data.
problem Evaluating the quality of cluster results from various techniques.
method Tree Index creates a decision tree from clustered data, combining entropy and depth of leaves.
result Tree Index discriminates between sensible and non-sensible clusters on brain dataset.
Jointly optimizes tree index and deep model for better recommendation accuracy.
problem Improving recommendation accuracy in large-scale recommender systems.
method Develops a joint optimization framework for tree index and user preference model.
result Significantly improves recommendation accuracy on real-world datasets.
Alternative to convolutions using decision trees for neural networks.
problem Replacing complex convolutions with simpler decision-based layers.
method Binary decisions as indices to conditional distributions, trained using backpropagation.
result Performance similar to conventional neural networks, with runtime improvements.
Automatically tunes hyperparameters for faster approximate nearest neighbor search.
problem Tuning hyperparameters for efficient approximate nearest neighbor search is slow and impractical.
method Proposes an algorithm using randomized space-partitioning trees to automatically tune hyperparameters.
result Significantly faster than existing approaches and competitive in query time.
The study proves how groups can be split with limited complexity.
problem Understanding the complexity of group splittings.
method Analyzing trees with finite stabilizers and their quotient structures.
result Deformation spaces of trees have maximal complexity.
Study shows critical exponents for tree-acting groups.
problem Understanding critical exponents of discrete groups on trees.
method Explicit construction of edge-indexed graphs.
result Proven existence of groups with specific critical exponents.
Spaces of circle embeddings in curved surfaces indexed by trees.
problem Classifying spaces of braided automorphism groups of trees.
method Indexed connected components with finite rooted trees, constructed strong deformation retract.
result Connected components are classifying spaces of braided automorphism groups.
GBST model improves credit risk quantification using survival analysis.
problem Quantifying credit risk in heterogeneous consumer finance data.
method Gradient boosting survival tree (GBST) model integrating survival analysis and gradient boosting.
result GBST model outperforms existing survival models in credit risk quantification.
New system studies trapped light paths in Euclidean space.
problem Trapping of light paths in Euclidean space with negative refractive index.
method Introduces wind-tree tiling billiards system to study trajectories of rays in Euclidean space with rectangular obstacles.
result Almost every configuration of the system traps trajectories with initial vertical direction in an infinite strip.
A new Tsallis entropy criterion unifies decision tree split criteria.
problem Improving decision tree performance using a unified split criterion.
method Proposes a Tsallis Entropy Criterion (TEC) algorithm to unify Shannon entropy, Gain Ratio, and Gini index.
result TEC algorithm achieves statistically significant improvement over classical algorithms.
Proposes a tree-based method to efficiently predict user interests in large recommender systems.
problem Efficiently predicting user-item preferences in large recommender systems with high calculation costs.
method Predicts user interests from coarse to fine using a tree structure, which can incorporate deep neural networks.
result Significantly outperforms traditional methods in both training and prediction.
The study evaluates various ML models for stock market prediction.
problem Predicting the Nifty 50 Index using machine learning models.
method 8 supervised machine learning models (AdaBoost, kNN, LR, ANN, RF, SGD, SVM, DT) applied to historical Nifty 50 Index data.
result Support Vector Machine performed best, but Stochastic Gradient Descent improved performance with larger datasets.
A new decision tree variant improves linear model performance.
problem Improving decision tree performance on non-linear data.
method Extremely random tree with non-linear data transformation and linear observer.
result Outperforms linear models on benchmark dataset.
Study improves stock index prediction accuracy using TPE-GRNN models.
problem Enhancing prediction of stock index prices in volatile markets.
method Gated recurrent neural networks (LSTM, GRU) combined with TPE Bayesian optimization.
result TPE-LSTM method shows lowest MAPE (best accuracy) for NIFTY 50 index prediction.
Confidence intervals improve decision tree accuracy in streaming data.
problem Improving decision tree accuracy in streaming data with confidence intervals.
method Deriving accurate confidence intervals for decision tree splitting criteria and extending to selective sampling.
result Confidence intervals enhance decision tree accuracy and reduce labeling costs.
