This work proposes a method to learn Sparse Structured Ensembles using SG-MCMC and weight pruning.
problem Training and testing of neural network ensembles are computationally expensive.
method Two-stage method: SG-MCMC with group sparse priors followed by weight pruning.
result Significant reduction in memory and computation cost for high prediction accuracy.
Compact Gaussian model approximates deep ensemble predictions.
problem Efficiently approximating deep ensemble models for image prediction.
method Sparse-structured multivariate Gaussian with Cholesky parameterization trained to match pre-trained ensemble outputs.
result Compact representation captures uncertainty and structured correlations explicitly.
E3 combines sparse MoEs and ensembles to improve model efficiency and performance.
problem Improving model efficiency and performance in machine learning.
method Combining sparse mixture of experts and ensembles, presenting Efficient Ensemble of Experts (E3). result E3 achieves better accuracy, log-likelihood, few-shot learning, robustness, and uncertainty estimates than baselines. FIRE extracts interpretable rules from tree ensembles.
problem Building sparse and interpretable rule sets from tree ensembles.
method Optimization-based framework with fusion regularization and sparsity-inducing penalties.
result FIRE outperforms state-of-the-art rule ensemble algorithms.
Real world systems typically feature a variety of different dependency types and topologies that complicate model selection for probabilistic graphical models. We introduce the ensemble-of-forests model, a generalization of the ensemble-of-trees model. Our model enables structure learning of Markov random fields (MRF) …
EASIER-net uses sparse networks to improve prediction accuracy for high-dimensional data.
problem Limited use of neural networks in high-dimensional data with small samples.
method Ensemble by Averaging Sparse-Input Hierarchical networks (EASIER-net) with small modifications to neural network architecture and training procedure.
result EASIER-net achieves higher prediction accuracy than off-the-shelf methods on average.
This work improves distribution recovery from sparse data using Random Forest implicit regularization.
problem Distribution recovery from limited statistics.
method Closed-form estimator for scaled beta distributions, using composite quantile and moment matching.
result Improved classification accuracy through closed-form distribution recovery and implicit regularization.
ControlBurn selects few features from tree ensembles for better model interpretability.
problem Improving model interpretability in machine learning models.
method Sparse tree ensembles with lasso optimization.
result ControlBurn selects feature-sparse subsets for better model interpretability.
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-statistic structure of ensemble predictions. result Modified empirical likelihood statistic achieves accurate coverage and practical reliability.
Proposes a new method for ensembling neural subnetworks.
problem Computational expense and limited flexibility of traditional deep ensembles.
method Sequential Bayesian neural subnetwork ensembling.
result Outperforms traditional ensembles in various metrics.
We study a new class of codes for lossy compression with the squared-error distortion criterion, designed using the statistical framework of high-dimensional linear regression. Codewords are linear combinations of subsets of columns of a design matrix. Called a Sparse Superposition or Sparse Regression codebook, this s…
REALITrees uses a Rashomon ensemble approach for active learning in sparse decision trees.
problem Active learning reduces labeling costs by selecting informative samples, but current methods often sacrifice model diversity and direct characterization of the hypothesis space.
method REALITrees constructs a committee of all near-optimal sparse decision tree models using a Rashomon Set and a Gibbs posterior to weight them by empirical risk.
result REALITrees outperforms randomized ensembles, especially in noisy environments, by leveraging expanded model multiplicity.
The study compares shallow and deep learning methods for text classification.
problem High-dimensional, sparse data from customer calls.
method Comparative evaluation of shallow, deep, and ensemble methods.
result Ensemble methods improve classification accuracy.
Unions of subspaces provide a powerful generalization to linear subspace models for collections of high-dimensional data. To learn a union of subspaces from a collection of data, sets of signals in the collection that belong to the same subspace must be identified in order to obtain accurate estimates of the subspace s…
Combines deep generative models with ensemble methods for subsurface property estimation.
problem Estimating spatially distributed subsurface properties from sparse measurements.
method Wasserstein Generative Adversarial Network (WGAN-GP) and Ensemble Smoother with Multiple Data Assimilation (ES-MDA).
result The proposed method outperforms variational inversion methods, especially for channelized and fractured fields.
