In this paper, we present a novel massively parallel algorithm for accelerating the decision tree building procedure on GPUs (Graphics Processing Units), which is a crucial step in Gradient Boosted Decision Tree (GBDT) and random forests training. Previous GPU based tree building algorithms are based on parallel multi-…
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48 results for “histogram trees”
We present sparse tree-based and list-based density estimation methods for binary/categorical data. Our density estimation models are higher dimensional analogies to variable bin width histograms. In each leaf of the tree (or list), the density is constant, similar to the flat density within the bin of a histogram. His…
Gradient boosting adapted for vector inputs.
problem Applying gradient boosting to multi-class classification problems.
method Extended gradient boosting framework to vector inputs using histogram-based decision trees.
result Efficient algorithm for vector-valued objectives.
WildWood improves Random Forest predictions using bootstrap out-of-bag samples.
problem Improving Random Forest predictions for supervised learning.
method Uses bootstrap out-of-bag samples to compute improved predictions by aggregating all possible subtrees with exponential weights.
result WildWood produces faster and more competitive predictions compared to other ensemble methods.
LiteMORT reduces memory usage for GBDT models by 30% with improved accuracy.
problem Memory inefficiency in GBDT models, especially with adaptive compact distributions.
method Share memory, implicit merging, adaptive histogram resizing.
result Significant reduction in memory usage with improved accuracy.
Proposes GBBHE for efficient large-scale regression.
problem Large-scale regression problems.
method Gradient Boosting with binary histogram partition and ensemble learning.
result Improves computational efficiency and performance on large datasets.
A new method detects concept drift in streaming data using k-means space partitioning.
problem Detecting distribution changes in streaming data.
method Equal intensity k-means space partitioning (EI-kMeans) and heuristic sensitivity improvement.
result EI-kMeans improves drift detection accuracy and sensitivity.
New method for neural networks to predict histogram data.
problem Lack of principled approach for histogram regression.
method Pinball loss applied to cumulative histogram.
result Accuracy similar to EMD with less computational cost.
Fast, fully-automated histograms for large data sets.
problem Efficiently constructing histograms for large-scale data.
method G-Enum histograms using Minimum Description Length (MDL) principle for model selection and a greedy search heuristic.
result Construct histograms in linearithmic time, significantly faster than previous methods.
For a dataset of label-count pairs, an anonymized histogram is the multiset of counts. Anonymized histograms appear in various potentially sensitive contexts such as password-frequency lists, degree distribution in social networks, and estimation of symmetric properties of discrete distributions. Motivated by these app…
A new histogram layer improves texture analysis performance.
problem Extracting features for texture analysis from local spatial regions.
method Directly computes local spatial distribution of features during backpropagation.
result Improves performance on three material/texture datasets.
Package for Fast ABC-Booststat.ML
Package implements ABC-Boost for multi-class classification.
problem Improving accuracy in multi-class classification.
method Combines histogram-based binning and explicit gain formula for efficient tree splitting.
result ABC-Boost significantly improves multi-class classification accuracy.
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.
The paper uses geometric methods to classify medical data histograms.
problem Classifying medical data histograms for disease diagnosis.
method Information geometry of beta distributions for comparing and classifying histograms.
result Geometric tools, particularly negatively curved Fisher information, enable unique mean calculation and K-means classification.
The histogram method is a powerful non-parametric approach for estimating the probability density function of a continuous variable. But the construction of a histogram, compared to the parametric approaches, demands a large number of observations to capture the underlying density function. Thus it is not suitable for …
Due to the success of the bag-of-word modeling paradigm, clustering histograms has become an important ingredient of modern information processing. Clustering histograms can be performed using the celebrated -means centroid-based algorithm. From the viewpoint of applications, it is usually required to deal with symm…
Histogram binning method proven with guarantees without splitting data.
problem Proving theoretical guarantees for histogram binning without sample splitting.
method Using Markov property of order statistics to prove calibration guarantees for original method.
result Proves histogram binning has strong calibration guarantees without sample splitting.