Optimal diagram found for complete graphs with linear trees.
problem Finding optimal diagrams for complete graphs.
method Using a linear tree structure to minimize crossing numbers.
result Optimal diagrams without free hamiltonian cycles for odd n≥7. Novel pricing method for equity-indexed annuities under uncertain volatility and stochastic interest rate.
problem Pricing equity-indexed annuities with early surrender risk under uncertain market conditions.
method Advanced financial modeling techniques, including uncertain volatility framework and Hull-White model for interest rate dynamics. Numerical algorithm using tree-based framework with local volatility optimization.
result High effectiveness of the proposed numerical algorithm compared to machine learning-based methods.
DNDT combines neural networks and decision trees for tabular data.
problem Tabular data processing with interpretability and efficiency.
method Deep Neural Decision Trees (DNDT) using neural networks to model decision trees.
result DNDT achieves both interpretability and efficiency in tabular data processing.
Develops a binary tree model for option pricing with skew dynamics.
problem Option pricing in incomplete markets with skew dynamics.
method Binary tree model with skew Brownian motion dynamics.
result Model preserves skewness under both discrete and continuous time limits.
This work introduces a novel nonparametric density index defined on graphs, the Sum-over-Forests (SoF) density index. It is based on a clear and intuitive idea: high-density regions in a graph are characterized by the fact that they contain a large amount of low-cost trees with high outdegrees while low-density regions…
Unified representation for tree ensembles indexed by nodes
problem Unifying geometric object for tree ensembles indexed by nodes
method KPP indexes feature map by nodes, weighted by path metric
result Unified non-diagonal Gram for prediction, additive attribution, robust radius, and risk bounds
Improved linear upper bound for ribbonlength of knots.
problem Estimating the ribbonlength of knots and links.
method Using four-page open book decompositions and spanning trees of checkerboard graphs, constructing a four-page presentation with at most 2c(K) arcs.
result Proved that ribbonlength is bounded above by the four-page index, leading to the linear bound Rib(K) ≤ 2c(K).
Improved metric indexes support malware analysis by handling insertions and queries in real-time.
problem Handling insertions and queries in real-time for malware analysis.
method Modified and improved three algorithms to support incremental insertion and querying with arbitrary metrics.
result Improved Vantage-Point tree of Minimum-Variance performs best for this scenario.
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.
Converts GBDT trees to neural networks for online updates.
problem Performance loss in converting GBDT trees to neural networks.
method Converts existing GBDT implementations to neural network architectures, allowing online updates of decision splits.
result Learning bounds for neural network architecture with updated splits.
The paper uses tensor decompositions to improve neural network models for tree data.
problem Encoding structural knowledge from tree-structured data efficiently.
method Introduces new aggregation functions using Canonical and Tensor-Train decompositions.
result Proposed models outperform traditional methods on tree classification tasks.
Let T be an R-tree, equipped with a very small action of the rank n free group Fn, and let H≤Fn be finitely generated. We consider the case where the action Fn↷T is indecomposable--this is a strong mixing property introduced by Guirardel. In this case, we show that the acti…
This work automates decision tree construction from neural networks.
problem Creating optimal decision tree architectures from neural networks.
method Automatic induction of non-greedy decision trees using weights transfer from neural networks.
result Improved model performance over fixed hyperparameters.
Accelerates nonparametric estimation to near-linear time.
problem Quadratic time complexity in local polynomial regression.
method Novel use of binary indexed trees for multi-dimensional data.
result Near-linear time complexity in computation.
TreeDOX predicts chaotic systems without hyperparameter tuning.
problem Forecasting chaotic systems requires hyperparameter tuning, limiting adoption.
method TreeDOX uses time delay overembedding and Extra-Trees Regressors.
result TreeDOX achieves state-of-the-art performance on chaotic systems.
Automated medical protocol uses neural networks and decision trees.
problem Improving healthcare delivery through automated decision-making.
method Hybrid model combining neural networks and decision trees.
result Effective early decisions for patient care.
