TP-AIS improves sampling efficiency over existing methods.
problem Efficient sampling from complex probability distributions.
method Iterative sampling using a tree pyramid structure.
result TP-AIS outperforms DM-PMC, M-PMC, and LAIS.
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48 results for “tree sampling”
A new algorithm, Regular Tree Search, tackles non-convex simulation optimization problems.
problem Non-convex objective functions in simulation optimization.
method Integrates adaptive sampling with recursive partitioning of the search space.
result Proves global convergence and reliably identifies the global optimum.
The paper introduces a new model to correct bias in treatment effect estimates due to sample selection.
problem Bias in treatment effect estimates due to sample selection.
method Type 2 Tobit Bayesian Additive Regression Trees (TOBART-2) with Dirichlet Process Mixture distribution and soft trees.
result Corrects bias in treatment effect estimates by accounting for nonlinearities and model uncertainty.
New algorithms identify optimal moves in game trees.
problem Identifying the best move in a game tree quickly.
method Best arm identification procedure applied to depth-one summaries.
result New algorithms outperform existing methods.
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.
BARK optimizes black-box functions using Bayesian Additive Regression Trees.
problem Bayesian optimization of complex, black-box functions with uncertainty quantification.
method BART Kernel using tree agreement for posterior over piecewise-constant functions, explored using MCMC.
result BARK obtains samples of Gaussian processes for function distributions, enabling acquisition functions for optimization.
A new algorithm improves sample complexity for thresholding in Monte Carlo Tree Search.
problem Determining if the root node value of a tree is at least a given threshold.
method Developed a δ-correct sequential sampling algorithm based on the Track-and-Stop strategy.
result Ratio-based modification of D-Tracking strategy reduces sample complexity and computational cost.
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.
SGA: A Robust Algorithm for Partial Recovery of Tree-Structured Graphical Models with Noisy Samplesstat.ML
Improved algorithm for partial recovery of tree-structured graphs with noisy data.
problem Learning Ising tree models with noisy observations.
method Symmetrized Geometric Averaging (SGA) algorithm with improved sample complexity.
result Significantly better sample complexity for partial tree recovery.
This work considers the problem of learning the structure of multivariate linear tree models, which include a variety of directed tree graphical models with continuous, discrete, and mixed latent variables such as linear-Gaussian models, hidden Markov models, Gaussian mixture models, and Markov evolutionary trees. The …
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.
For a density on , a {\it high-density cluster} is any connected component of , for some . The set of all high-density clusters forms a hierarchy called the {\it cluster tree} of . We present two procedures for estimating the cluster tree given samples from . The first…
This paper improves sample complexity for tree-structured Ising model learning with noisy data.
problem Learning tree-structured Ising models with noisy data.
method High-probability sample complexity guarantees for structure recovery and predictive learning.
result Sample complexity remains logarithmic in the number of vertices, but depends on noise level.
Method finds influential training samples for GBDT models efficiently.
problem Finding influential training samples for GBDT models.
method Leave-one-out retraining, extending to non-parametric GBDT ensembles, and approximations.
result Efficiently finds influential training samples for GBDT models.
CEDA analyzes large categorical datasets using tree geometry and binary codes.
problem Analyzing large categorical datasets with extreme- samples.
method CEDA uses tree geometry and binary codes to analyze categorical data.
result CEDA discovers patterns and evaluates their reliability in large categorical datasets.
This paper shows how to predict variable values given some others using a tree model, requiring fewer samples than previously thought.
problem Predicting variable values given some others using a tree model.
method Defining a new distance metric (ssTV) to measure prediction accuracy and deriving sample complexity bounds.
result Fewer samples are needed for accurate predictions compared to recovering the underlying tree.
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.
Inference Trees adaptively sample to balance exploration and exploitation.
problem Balancing exploration and exploitation in adaptive inference methods.
method Inference Trees use hierarchical partitions and online learning to adaptively sample.
result ITs identify high posterior mass regions and maintain uncertainty estimates.
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.
Method learns hierarchical representations of samples and features simultaneously.
problem Hierarchical structures in samples and features not considered by existing methods.
method Jointly learns hierarchical representations via Tree-Wasserstein Distance alternating between samples and features.
result Method improves performance in link prediction and node classification tasks.
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.
DTS improves inference-time alignment of diffusion models with less compute.
problem Inference-time alignment of diffusion models suffers from inaccurate value estimation and inefficient reuse of past computations.
method Diffusion Tree Sampling (DTS) uses a tree-based approach to propagate terminal rewards and iteratively refine value estimates.
result DTS produces asymptotically exact samples and matches the FID of best-performing baselines with up to 10x less compute.
A new gradient tree boosting framework reduces variance and accelerates performance.
problem High variance in stochastic gradient boosting.
method Combining gradient tree boosting with importance sampling and a regularizer.
result Achieves a linear convergence rate on logistic loss and 2.5x--18x acceleration on LogitBoost and LambdaMART.
Robustifies tree learning algorithms for corrupted data.
problem Learning latent tree structures with corrupted vector observations.
method Presented robustified algorithms using truncated inner product.
result Optimalities of robust CLRG and NJ verified by sample complexities and impossibility results.
Efficiently learns Gaussian tree models with near-optimal sample complexity.
problem Learning tree-structured Gaussian distributions efficiently.
method Conditional mutual information tester for Gaussian variables, near-optimal sample complexity.
result Near-optimal sample complexity for structure learning of Gaussian tree models.
