SES finds multiple feature subsets with similar predictive power.
problem Finding a single optimal feature subset for predictive accuracy.
method SES algorithm identifies multiple statistically equivalent feature subsets.
result Multiple feature subsets can achieve close to maximal predictive accuracy.
Let M be a possibly noncompact manifold. We prove, generically in the C^k-topology (k=2,...,\infty), that semi-Riemannian metrics of a given index on M do not possess any degenerate geodesics satisfying suitable boundary conditions. This extends a result of Biliotti, Javaloyes and Piccione for geodesics with fixed endp…
Let M be a possibly non compact smooth manifold. We study genericity in the C^k-topology (3<=k<=+infty) of nondegeneracy properties of semi-Riemannian geodesic flows on M. Namely, we prove a new version of the Bumpy Metric Theorem for a such M and also genericity of metrics that do not possess any degenerate geodesics …
R package innsight interprets deep neural networks predictions.
problem Interpreting predictions of deep neural networks.
method Unified and user-friendly framework implementing feature attribution methods for neural networks, independent of deep learning library.
result Offers a variety of visualization tools for tabular, signal, image data or a combination.
Graphical models provide powerful tools to uncover complicated patterns in multivariate data and are commonly used in Bayesian statistics and machine learning. In this paper, we introduce the R package BDgraph which performs Bayesian structure learning for general undirected graphical models (decomposable and non-decom…
R package psvmSDR simplifies SDR computation for machine learning.
problem Sufficient dimension reduction in machine learning.
method Principal machine (PM) generalized from PSVM.
result Efficient computation of SDR estimators in various scenarios.
Optimizes package types for e-commerce to reduce damage and costs.
problem Sub-optimal package types lead to damaged shipments and high costs.
method Multi-stage approach that balances shipment and damage costs using a scalable algorithm.
result Significant cost savings of tens of millions of dollars achieved.
The public package registry npm is one of the biggest software registry. With its 216 911 software packages, it forms a big network of software dependencies. In this paper we evaluate various methods for finding similar packages in the npm network, using only the structure of the graph. Namely, we want to find a way of…
RSSL package aids semi-supervised learning in R.
problem Handling limited labeled data in machine learning.
method Semi-supervised learning methods implemented in R.
result Replicates results from semi-supervised learning literature.
FSinR is a comprehensive R package for feature selection.
problem Feature selection in machine learning.
method Filter and wrapper methods, search algorithms.
result Provides a variety of feature selection methods and search algorithms.
grangersearch tests causal relationships in time series data.
problem Testing causal relationships between multiple time series.
method Exhaustive pairwise search, automatic lag order optimization, tidyverse integration.
result Automated Granger causality testing simplifies causal analysis.
R package sentometrics analyzes text sentiment for predictions.
problem Unlocking value from textual data using sentiment analysis.
method Optimized textual sentiment indexation with R package sentometrics.
result Forecasted CBOE Volatility Index using text sentiment data.
R package stagedtrees learns staged tree structures from data.
problem Learning the structure of staged trees from data.
method Score-based and clustering-based algorithms implemented.
result Illustrated capabilities using two datasets.
R package huge simplifies graph estimation for high-dimensional data.
problem Estimating high-dimensional undirected graphs from data.
method Uses recent results in literature, including recent graph estimation methods.
result Improves on existing package glasso by providing more features and better efficiency.
Random Forests [Breiman:2001] (RF) are a fully non-parametric statistical method requiring no distributional assumptions on covariate relation to the response. RF are a robust, nonlinear technique that optimizes predictive accuracy by fitting an ensemble of trees to stabilize model estimates. The randomForestSRC packag…
Python package for SPD matrix distances, reproducible and extensible.
problem Computing distances between SPD matrices for various applications.
method Unified, extensible framework supporting multiple SPD metrics.
result Reproducible and accessible SPD matrix comparison tool.
A package for classification using conformal prediction methods.
problem Improving the reliability of machine learning predictions.
method Transductive and Inductive Conformal Prediction based on random forest.
result Computational efficiency and validity of predictions.
New R package for NMF evaluated on real-world data.
problem Limited comprehensive evaluations of NMF packages under real-world conditions.
method Systematic performance comparison of three NMF packages using real-world data.
result New package outperforms existing ones in computational efficiency and reconstruction accuracy.
Efficient packages solve SLOPE problem in multiple languages.
problem Efficiently solving SLOPE problem for various data types.
method Hybrid coordinate descent algorithm for GLMs with multiple loss functions.
result Packages outperform existing SLOPE implementations in speed.