This paper examines the Histogram Loss for regression, revealing its effectiveness without needing complex tuning.
problem Improving regression models by learning the entire distribution.
method Investigates Histogram Loss, a method that minimizes cross-entropy between a target distribution and a histogram prediction.
result The performance gain in regression models using Histogram Loss comes from optimization improvements, not extra modeling.
Improved bounds on neural network regions using activation histograms.
problem Bounding the number of affine regions in ReLU networks.
method Analysis of algebraic topology problem, extension of framework to subnetwork composition.
result Slightly tighter bounds and insights into parameter initialization.
ABHT boosts regression by filtering regions with different smoothness.
problem Improving regression performance through local adaptivity.
method Gradient boosting with adaptive histogram transform.
result ABHT converges faster than PEHT in Hölder continuous spaces.
Bayesian histograms achieve optimal distribution estimation with minimal memory usage.
problem Efficiently estimating distributions with minimal memory footprint.
method Bayesian histograms for distribution estimation under Wasserstein distance.
result Bayesian histograms require fewer bins to achieve minimax optimality, reducing memory usage by a polynomial factor.
Introduces CHL, a new loss function for continuous similarity learning.
problem Binary similarity learning limitations.
method CHL is a novel loss function that generalizes histogram loss to continuous similarities.
result CHL solves a wider range of tasks including similarity learning, representation learning, and data visualization.
Efficient logistic regression for aggregated data reduces computation time.
problem Inference for logistic regression models is computationally expensive for large datasets.
method Adapted symbolic data analysis to summarise predictor variables into histograms and use composite likelihoods.
result The method achieves comparable classification rates to full data analysis but at lower computational cost.
A novel text-independent speaker identification (SI) method is proposed. This method uses the Mel-frequency Cepstral coefficients (MFCCs) and the dynamic information among adjacent frames as feature sets to capture speaker's characteristics. In order to utilize dynamic information, we design super-MFCCs features by cas…
The paper improves prediction intervals for non-parametric regression using histograms.
problem Computing accurate prediction intervals for non-parametric regression models.
method Uses conditional histograms to estimate conditional distributions and compute shortest prediction intervals.
result The method provides prediction intervals with provable marginal coverage and asymptotic conditional coverage.
Sparse coding (Sc) has been studied very well as a powerful data representation method. It attempts to represent the feature vector of a data sample by reconstructing it as the sparse linear combination of some basic elements, and a norm distance function is usually used as the loss function for the reconstructio…
Segmentation of skin lesions based on fuzzy classification of pixels and histogram thresholdingcs.CV
This paper proposes an innovative method for segmentation of skin lesions in dermoscopy images developed by the authors, based on fuzzy classification of pixels and histogram thresholding.
Paper finds how many neurons are needed to approximate histogram distributions.
problem How many neurons are needed to approximate a target probability distribution?
method Examined for uniform input distribution and histogram target distributions, using efficient neural net construction.
result Obtained a new upper bound on the number of required neurons, strictly better than previous bounds.
Paper introduces a new histogram estimator for nonparametric density estimation that improves performance.
problem Smoothness-based nonparametric density estimators are not optimal for all types of data.
method Incorporates a multi-view latent variable model into histogram-style estimators.
result A new histogram estimator converges faster to multi-view models in error.
EHBOS enhances HBOS by capturing feature interactions, improving anomaly detection.
problem Limited ability of HBOS to detect anomalies in datasets with feature interactions.
method Incorporates two-dimensional histograms to capture feature pair dependencies.
result EHBOS outperforms HBOS on datasets with critical feature interactions, achieving notable improvements in ROC AUC.
Covariance and histogram image descriptors provide an effective way to capture information about images. Both excel when used in combination with special purpose distance metrics. For covariance descriptors these metrics measure the distance along the non-Euclidean Riemannian manifold of symmetric positive definite mat…
Histogram transform ensembles improve density estimation accuracy.
problem Improving density estimation accuracy for various distributions.
method Histogram transform ensembles (HTE) with theoretical analysis and experimental validation.
result HTE outperforms single histogram transforms and offers almost optimal convergence rates in Hölder space .