Tree-SMU enables strong compositional generalization in neural networks.
problem Zero-shot generalization to novel compositions of concepts.
method Tree Stack Memory Units (Tree-SMU) with Stack Memory Units (SMU).
result Tree-SMU achieves strong empirical results on mathematical reasoning benchmarks.
Let Γ be a finite index subgroup of the mapping class group MCG(Σ) of a closed orientable surface Σ, possibly with punctures. We give a precise condition (in terms of the Nielsen-Thurston decomposition) when an element g∈Γ has positive stable commutator length. In addition, we show that in these situations th…
New tree and forest methods use oblique splits for better risk bounds.
problem Improving risk bounds for regression algorithms.
method Randomized decision trees and forests with oblique splits.
result Oblique splits lead to better risk bounds for multi-index models.
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.
The investigations of financial markets from a complex network perspective have unveiled many phenomenological properties, in which the majority of these studies map the financial markets into one complex network. In this work, we investigate 30 world stock market indices through their visibility graphs by adopting the…
Neural models can realize decision trees with parameter sharing and improved performance.
problem Training and optimizing oblique decision trees.
method Locally constant networks based on ReLU gradients, parameter sharing, and neural tools.
result Locally constant networks can implicitly model oblique decision trees with fewer neurons.
Proposes neural decision trees combining neural networks and decision trees.
problem Combining neural networks and decision trees for improved modeling power.
method Integrates multilayer perceptrons into decision tree structure with weight sharing.
result Demonstrates improved modeling power and flexibility over standard decision trees and MLPs.
Minimal grid diagrams found for 13-crossing prime knots with 13 arc index.
problem Finding minimal grid diagrams for prime knots with specific crossing and arc indices.
method Used Knotscape to generate spanning trees and obtain minimal arc presentations in grid diagrams.
result 9,988 prime knots with 13 crossings and 13 arc index were identified.
ANTs integrate neural networks and decision trees for better performance.
problem Combining neural networks and decision trees for improved performance.
method Adaptive Neural Trees (ANTs) integrate representation learning into decision tree structures, using backpropagation for adaptive growth.
result ANTs achieve competitive performance on classification and regression datasets, with benefits in lightweight inference, feature separation, and adaptable architecture.
A new k-means algorithm using cover trees accelerates clustering.
problem Efficiently clustering large datasets with k-means.
method Combining cover trees with upper and lower bounds.
result Significantly reduces distance computations and improves clustering performance.
Neural regression trees convert regression to classification more effectively.
problem Suboptimal approaches for regression via classification.
method Joint optimization framework for learning optimal discretization thresholds and feature selection in a neural regression tree.
result Empirically validated as state-of-the-art on challenging regression tasks.
Paper analyzes soft tree ensembles using NTK, finding only leaf count matters.
problem Understanding impact of various tree architectures in ensemble learning.
method Formulated and analyzed Neural Tangent Kernel (NTK) for soft tree ensembles.
result Only the number of leaves at each depth is relevant for tree architecture in ensemble learning.
Neural model parses non-projective dependency trees efficiently.
problem Parsing non-projective dependency trees.
method Probabilistic parsing model using neural representations and Kirchhoff's Matrix-Tree Theorem.
result State-of-the-art parsing performance on nine datasets.
Constructs a cell decomposition for the Fulton MacPherson operad FM_2.
problem Cellular decomposition of the Fulton MacPherson operad FM_2.
method Indexed by trees with two colors and cacti operad cells, compatible with operad composition.
result Computes generating functions for cell counts, algebraic.
This work uses decision trees to encode relevant features and their interactions into neural networks, improving model performance.
problem Overfitting in neural networks with many irrelevant variables.
method Defines a mapping to encode decision tree extracted relationships into a neural network.
result The approach outperforms fully connected neural networks and tree-based methods.
A grammar-driven tree-to-tree model improves program translation accuracy.
problem Improving program translation accuracy between programming languages.
method A grammar-driven tree-to-tree model that exploits known grammar rules of the target language.
result The grammar-based model outperforms state-of-the-art models in program translation accuracy.