ECV method optimizes ensemble parameters for randomized ensembles.
problem Efficient tuning of ensemble parameters in randomized ensembles.
method ECV (Extrapolated Cross-Validation) method for tuning ensemble and subsample sizes.
result ECV yields δ-optimal ensembles for squared prediction risk.
New hyperparameter ensembles boost neural network performance and uncertainty.
problem Improving neural network robustness and uncertainty quantification.
method Designing ensembles over both weights and hyperparameters, stratified across random initializations.
result Hyper-deep and hyper-batch ensembles outperform deep and batch ensembles on various architectures.
A new ensemble method using random projections for kNN classification.
problem Improving kNN classification accuracy through ensemble methods.
method Random projection of bootstrap samples into lower dimensions, using extended neighbourhood rule for base learners.
result Enhanced classification accuracy compared to traditional kNN and other ensembles.
Estimates algorithmic variance for bagging and random forests using bootstrap.
problem Deciding when an ensemble is large enough for accurate predictions.
method Bootstrap method to estimate algorithmic variance under a first-order model.
result Consistent approximation of the centered law of prediction error as ensemble size increases.
Develops RKHS framework for analyzing tree ensembles.
problem Analyzing the theoretical properties of tree ensembles.
method Reproducing Kernel Hilbert Spaces (RKHS) for tree ensembles.
result Characterizes Random Forest predictor as minimizer of a penalized empirical risk functional in RKHS.
In this work a novel method to quantify spectral ergodicity for random matrices is presented. The new methodology combines approaches rooted in the metrics of Thirumalai-Mountain (TM) and Kullbach-Leibler (KL) divergence. The method is applied to a general study of deep and recurrent neural networks via the analysis of…
Extends spectral number variance convergence to random matrix ensembles for twisted Laplacians.
problem Spectral number variance convergence for twisted Laplacians and Dirac operators.
method Extends Rudnick's approach to Gaussian ensembles for twisted Laplacians and Dirac operators.
result Convergence to Gaussian ensembles for twisted Laplacians and Dirac operators.
Randomized gradient-based ensemble improves prediction accuracy.
problem Improving prediction accuracy in machine learning.
method Randomization and gradient-based aggregation of weakly-correlated estimators.
result The method outperforms existing techniques in terms of increased accuracy.
HD algorithm simulates dynamics on random matrix ensembles without generating full matrices.
problem Simulating dynamics on dense random matrix ensembles with high space and time complexity.
method Householder reflectors for adaptive and recursive construction, deferring decisions.
result Significant reductions in runtime and memory footprint for practical T ≪ n T \ll n T ≪ n . The paper verifies the robustness of classifier ensembles against randomized attacks.
problem Ensuring classifier ensembles are robust against arbitrary randomized attacks.
method Formal verification procedure using SMT and MILP encodings to assess robustness.
result Proves the NP-hardness of the robustness-checking problem and provides upper bounds on attack sets.
Random matrix ensembles yield uniform distributions on manifolds.
problem Understanding distributions of vectors in random matrix ensembles.
method Analyzing eigenvalues, singular values, and Autonne-Takagi vectors of various random matrix ensembles.
result Uniform distributions on specific manifolds for different types of random matrix ensembles.
Random Hyperboxes is a simple yet effective ensemble classifier.
problem Improving classification accuracy using ensemble methods.
method Random subsets of sample and feature spaces are used to train individual hyperbox-based classifiers, which are then combined into an ensemble.
result The proposed classifier outperforms other fuzzy min-max neural networks and ensemble methods on 20 datasets.
Develops an empirical likelihood framework for random forests and ensembles.
problem Quantifying the statistical uncertainty of random forests and ensembles.
method Empirical likelihood framework exploiting the incomplete U U U -statistic structure of ensemble predictions. result Modified empirical likelihood statistic achieves accurate coverage and practical reliability.
Ensembles dynamic models using random feature approximations.
problem Online scalable Bayesian learning with dynamic models and ensembling.
method Random feature approximations and dynamic models using random walks.
result Better performance with alternative basis expansions like Hilbert space Gaussian processes.
New method defends machine learning models from black-box attacks.
problem Protecting machine learning models from black-box transfer attacks.
method Randomized ensemble technique with provable security guarantee.
result Empirical evidence of security for random binary classifiers.
An ensemble of randomized NNs improves time series forecasting accuracy.
problem Forecasting time series with multiple seasonality and nonstationarity.
method Randomized neural networks with pattern-based time series representation and diversity control strategies.
result Outperforms statistical and machine learning models in forecasting accuracy.