A new algorithm HTE for large-scale regression improves accuracy compared to single estimators.
problem Improving accuracy in large-scale regression problems.
method Histogram transform ensembles (HTE) with random transformations and kernel histogram transforms (KHT).
result Ensemble HTE outperforms single estimators in accuracy for various Hölder spaces.
In this era of big data, databases are growing rapidly in terms of the number of records. Fast automatic detection of anomalous records in these massive databases is a challenging task. Traditional distance based anomaly detectors are not applicable in these massive datasets. Recently, a simple but extremely fast anoma…
This paper presents a novel method to compute the exact Kantorovich-Wasserstein distance between a pair of -dimensional histograms having bins each. We prove that this problem is equivalent to an uncapacitated minimum cost flow problem on a -partite graph with nodes and arcs,…
A JAX toolbox solves optimal transport problems for point clouds and histograms.
problem Optimal transport problems between point clouds and histograms.
method Automatic and custom reverse mode differentiation, vectorization, just-in-time compilation, and accelerators support.
result Solves a wide range of optimal transport problems including regularized OT, barycenters, Gromov-Wasserstein, and low-rank solvers.
Heuristic algorithm for 1D and 2D unfoldingphysics.data-an
A very simple heuristic approach to the unfolding problem will be described. An iterative algorithm starts with an empty histogram and every iteration aims to add one entry to this histogram. The entry to be added is selected according to a criteria which includes a test and a regularization. After a relatively s…
Databases in domains such as healthcare are routinely released to the public in aggregated form. Unfortunately, naive modeling with aggregated data may significantly diminish the accuracy of inferences at the individual level. This paper addresses the scenario where features are provided at the individual level, but th…
A faster Wasserstein k-means algorithm for histogram data reduces computation and maintains clustering quality.
problem Efficiently clustering histogram data with reduced computation time.
method Sparse simplex projection to reduce data samples, centroids, and ground cost matrix, dynamically removing lower-valued samples.
result Significant reduction in computational complexity without compromising clustering quality.
Paper proposes a method to detect glare in document images.
problem Glare obscures text in document images, hindering recognition.
method Divides document into blocks, collects luminance and histogram features, uses CNN to detect glare.
result High recall and f-score in detecting glare.
Most users of online services have unique behavioral or usage patterns. These behavioral patterns can be exploited to identify and track users by using only the observed patterns in the behavior. We study the task of identifying users from statistics of their behavioral patterns. Specifically, we focus on the setting i…
Unified framework for PDF estimation using MDL-based binning and tensor factorization.
problem Challenges in estimating PDFs for non-uniform, multimodal data.
method MDL-based binning with quantile cuts, tensor factorization (CPD).
result Effective PDF estimation on synthetic and real data.
A new probabilistic framework for optimal transport using collective graphical models.
problem Measuring similarity between probability distributions and histograms.
method Probabilistic Optimal Transport based on Collective Graphical Models.
result OT with entropic regularization is equivalent to maximizing a posterior probability of a CGM.
GBHT uses gradient boosting for density estimation with theoretical guarantees.
problem Density estimation for unsupervised learning.
method Gradient Boosting Histogram Transform (GBHT) with Negative Log Likelihood loss.
result GBHT achieves faster convergence rates and better performance than base learners in density estimation.
New methods for distributed CP improve reliability in healthcare.
problem Post-hoc calibration of models in safety-critical domains.
method Quantile-based and histogram-based distributed conformal prediction via message passing.
result Improved reliability and coverage guarantees in decentralized settings.
Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learningstat.ML
This paper introduces a new nonlinear dictionary learning method for histograms in the probability simplex. The method leverages optimal transport theory, in the sense that our aim is to reconstruct histograms using so-called displacement interpolations (a.k.a. Wasserstein barycenters) between dictionary atoms; such at…
In an orientable surface with boundary, free homotopy classes of curves on surfaces are in one to one correspondence with cyclic reduced words in a set of standard generators of the fundamental group. The combinatorial length of a class is the number of letters of the corresponding word. The self-intersection of a free…
Accurate calibration of probabilistic predictive models learned is critical for many practical prediction and decision-making tasks. There are two main categories of methods for building calibrated classifiers. One approach is to develop methods for learning probabilistic models that are well-calibrated, ab initio. The…