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 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.
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.
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…
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.
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.
Paper proposes a new method for density estimation using tree tensor-network states.
problem Density estimation for complex graphical models with loops.
method Determines tree topology with Chow-Liu algorithm and uses sketching techniques to define tensor-network components.
result Sample complexity guarantees and empirical validation provided.
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.
This paper finds efficient algorithms for approximating Markov networks with k-tree topologies.
problem Efficiently approximating Markov networks with complex topologies.
method Developed O(n^{k+1})-time algorithms for finding maximum spanning k-trees (MSkT) that retain certain subgraphs.
result Optimal approximation of Markov networks with k-tree topology is achieved in polynomial time.
Generative Adversarial Network creates realistic halo merger trees.
problem Comparing galaxy formation theories with observations using halo merger trees.
method Treated halo merger tree construction as a matrix generation problem, using Generative Adversarial Network.
result Generated halo merger trees are of high quality and realistic.
New algorithms learn simple staged trees from data, improving model fit.
problem Complex conditional independences in categorical data vectors.
method Structural learning algorithms for simple staged trees, coalescing the underlying tree.
result Data-learned simple staged trees often outperform Bayesian networks in model fit.
Deep neural networks have been proven powerful at processing perceptual data, such as images and audio. However for tabular data, tree-based models are more popular. A nice property of tree-based models is their natural interpretability. In this work, we present Deep Neural Decision Trees (DNDT) -- tree models realised…
An efficient algorithm for aligning diffusion trees to networks with information asymmetry.
problem Aligning diffusion trees to networks with information asymmetry.
method Tree correlation tests for extracting alignment information.
result Explicit lower bounds on the probability of correct matches for each vertex on the diffusion tree.
Integrates differentiable decision trees into neural networks for faster training and inference.
problem Combining differentiability and conditional computation in tree ensembles for neural networks.
method Sparse activation function and specialized forward/backward propagation algorithms for efficient training and inference.
result 10x speed-ups and 20x reduction in parameters compared to existing methods, while maintaining performance.
L1-orthogonal regularization improves decision tree explainability of deep neural networks.
problem Lack of explainability in deep neural networks.
method L1-orthogonal regularization during training of decision trees.
result Decision trees closely approximate trained deep neural networks with improved accuracy and fidelity.
Develops a fair tree boosting method for tabular data.
problem Lack of fair classifiers based on decision trees in tabular data.
method Adversarial gradient tree boosting that minimizes adversarial neural network's ability to predict sensitive attributes.
result Achieves higher accuracy while maintaining fairness.
A new test statistic counts tree co-occurrences to detect edge correlation between networks.
problem Detecting edge correlation between networks using latent vertex correspondence.
method The test statistic is based on counting co-occurrences of signed trees for a family of non-isomorphic trees.
result The test runs in n2+o(1) time and succeeds with high probability for large n. Tree tensor networks balance model complexity and empirical risk for high-dimensional function approximation.
problem Selecting optimal tree structure and ranks for high-dimensional function approximation.
method Proposes a complexity-based model selection method for tree tensor networks in empirical risk minimization.
result Demonstrates near-minimax adaptive performance across various smoothness classes.
Transform ANNs into interpretable decision trees.
problem Lack of interpretability in ANNs.
method Developed two MDT algorithms: EC-DT and Extended C-Net.
result Extended C-Net generates the most compact and effective trees.
New method estimates root-directed tree from extreme data.
problem Discovering causality in river networks from extreme flow data.
method Qualitative max-linear Bayesian network approach to estimate bivariate scores and root-directed spanning tree.
result The new estimator is consistent under max-linear Bayesian network model with noise.
TSN improves sparse signal recovery with less complexity.
problem Sparse regression problem of recovering sparse signals from measurements.
method Tree search algorithm driven by deep neural network with pruning.
result TSN outperforms conventional methods in various sensing matrices.
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.
Combines neural networks and decision trees for supervised learning.
problem Challenges in optimizing hierarchical parameters and constructing tree structures.
method Probabilistic approach with modified gradient ascent and adaptive tree construction.
result Novel classification and regression technique that combines strengths of neural networks and decision trees.
Extracts decision trees from CNNs to explain concept importance.
problem Understanding how CNNs make decisions about human-understandable concepts.
method Inferring labeled concept data from CNN hidden layer activations and creating a shallow decision tree.
result Extracted decision trees accurately represent CNN classifications.
Study infers tree topology from customer data using contrastive learning.
problem Inferring local network topology from customer data.
method Contrastive learning approach for binary event encoding from continuous time series.
result Preliminary results show potential for valuable encoder learning.
Proposes a knowledge-guided semantic computing network for better neural network performance.
problem Difficulties in designing, interpreting, and predicting neural network performance.
method Knowledge-guided semantic tree and data-driven neural network modules.
result Improved performance with fewer training samples and lower complexity.
Knowledge distillation simplifies deep models into interpretable decision trees.
problem Interpretability of deep neural networks is challenging and important for practical deployment.
method Knowledge distillation applied to transform deep models into decision trees.
result The student model achieves better accuracy than vanilla decision trees.