Tree ensembles such as random forests and boosted trees are accurate but difficult to understand, debug and deploy. In this work, we provide the inTrees (interpretable trees) framework that extracts, measures, prunes and selects rules from a tree ensemble, and calculates frequent variable interactions. An rule-based le…
BoostForest combines multiple BoostTree models for improved accuracy.
problem Improving ensemble learning performance.
method BoostTree uses gradient boosting and random cut-points. BoostForest bootstraps training data and randomly samples parameters.
result BoostForest outperforms classical ensemble methods on 35 datasets.
Ensembles of random-feature models can't outperform a single large model.
problem Finding the optimal balance between model size and ensemble size.
method Deterministic equivalent risk estimates and scaling laws analysis.
result Ensembles of random-feature models achieve near-optimal performance only under specific conditions.
RAFs Ensemble improves neural network uncertainty quantification.
problem Inaccurate predictions in out-of-distribution data.
method Proposes RAFs Ensemble, using random activation functions in neural networks.
result RAFs Ensemble outperforms state-of-the-art methods in uncertainty quantification.
Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans. We raise questions about the resulting loss of diversity in the decision making process. We study the potential benefits of using random classifier ensembles instead of a single classifier in the context…
The paper develops a bootstrap method to measure algorithmic convergence in regression.
problem Ensuring a randomized ensemble performs nearly as well as an ideal infinite ensemble.
method Bootstrap method for regression setting, complementing classification setting.
result Theoretical guarantees for the bootstrap method can be established under weaker assumptions.
BERET improves binary expansion test for multivariate independence.
problem Testing independence of random vectors in arbitrary dimensions.
method Ensemble approach using sum of squared symmetry statistics and distance correlation.
result Improves power while preserving interpretability.
K-nearest neighbor (kNN) search has wide applications in many areas, including data mining, machine learning, statistics and many applied domains. Inspired by the success of ensemble methods and the flexibility of tree-based methodology, we propose random projection forests (rpForests), for kNN search. rpForests finds …
SPQR improves Q-ensemble diversity in reinforcement learning.
problem Overestimation bias in Q-learning for complex tasks.
method Introduces SPQR for Q-ensemble independence regularization.
result SPQR outperforms baseline algorithms in online and offline RL benchmarks.
Paper studies ensemble probabilistic regression trees for smooth approximations.
problem Smooth approximations of regression functions.
method Ensemble versions of probabilistic regression trees.
result Ensemble probabilistic regression trees are consistent and perform well.
Deep ensembles improve model accuracy and robustness, but their theoretical underpinnings are not fully understood.
problem Understanding why deep ensembles work well in practice despite theoretical limitations.
method Investigating the loss landscape of neural networks and exploring the diversity of functions in function space.
result Random initializations explore diverse modes in function space, while ensembles along an optimization trajectory cluster within a single mode.
New ensemble SVM model reduces prediction error without choosing best kernel.
problem Reducing prediction error in regression problems.
method Bagged-weighted support vector regression model with random machines.
result Regression Random Machines achieve lower generalization error.
SWEEN improves certified robustness via weighted ensembling of smoothed classifiers.
problem Finding optimal base classifiers for randomized smoothing.
method Smoothed WEighted ENsembling (SWEEN) scheme.
result SWEEN achieves optimal certified robustness under mild assumptions.
Generalizes JT gravity to time-reversal symmetric theories with fermions and supersymmetry.
problem Matching JT gravity and random matrix ensembles with different symmetries.
method Extends techniques from hermitian matrices to other ensembles, including super Riemann surfaces.
result Volume of moduli spaces in JT gravity and supergravity is related to random matrix volumes.
Sharp threshold found for Frechet mean of inhomogeneous graphs.
problem Finding the Frechet mean of inhomogeneous Erdos-Renyi random graphs.
method Thresholding the expected adjacency matrix of the ensemble.
result The Frechet mean graph of inhomogeneous Erdos-Renyi random graphs exhibits a sharp threshold.
Recent studies have revealed the vulnerability of deep neural networks: A small adversarial perturbation that is imperceptible to human can easily make a well-trained deep neural network misclassify. This makes it unsafe to apply neural networks in security-critical applications. In this paper, we propose a new defense…
Random forests reduce bias and variance, especially in low SNR settings.
problem Reducing bias and variance in machine learning models, particularly in low SNR scenarios.
method Empirical study of random forests and bagging ensembles, focusing on the importance of m t r y mtry m t r y tuning. result Random forests reduce both bias and variance, outperforming bagging ensembles in high SNR settings.
