A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
E combines sparse MoEs and ensembles to improve model efficiency and performance.
ControlBurn selects few features from tree ensembles for better model interpretability.
An ensemble of neural networks is known to be more robust and accurate than an individual network, however usually with linearly-increased cost in both training and testing. In this work, we propose a two-stage method to learn Sparse Structured Ensembles (SSEs) for neural networks. In the first stage, we run SG-MCMC wi…
Proposes a new method for ensembling neural subnetworks.
REALITrees uses a Rashomon ensemble approach for active learning in sparse decision trees.
FIRE extracts interpretable rules from tree ensembles.
Latent-EnSF improves data assimilation for high-dimensional systems with sparse observations.
RaSE ensemble framework improves sparse classification accuracy.
This work improves distribution recovery from sparse data using Random Forest implicit regularization.
EASIER-net uses sparse networks to improve prediction accuracy for high-dimensional data.
Paper analyzes ensemble Kalman updates for effective dimension and localization.
New research shows deep models learn sparse features, limiting transfer learning; ensembling improves performance.
Compact Gaussian model approximates deep ensemble predictions.
The paper learns compact implicit surface maps from streaming data using an ensemble of sparse Gaussian processes.
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…
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…
This paper focuses on a comparative evaluation of the most common and modern methods for text classification, including the recent deep learning strategies and ensemble methods. The study is motivated by a challenging real data problem, characterized by high-dimensional and extremely sparse data, deriving from incoming…
Integrates differentiable decision trees into neural networks for faster training and inference.
Develops an empirical likelihood framework for random forests and ensembles.
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) …
Combines deep generative models with ensemble methods for subsurface property estimation.
Multistage Defer Trees improve model accuracy while maintaining interpretability.
This paper focuses on scalability and robustness of spectral clustering for extremely large-scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra-scalable spectral clustering (U-SPEC) and ultra-scalable ensemble clustering (U-SENC). In U-SPEC, a hybrid representative selection strategy…
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…
Combining LETKF and RC improves chaotic system prediction from noisy, sparse data.
The estimation of advantage is crucial for a number of reinforcement learning algorithms, as it directly influences the choices of future paths. In this work, we propose a family of estimates based on the order statistics over the path ensemble, which allows one to flexibly drive the learning process, towards or agains…
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.
Ensemble learning use multiple algorithms to obtain better predictive performance than any single one of its constituent algorithms could. With growing popularity of deep learning, researchers have started to ensemble them for various purposes. Few if any, however, has used the deep learning approach as a means to ense…
We consider filtering in high-dimensional non-Gaussian state-space models with intractable transition kernels, nonlinear and possibly chaotic dynamics, and sparse observations in space and time. We propose a novel filtering methodology that harnesses transportation of measures, convex optimization, and ideas from proba…
New method improves model explainability and accuracy with low computational cost.
Multi-objective optimization for hyperparameters and features.
Sharp threshold found for Frechet mean of inhomogeneous graphs.
TreeHFD algorithm explains tree ensemble models through hierarchical orthogonality.
Analyzes geodesic lengths in sparse networks, deriving a distribution.
In this note we compare two recently proposed semidefinite relaxations for the sparse linear regression problem by Pilanci, Wainwright and El Ghaoui (Sparse learning via boolean relaxations, 2015) and Dong, Chen and Linderoth (Relaxation vs. Regularization A conic optimization perspective of statistical variable select…
When choosing a suitable technique for regression and classification with multivariate predictor variables, one is often faced with a tradeoff between interpretability and high predictive accuracy. To give a classical example, classification and regression trees are easy to understand and interpret. Tree ensembles like…
DSAEE FS selects features for imbalanced data.
Paper distills ensemble ENSO forecasts into simpler models for better diagnostics.
A hybrid method combines data assimilation and machine learning to predict chaotic dynamics from sparse noisy data.
Independent component analysis (ICA) is a cornerstone of modern data analysis. Its goal is to recover a latent random vector S with independent components from samples of X=AS where A is an unknown mixing matrix. Critically, all existing methods for ICA rely on and exploit strongly the assumption that S is not Gaussian…
GER learns particle dynamics from unpaired snapshots using physics-informed GANs.
Given the superposition of a low-rank matrix plus the product of a known fat compression matrix times a sparse matrix, the goal of this paper is to establish deterministic conditions under which exact recovery of the low-rank and sparse components becomes possible. This fundamental identifiability issue arises with tra…
The question why deep learning algorithms generalize so well has attracted increasing research interest. However, most of the well-established approaches, such as hypothesis capacity, stability or sparseness, have not provided complete explanations (Zhang et al., 2016; Kawaguchi et al., 2017). In this work, we focus on…
Radar-based road user classification is an important yet still challenging task towards autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse data representation which is tough to recover by subsequent signal processing. In this article, classifier ensembles origin…
The paper explores how multiway data from PDEs can be accurately tracked using EnKF with specific covariance and precision estimators.
The exploration mechanism used by a Deep Reinforcement Learning (RL) agent plays a key role in determining its sample efficiency. Thus, improving over random exploration is crucial to solve long-horizon tasks with sparse rewards. We propose to leverage an ensemble of partial solutions as teachers that guide the agent's…
LD-EnSF speeds up data assimilation with sparse observations.