The paper proposes a method to efficiently predict using labeled binary trees and analyzes the number of samples needed.
problem Efficiently predicting using compositional nonparametric models.
method A compositional nonparametric method expressed as a labeled binary tree, with a greedy algorithm for regression validation.
result The sufficient number of samples is , and the necessary number of samples is .
Algorithm infers sampling distribution from i.i.d. samples without supervision.
problem Learning probability distributions from unlabeled data.
method Unsupervised tree boosting using additive tree ensembles and new distributional operations.
result Algorithm outperforms deep learning in multivariate density estimation.
Optimal rates for learning hidden tree structures are determined.
problem Learning hidden tree structures from noisy data.
method Study of the (noisy) information threshold and the Chow-Liu algorithm.
result Optimal rates for structure recovery are inversely proportional to the information threshold squared.
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.
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.
A new nonparametric test measures dependence between variables using decision trees.
problem Measuring statistical dependence between two variables robustly and efficiently.
method An ensemble of decision trees discriminates between observed and permuted samples without generating the latter.
result The method effectively detects complex relationships from noisy data.
New method improves feature selection in tree-based models.
problem Previous feature selection methods in tree-based models lack sufficient regularization and sub-optimal performance.
method Developed a new gain penalization approach for tree-based models that allows for flexible feature-specific importance weights.
result The new method improves out-of-sample performance, especially with correlated features.
Deep imagination optimizes decision-making in large trees with limited resources.
problem Optimal planning in large decision trees with limited resources and time.
method Analytical solutions and numerical analysis of sampling capacity allocation.
result Optimal policy is to allocate few samples per level for deep exploration, favoring depth over breadth.
The ability to adequately model risks is crucial for insurance companies. The method of "Copula-based hierarchical risk aggregation" by Arbenz et al. offers a flexible way in doing so and has attracted much attention recently. We briefly introduce the aggregation tree model as well as the sampling algorithm proposed by…
New method builds robust trees from noisy data.
problem Building accurate classification trees from noisy labeled data.
method Combines SVM-like splitting rules and label noise detection.
result Effective in detecting and mitigating label noise.
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.
TQ separates sampling and integration for high-dimensional integrals.
problem High-dimensional integration challenges in science.
method Tree Quadrature (TQ) constructs a surrogate model using regression trees.
result TQ outperforms existing methods in up to 15 dimensions.
Active-LATHE boosts error exponent for learning homogeneous trees.
problem Learning homogeneous trees from i.i.d. data with active sampling.
method Design and analysis of Active Learning Algorithm for Trees with Homogeneous Edge (Active-LATHE).
result Active-LATHE boosts the error exponent by at least 40% for .
Efficiently learns tree-structured Ising models with minimal samples.
problem Learning tree-structured Ising models efficiently and accurately.
method Plug-in estimator for mutual information using the Chow-Liu algorithm.
result Proper learning of tree-structured Ising models with samples.
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse samplingcs.LG
Bayesian methods improve drug discovery experiment design.
problem Optimizing drug screening experiments in high-dimensional data.
method Bayesian inference and optimisation with upper confidence bound algorithms, Thompson sampling, and sparse tree search.
result Sparse tree search techniques outperform other methods in drug toxicity screening.
A new approach uses partial likelihood to improve tree-based density estimation and inference.
problem Inference on tree-based models suffers from overfitting and reduced efficiency due to data-independent partitioning.
method Proposes a partial likelihood approach to data-dependent partitioning of tree-based models.
result Significant gains in estimation accuracy and computational efficiency from adopting partial likelihood.
flexBART improves BART for categorical predictors by creating flexible tree partitions.
problem Limitation of BART in handling categorical predictors with one-hot encoding.
method flexBART re-implements BART with regression trees that can assign multiple levels to both branches of a decision tree node, and proposes a new decision rule prior for spatial data.
result flexBART often yields improved predictive performance and scales better to larger datasets than existing BART implementations.
We consider the inference of the structure of an undirected graphical model in an exact Bayesian framework. More specifically we aim at achieving the inference with close-form posteriors, avoiding any sampling step. This task would be intractable without any restriction on the considered graphs, so we limit our explora…
Estimates sample size for subgroup analysis in randomized experiments.
problem Determining sample size for accurate subgroup analysis.
method Turns inference problem into simultaneous inference, calculates sample size based on confidence level and margin of error.
result Allows inversion of sample size to feasible number of treatment arms or partition complexity.
Proposes new attribution methods for trees with regularization.
problem Feature attribution for trees trained with regularization.
method Prediction Decomposition Attribution (PreDecomp) and TreeInner.
result TreeInner shows state-of-the-art feature selection performance.
Web crawling, snowball sampling, and respondent-driven sampling (RDS) are three types of network sampling techniques used to contact individuals in hard-to-reach populations. This paper studies these procedures as a Markov process on the social network that is indexed by a tree. Each node in this tree corresponds to an…
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.
This paper proposes an improved active learning method using classification trees.
problem Reducing the size of training sets while maintaining high accuracy in supervised learning.
method A wrapper active learning method using a classification tree to sub-sample from low-entropy regions.
result The proposed method constructs accurate classification models even with severely restricted labeled data.