Proposes BehavDT model for context-aware user behavior prediction.
problem Building a context-aware predictive model based on diverse user behavioral activities.
method Introduces BehavDT, a behavioral decision tree that considers user behavior-oriented generalization.
result BehavDT model outperforms traditional machine learning approaches in predicting user diverse behaviors considering multi-dimensional contexts.
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.
Decision tree learning is a popular approach for classification and regression in machine learning and statistics, and Bayesian formulations---which introduce a prior distribution over decision trees, and formulate learning as posterior inference given data---have been shown to produce competitive performance. Unlike c…
We analyze the consistency of decision trees and random forests in regression.
problem Consistency of decision trees and random forests in regression.
method Elementary proofs following classical arguments for smoothing methods.
result Establish weak and almost sure convergence of honest trees and forest averages to the true regression function.
The paper challenges the use of decision trees for pointwise inference due to slow convergence rates.
problem The slow convergence rates of decision trees in uniform norm, especially with non-vanishing probability.
method Demonstrates the limitations of adaptive recursive partitioning and shows how random forests can improve performance.
result Decision trees can fail to achieve polynomial rates of convergence in uniform norm, even with pruning.
The paper studies how neural policies can be interpreted using decision trees.
problem Understanding how machine learning controllers make decisions in complex environments.
method The approach involves disentangled representation using decision trees to interpret neural policies.
result The paper shows that disentanglement of learned neural dynamics improves interpretability.
Glitches cause unreliable AI decisions with steep boundaries.
problem Glitches impair the reliability of AI models with steep decision boundaries.
method Formal definition of glitches, algorithmic search using MILP encoding.
result Glitches are widespread and indicate potential model inconsistencies.
Novel loss functions improve decision tree learning from noisy data.
problem Training decision trees with noisy labels.
method Introducing distribution losses and a new negative exponential loss.
result The negative exponential loss leads to efficient and robust decision tree learning.
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.
Within machine learning, the supervised learning field aims at modeling the input-output relationship of a system, from past observations of its behavior. Decision trees characterize the input-output relationship through a series of nested if−then−else questions, the testing nodes, leading to a set of predictions, th…
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.
A new method creates simpler, more interpretable decision trees from complex ensembles.
problem Complex tree ensembles reduce interpretability and control over machine learning models.
method Dynamic-programming based algorithm for finding a minimum-size decision tree.
result Optimal born-again trees are simpler and more interpretable than original ensembles.
Decision Machines embeds decision trees into vector spaces for improved optimization.
problem Overfitting and difficulty in finding optimal decision tree structure.
method Embedding Boolean tests into a binary vector space and representing tree structure as matrices.
result Optimized decision trees with enhanced predictive power.
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.
The paper proposes a method to assess and improve data quality using GBDT training dynamics.
problem Improving data quality in datasets with noisy labels and varying contributions.
method Metrics computed from training dynamics of Gradient Boosting Decision Trees (GBDTs).
result The method achieved the best results compared to other approaches.
This paper distills a complex travel mode choice model into simpler, interpretable models.
problem Lack of interpretability in complex machine learning models for travel behavior.
method Model distillation combined with market segmentation.
result Generated interpretable models that closely match the predictions of the original complex model.
dtControl uses decision trees to represent controllers efficiently and explainably.
problem Representing controllers concisely and explainably.
method dtControl uses decision tree learning algorithms to represent controllers. Novel techniques for determinizing controllers are introduced.
result Novel techniques for determinizing controllers during decision tree construction are extremely efficient, yielding small decision trees.
BFTS uses Bayesian Additive Regression Trees for improved personalized mobile health interventions.
problem Adapting to complex, non-linear user behaviors in personalized mobile health interventions.
method Bayesian Forest Thompson Sampling (BFTS) integrates Bayesian Additive Regression Trees (BART) into the exploration loop of contextual bandits.
result BFTS achieves state-of-the-art regret on tabular benchmarks and improves engagement rates by over 30% in a behavioral intervention study.
The study analyzes when Bayesian averaging over decision trees is reliable.
problem When do Bayesian model averaging weights over decision trees provide reliable information?
method Closed-form solution for Bayesian decision trees with Catalan-exponential priors.
result Established a complete non-asymptotic theory of rational commitment thresholds.
Trinary decision tree improves handling of missing data in machine learning.
problem Improving accuracy in decision tree algorithms when dealing with missing data.
method Introduces Trinary decision tree, which does not assume missing values contain information about the response.
result Trinary decision tree outperforms other algorithms in Missing Completely at Random settings, especially when data is only missing out-of-sample.
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.
The paper improves decision tree stability for health care applications.
problem Stability of decision trees in health care applications.
method Introducing a new distance metric to determine tree stability and proposing a novel training methodology.
result On average, a 4.6% decrease in predictive power yields a 38% increase in model stability.
Convex polytope trees expand decision trees with interpretable boundaries.
problem High accuracy often requires many nodes in decision trees, reducing interpretability.
method CPT uses logical disjunction of weighted linear decision-makers, geometrically a convex polytope.
result CPT achieves high accuracy with fewer nodes compared to existing methods.