Overparameterized ensembles don't offer generalization benefits over single large models.
problem Theoretical limitations of ensembles in overparameterized settings.
method Using ensembles of random feature (RF) regressors, the paper clarifies how modern ensembles differ from underparameterized counterparts.
result Infinite ensembles of overparameterized RF regressors become pointwise equivalent to single infinite-width RF regressors, and finite width ensembles converge to single models with the same parameter budget.
RST improves environmental time series classification accuracy using randomized B-spline trees.
problem Improving accuracy in classifying complex environmental time series.
method Randomized Spline Trees (RST) integrates randomized functional representations into ensemble learning.
result RST variants outperform standard Random Forests and Gradient Boosting on most environmental time series datasets.
Optimal survival trees ensemble reduces tree count and improves predictive performance.
problem Improving predictive performance in survival analysis.
method Grows a forest of optimal survival trees by ranking and selecting the best trees based on out-of-bag error.
result Reduces the number of trees in the ensemble while improving predictive performance.
RaSE ensemble framework improves sparse classification accuracy.
problem Sparse classification challenges in high-dimensional data.
method Random Subspace Ensemble (RaSE) framework with subspace selection via RIC.
result RaSE achieves low misclassification rates and accurate feature ranking.
A new method for handling imbalanced big data using ensembles and smart data.
problem Imbalanced data distribution in big data scenarios.
method Smart Data driven Decision Trees Ensemble (SD_DeTE) methodology.
result SD_DeTE outperforms Random Forest in handling imbalanced binary classification problems in big data.
Improved regret bound for linear ensemble sampling.
problem Closing the gap between theory and practice in linear ensemble sampling.
method General regret analysis framework for linear bandit algorithms, revealing a relationship with LinPHE.
result Achieves a frequentist regret bound of i l d e O ( d 3 / 2 T ) ilde{O}(d^{3/2}\sqrt{T}) i l d e O ( d 3/2 T ) for linear ensemble sampling. New matrix ensembles better match deep neural network spectral densities.
problem Theoretical spectral density models for deep networks do not match empirical observations.
method Introduced new matrix ensemble classes to better fit observed spectral densities.
result Theoretical models for deep networks are significantly flawed.
The paper develops a stationary-distribution theory for Random Forest ensemble size selection.
problem Determining the optimal number of trees in Random Forests.
method Modeling the ensemble size as a birth-death Markov chain and deriving its stationary distribution.
result The stationary ensemble size B ∗ B_* B ∗ scales as O ( ε − 2 ) O(\varepsilon^{-2}) O ( ε − 2 ) as ε ↓ 0 \varepsilon\downarrow 0 ε ↓ 0 . Unified framework for cost-sensitive ensemble learning.
problem Different misclassification costs in data.
method A unifying framework for cost-sensitive ensemble methods.
result Unified understanding and natural extensions of existing methods.
When randomized ensembles such as bagging or random forests are used for binary classification, the prediction error of the ensemble tends to decrease and stabilize as the number of classifiers increases. However, the precise relationship between prediction error and ensemble size is unknown in practice. In the standar…
We develop ensemble Convolutional Neural Networks (CNNs) to classify the transportation mode of trip data collected as part of a large-scale smartphone travel survey in Montreal, Canada. Our proposed ensemble library is composed of a series of CNN models with different hyper-parameter values and CNN architectures. In o…
In ensemble methods, the outputs of a collection of diverse classifiers are combined in the expectation that the global prediction be more accurate than the individual ones. Heterogeneous ensembles consist of predictors of different types, which are likely to have different biases. If these biases are complementary, th…
Improved neural network regression uncertainty estimation.
problem Neural networks lack classical uncertainty due to finite data.
method Bootstrapped Deep Ensembles, incorporating parametric bootstrap.
result Significantly improved uncertainty estimation compared to standard Deep Ensembles.
This study shows how social insects and machine learning methods share a common mathematical framework.
problem Understanding how decentralized systems achieve optimal decision-making.
method Developed a rigorous mathematical framework to show isomorphism between ant colonies and ensemble machine learning.
result Demonstrated that ant colony decision-making and random forest learning implement identical variance reduction strategies through decorrelation of identical units.
Study finds Calabi-Yau models' operator spectra match random matrix theory.
problem Understanding spectra of Calabi-Yau sigma models.
method Numerical methods for Ricci-flat metrics, averaging over complex structure moduli space.
result Spectrum matches Gaussian orthogonal ensemble of random matrix theory.