Latent-EnSF improves data assimilation for high-dimensional systems with sparse observations.
problem Challenges in high-dimensional, nonlinear Bayesian filtering with sparse observations.
method A novel data assimilation method using latent representations and a coupled VAE for efficient state encoding and reconstruction.
result Latent-EnSF outperforms traditional methods in accuracy, convergence, and efficiency for complex systems.
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.
We analyze the necessary number of samples for sparse vector recovery in a noisy linear prediction setup. This model includes problems such as linear regression and classification. We focus on structured graph models. In particular, we prove that sufficient number of samples for the weighted graph model proposed by Heg…
New algorithm recovers sparse, generic mixing matrix from covariance of mixed data.
problem Recovering independent components from mixed Gaussian data.
method Sparse and generic mixing matrix, efficient algorithm.
result Efficiently recovers mixing matrix from covariance of mixed data.
Paper analyzes ensemble Kalman updates for effective dimension and localization.
problem Why small ensemble sizes work well in inverse problems and data assimilation.
method Non-asymptotic analysis of ensemble Kalman updates, focusing on effective dimension and localization.
result Rigorously explains why a small ensemble size is sufficient when prior covariance has moderate effective dimension.
The paper proposes biased advantage estimates for reinforcement learning, improving efficiency and performance.
problem Estimating advantages in reinforcement learning algorithms.
method A family of estimates based on order statistics over the path ensemble.
result Biased estimates can significantly benefit reinforcement learning, especially in environments with sparse rewards or critical actions.
Metrics assess uncertainty structure and distribution for regression models.
problem Quantifying uncertainty in high-dimensional and nonlinear regression tasks.
method Two bounded comparison metrics for uncertainty structure and distribution.
result DNNs and DNOs provide encouraging uncertainty metric values in high dimensions.
Learning structured outputs with general structures is computationally challenging, except for tree-structured models. Thus we propose an efficient boosting-based algorithm AdaBoost.MRF for this task. The idea is based on the realization that a graph is a superimposition of trees. Different from most existing work, our…
SpaceGAN enhances geospatial data using deep learning.
problem Challenges in modeling geospatial data with deep learning.
method SpaceGAN, a generative model that learns spatial structures through conditioning on neighbours.
result SpaceGAN produces synthetic samples faithful to real spatial patterns, improving data augmentation and model generalization.
The paper explores how multiway data from PDEs can be accurately tracked using EnKF with specific covariance and precision estimators.
problem Tracking sparse and multiway structures in dynamical processes governed by PDEs.
method Examined several multiway covariance and precision matrix estimators in the context of physics-driven forecasting and EnKF.
result Multiway data from Poisson and convection-diffusion PDEs can be accurately tracked using EnKF with appropriate estimators.
New research shows deep models learn sparse features, limiting transfer learning; ensembling improves performance.
problem Sparse feature learning in deep models limits transfer learning performance.
method Developed a theoretical framework and proposed an ensembling strategy to aggregate multiple models.
result Ensembling yields a 9% improvement in transfer accuracy without extra pretraining cost.
Radar-based classification improves accuracy of autonomous driving by identifying new classes.
problem Challenges in classifying sparse radar data for autonomous driving.
method Ensemble of classifiers using one-vs-one and one-vs-all strategies, with feature selection.
result Improved classification performance and identification of novel classes.
The paper learns compact implicit surface maps from streaming data using an ensemble of sparse Gaussian processes.
problem Creating compact and accurate implicit surface maps from streaming range data.
method An ensemble of sparse Gaussian process experts, incrementally adjusted, trades-off between model complexity and prediction error.
result The approach learns compact and accurate implicit surface models comparable to or better than exact GP regression with subsampled data.
Although many successful ensemble clustering approaches have been developed in recent years, there are still two limitations to most of the existing approaches. First, they mostly overlook the issue of uncertain links, which may mislead the overall consensus process. Second, they generally lack the ability to incorpora…
Paper distills ensemble ENSO forecasts into simpler models for better diagnostics.
problem Interpreting complex ensemble ENSO forecasts.
method Aggregates eSPA ensemble members that correctly predict ENSO phase.
result Compact distilled models maintain forecast performance and enable diagnostics.
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.
Dynamic models learn from sparse, interacting sub-systems.
problem Learning robust models for systems with local views and spatial locations.
method Abstracting the system as a collection of sparsely interacting sub-systems, each with a learned topology informed by spatial structure.
result Models are more robust to the number of available views and generalize better to novel tasks.