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.
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.
Paper introduces algorithms for private decision tree learning.
problem Private decision tree learning in distributed settings.
method Proposes DP-TopDown, NoisyCounts, and LocalRNM.
result First utility guarantees for private decision tree learning.
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.
Max-Cut decision tree improves classification accuracy and reduces computation time.
problem Improving decision tree accuracy and efficiency for complex classification tasks.
method Alternative splitting metric (max cut) and PCA-based feature selection at each node.
result 49% improvement in accuracy with 94% reduction in CPU time on CIFAR-100 data.
New techniques optimize decision trees for interpretable machine learning.
problem Optimizing decision trees for interpretable machine learning.
method General framework for decision tree optimization addressing imbalanced data and continuous variables.
result Proves optimal decision trees for various objectives.
TAO outperforms other decision tree algorithms in accuracy.
problem Optimizing decision tree algorithms for better performance.
method Comparison of TAO with established algorithms on various datasets.
result TAO achieves higher accuracy in nearly all datasets.
Problem definition. In retailing, discrete choice models (DCMs) are commonly used to capture the choice behavior of customers when offered an assortment of products. When estimating DCMs using transaction data, flexible models (such as machine learning models or nonparametric models) are typically not interpretable and…
This work presents an approach to automatically induction for non-greedy decision trees constructed from neural network architecture. This construction can be used to transfer weights when growing or pruning a decision tree, allowing non-greedy decision tree algorithms to automatically learn and adapt to the ideal arch…
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.
Decision trees are an extremely popular machine learning technique. Unfortunately, overfitting in decision trees still remains an open issue that sometimes prevents achieving good performance. In this work, we present a novel approach for the construction of decision trees that avoids the overfitting by design, without…
Decision trees improve performance in various fields.
problem Improving performance in machine learning and data mining.
method Introduce tree-based models and training techniques.
result Highlight probabilistic characteristics and practical benefits.
Decision trees improve decision-making by optimizing predictions of unknown parameters.
problem Optimizing decisions based on predicted unknown parameters.
method SPO Trees (SPOTs) for training decision trees under the SPO loss function.
result SPOTs provide higher quality decisions and significantly lower model complexity compared to other machine learning approaches.
Decision trees can be biased towards minority class, contrary to belief.
problem Bias in decision trees towards minority class in imbalanced datasets.
method Critical evaluation of past literature, specific conditions analysis, tree-fitting adjustments, and post-hoc calibration methods.
result Decision trees can be biased towards minority class under specific conditions, not always towards majority.
We show how neural models can be used to realize piece-wise constant functions such as decision trees. The proposed architecture, which we call locally constant networks, builds on ReLU networks that are piece-wise linear and hence their associated gradients with respect to the inputs are locally constant. We formally …
We introduce a novel incremental decision tree learning algorithm, Hoeffding Anytime Tree, that is statistically more efficient than the current state-of-the-art, Hoeffding Tree. We demonstrate that an implementation of Hoeffding Anytime Tree---"Extremely Fast Decision Tree", a minor modification to the MOA implementat…
Decision trees are ubiquitous in machine learning for their ease of use and interpretability. Yet, these models are not typically employed in reinforcement learning as they cannot be updated online via stochastic gradient descent. We overcome this limitation by allowing for a gradient update over the entire tree that i…
Rectified decision trees improve machine learning interpretability and effectiveness.
problem Combining interpretability and effectiveness in machine learning models.
method Knowledge distillation and modified decision tree splitting criteria.
result Soft labels improve model performance and reduce model size.
Algorithm learns decision trees from noisy data.
problem Learning stochastic decision trees from corrupted samples.
method Quasipolynomial-time algorithm for adversarial noise.
result Returns a hypothesis with error within 2η+ε of optimal. Study explores loss design for decision trees to improve robustness against noisy labels.
problem Improving decision tree robustness to noisy labels.
method Investigated loss correction and symmetric losses, found ineffective.
result Other loss design directions need exploration for robust decision trees.
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.
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.
Decision trees improve intraday trading strategies for NIFTY50 stocks.
problem Creating optimal trading rules for individual stocks.
method Using decision trees to generate unique trading rules for each stock based on technical indicators.
result Decision tree strategies outperform simple buy-and-hold for many stocks.
This paper solves the open problem of computing Bayes optimal prediction for decision trees using a Markov chain Monte Carlo method.
problem Computing the Bayes optimal prediction for decision trees is infeasible due to an infeasible summation over all division patterns of a feature space.
method Solved the open problem using a Markov chain Monte Carlo method with adaptively tuned step size.
result Computed the Bayes optimal prediction for decision trees using a Markov chain Monte Carlo method.
The paper uses model-based trees to create interpretable surrogate models for complex machine learning models.
problem Interpreting complex machine learning models.
method Using model-based trees to partition feature space and create interpretable models.
result Model-based trees generate optimal surrogate models that balance interpretability and performance.