In this paper, we investigate the statistical features of the weighted international-trade network. By finding the maximum weight spanning trees for this network we make the extraction of the truly relevant connections forming the network's backbone. We discuss the role of large-sized countries (strongest economies) in…
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.
Study examines insurance sector linkages and systemic risk using dynamic spanning trees.
problem Interlinkages and systemic risk in the European insurance sector.
method Analysis of linkage dynamics and systemic risk using correlation networks, copulas, and minimum spanning trees.
result Minimum spanning trees describe linkage dynamics in the European insurance sector.
Tree-Transformer improves grammar correction in code and natural language.
problem Grammar correction in tree-structured data.
method Tree-Transformer neural network architecture for tree-structured data.
result Significant improvement in grammar correction accuracy.
RNNs guide clause selection in proof trees, improving inference accuracy.
problem Improving inference accuracy in proof trees.
method Recurrent Neural Networks (RNNs) encode literals to select clauses.
result RNNs outperform gradient boosted trees in clause selection.
USNRT uses tree-structured learning to improve uncertainty quantification of variance networks.
problem Improving uncertainty quantification of variance networks.
method Tree-structured local neural network model that partitions feature space into regions for training region-specific neural networks to predict mean and variance.
result USNRT shows superior performance in estimating uncertainty with variances on UCI datasets compared to recent methods.
Tree regularization makes deep models interpretable by approximating them with simple decision trees.
problem Lack of interpretability in deep neural networks.
method Tree regularization to train deep models to resemble compact, axis-aligned decision trees.
result Tree regularized models are easier for humans to interpret without sacrificing accuracy.
Proposes regional tree regularization for interpretable deep models.
problem Lack of interpretability in deep neural networks.
method Encourages deep models to be well-approximated by separate decision trees for predefined regions of the input space.
result Regional tree regularization delivers more accurate predictions than training separate decision trees for each region, while producing simpler explanations.
Optimized decision trees for reinforcement learning, improving sample complexity and interpretability.
problem Updating decision trees online in reinforcement learning.
method Gradient update over differentiable decision trees, including theoretical justification and empirical validation.
result Our approach outperforms neural networks in sample complexity and achieves higher rewards online.
Survey of TreeLSTM models for transductions.
problem Handling tree structures in neural networks.
method Analysis of recent TreeLSTM models and their biases.
result No single model is adequate for all transduction problems.
Graph-to-Tree Neural Networks improve structured input-output translation in tasks like semantic parsing and math word problems.
problem Improving performance on tasks like semantic parsing and math word problem solving.
method Graph-to-Tree Neural Networks, consisting of a graph encoder and a hierarchical tree decoder.
result Graph2Tree model outperforms or matches state-of-the-art models on neural semantic parsing and math word problem tasks.
A new framework for efficient Bayesian network inference.
problem High-dimensional Bayesian networks are hard to infer due to computational scaling.
method Directed convex subgraphs and minimal d-decomposition tree for decomposition, enabling parallel computation.
result The method reduces computational cost and enables parallel computation.
A behavior of extreme networks under deformations of their boundary sets is investigated. It is shown that analyticity of a deformation of boundary set guarantees preservation of the networks types for minimal spanning trees, minimal fillings and so-called stable shortest trees in the Euclidean space.
Estimates high-dimensional distributions using tree tensor networks.
problem Estimating high-dimensional probability distributions from i.i.d. samples.
method Tree-based tensor formats, empirical risk minimization, L2 contrast, orthogonal bases.
result Effective approximation of classical probabilistic models like Gaussian and graphical models.
PIN models feature interactions using a neural network that mimics decision trees.
problem Modeling feature interactions in tabular data for predictive modeling.
method Tree-like Pairwise Interaction Network (PIN) architecture that captures pairwise feature interactions through a shared feed-forward neural network.
result PIN outperforms traditional and modern neural networks benchmarks in predictive accuracy.
NSOTree combines neural networks and trees for better survival analysis interpretability.
problem Survival analysis event prediction with interpretability.
method Proposes NSOTree integrating neural networks and trees for better interpretability.
result NSOTree improves both performance and interpretability in survival analysis.
Bayesian networks are simplified for categorical variables using staged trees and asymmetry-labeled DAGs.
problem Representing non-symmetric conditional independences in Bayesian networks.
method Formalized relationship between Bayesian networks and staged trees, introduced asymmetry-labeled DAGs, and developed an algorithm to learn staged trees.
result A novel algorithm for learning staged trees that captures non-symmetric independences.
ARTree uses deep learning to infer tree topologies efficiently.
problem Efficient phylogenetic inference from tree topologies.
method Deep autoregressive model based on graph neural networks (GNNs).
result ARTree provides a flexible family of distributions over tree topologies.
The wavelet transform has seen success when incorporated into neural network architectures, such as in wavelet scattering networks. More recently, it has been shown that the dual-tree complex wavelet transform can provide better representations than the standard transform. With this in mind, we extend our previous meth…
PGS uses neural networks to improve policies online without search trees.
problem Limited scalability of Monte Carlo Tree Search (MCTS) for high branching factor games.
method Adapts a neural network simulation policy via policy gradient updates, avoiding search trees.
result PGS achieves comparable performance to MCTS and defeats strong Hex agents.