Two novel algorithms improve scalability and robustness of spectral clustering for large datasets.
problem Scalability and robustness of spectral clustering for large-scale datasets.
method Ultra-scalable spectral clustering (U-SPEC) and ultra-scalable ensemble clustering (U-SENC) algorithms.
result Robust and efficient clustering of ten-million-level datasets on a PC.
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.
Improved method for estimating precision matrices without knowing variable order.
problem Estimating precision matrices without knowing the order of variables.
method Combining multiple permutations and thresholding for sparse structure.
result Consistent property established under weak conditions, superior performance in simulations and real data.
Combining LETKF and RC improves chaotic system prediction from noisy, sparse data.
problem Improving chaotic system prediction from imperfect observations and models.
method Combining LETKF and RC to predict spatio-temporal chaotic systems from noisy and sparsely distributed observations.
result The proposed method using LETKF and RC outperforms LETKF in predicting chaotic systems from noisy and sparse observations.
forgeNet uses a tree-based ensemble to learn feature graphs for deep learning in omics data.
problem Small sample size vs. large feature space in omics data.
method forgeNet integrates a forest feature graph extractor with a GEDFN architecture.
result ForgeNet achieves high classification accuracy on synthetic and real datasets.
We propose a novel approach for nonlinear regression using a two-layer neural network (NN) model structure with sparsity-favoring hierarchical priors on the network weights. We present an expectation propagation (EP) approach for approximate integration over the posterior distribution of the weights, the hierarchical s…
Efficiently estimates neural connectivity using ensemble stimulation.
problem Estimating functional connectivity in large, behaving neural populations.
method Noisy group testing combined with convex optimization and Bayesian inference.
result Connectivity can be inferred with logarithmic growth in tests, even for large networks.
A new method for Bayesian neural networks using probabilistic backpropagation.
problem Approximating posterior distributions in Bayesian neural networks.
method Variational Expectation Propagation (VEP) with probabilistic backpropagation.
result Efficient algorithm for approximate integration over posterior distributions.
Paper proposes ensemble distillation for well-calibrated structured prediction.
problem Well-calibrated predictions are hard to achieve in structured prediction.
method Ensemble distillation framework for structured prediction.
result Ensemble distillation produces well-calibrated models with similar performance and calibration benefits to ensembles.
Proposes ldcTree for improved conversion rate prediction in recommendation systems.
problem Challenges in predicting conversion rate due to sparseness and weak correlation features.
method Introduces multi-Level Deep Cascade Trees (ldcTree) with deep cascade structures and cross-entropy feature representation.
result Demonstrates improved prediction accuracy through experimental validation.
Novel filtering method for high-dimensional chaotic systems.
problem Filtering in high-dimensional non-Gaussian models with chaotic dynamics and sparse observations.
method Transportation of measures, convex optimization, probabilistic graphical models, nonlinear couplings.
result State-of-the-art tracking performance on chaotic systems like Lorenz-96 model.
Deep learning ensemble improves Alzheimer's disease classification accuracy.
problem Improving diagnostic accuracy for Alzheimer's disease.
method Proposes a deep ensemble learning framework integrating multisource data and expert wisdom.
result 4% improvement in classification accuracy compared to existing methods.
This paper develops a new theory for ensemble learning beyond variance reduction.
problem Ensemble learning's effectiveness for stable estimators is not fully explained by variance reduction.
method Develops a general weighting theory for ensemble learning, formalizing ensembles as linear operators and introducing geometric and spectral constraints.
result Structured weights can outperform uniform averaging by reshaping approximation geometry and redistributing spectral complexity.
Decision forests, including Random Forests and Gradient Boosting Trees, have recently demonstrated state-of-the-art performance in a variety of machine learning settings. Decision forests are typically ensembles of axis-aligned decision trees; that is, trees that split only along feature dimensions. In contrast, many r…
Single model estimates ensemble uncertainty efficiently.
problem Efficient uncertainty quantification in deep learning models.
method Contextual similarity distillation, approximating ensemble variance with a single model.
result Single model estimates predictive variance with a single forward pass.
Study infers evolutionary interactions from protein sequences using regularization methods.
problem Inferring evolutionary interactions from protein sequences.
method Regularization methods, including L2 for fields and group L1 for couplings, with parameter tuning. result Effective regularization parameters for sparse couplings improve accuracy.