This paper conducts an empirically study on the trade package composed of a sequence of consecutive purchases or sales of 23 stocks in Chinese stock market. We investigate the probability distributions of the execution time, the number of trades and the total trading volume of trade packages, and analyze the possible s…
Develops rMultiNet R package for multilayer network analysis.
problem Analyzing multilayer network data.
method Two frameworks: MMSBM and MMLSM for generating multilayer networks; methods for node and layer embedding; clustering.
result Provides tools for multilayer network analysis.
Witten deformation connects manifold spectra to Morse functions.
problem Understanding spectral properties of Riemannian manifolds.
method Rellich-Kato theorem applied to Witten deformation.
result Relates spectral package to Morse complex and harmonic oscillators.
Super learning in SAS matches R package performance in various tasks.
problem Improving machine learning performance in SAS.
method Developed a new SAS macro for super learning, comparing it to R package performance.
result SAS macro performs similarly to R package across simulated and real datasets.
bnlearn is an R package which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables. Both constraint-based and score-based algorithms are implemented, and can use the functionality provided by the snow package to improve their performance via parallel c…
The book "Handbook of Finsler geometry" has been included with a CD containing an elegant Maple package, FINSLER, for calculations in Finsler geometry. Using this package, an example concerning a Finsler generalization of Einstein's vacuum field equations was treated. In this example, the calculation of the components …
L0Learn solves sparse learning problems with millions of features.
problem Sparse learning problems with millions of features.
method Approximate algorithms based on coordinate descent and local combinatorial optimization.
result Achieves competitive run times and statistical performance.
BKP R package models spatially varying binomial probabilities efficiently.
problem Modeling spatially varying binomial probabilities efficiently.
method Beta Kernel Process (BKP) combining localized kernel-weighted likelihoods with conjugate beta priors.
result Closed-form posterior inference without requiring latent variables or intensive MCMC sampling.
fmeffects package interprets non-linear models in plain language.
problem Interpreting complex non-linear models.
method Forward marginal effects for model-agnostic explanations.
result First software implementation of forward marginal effects.
R package connects to OpenML for machine learning tasks and data sharing.
problem Efficiency and reproducibility in machine learning research.
method R package interface with OpenML platform.
result Enables easy data and task sharing, reproducibility, and collaboration.
DriveML automates machine learning tasks in R.
problem Manual machine learning tasks are time-consuming and error-prone.
method Automated data preparation, feature engineering, model building, and model explanation.
result DriveML performs best across different parameters compared to other packages.
ctsfeatures R package analyzes categorical time series data.
problem Lack of tools for analyzing categorical time series data.
method Provides functions for extracting statistical features and constructing graphs.
result Package enables traditional machine learning tasks on categorical time series.
KnotMosaics package simplifies knot theory computations in SageMath.
problem Efficiently computing knot mosaic diagrams and their properties.
method Developed a SageMath package for knot mosaic diagrams, implementing validation, strand tracing, and computation algorithms.
result Enabled easy computation of knot mosaic diagrams and their properties.
Julia package for Gaussian processes offers fast, flexible, and scalable tools.
problem Modeling complex data sources in various sciences and industries.
method Utilizes Julia's computational benefits for fast, flexible, and user-friendly Gaussian processes.
result Fast, scalable, and user-friendly Gaussian processes package for Julia.
Package {mlr3spatiotempcv} simplifies spatiotemporal resampling methods in R.
problem Assessing and tuning spatial and spatiotemporal machine learning models.
method Integrates various spatiotemporal resampling methods into the {mlr3} framework.
result Provides a consistent interface for spatiotemporal resampling methods.
ODTLearn learns optimal decision trees for predictive and prescriptive tasks.
problem Learning optimal decision trees for high-stakes predictive and prescriptive tasks.
method Mixed-integer optimization framework and object-oriented design.
result Implementation of optimal decision trees for various tasks.
stCEG models spatial events using Chain Event Graphs in R.
problem Modeling events over spatial areas.
method Full specification of CEG models from data, interactive plots, web GUI.
result First software for CEGs with full model customisation.
R package for counterfactual explanation methods.
problem Lack of unified interfaces for counterfactual explanation methods.
method Developed a modular R6-based interface for three existing counterfactual methods and proposed extensions.
result Comparison of implemented methods' quality and runtime behavior.
neuralGAM package interprets neural networks by fitting them to feature contributions.
problem difficulty understanding neural network decisions
method Generalized Additive Neural Networks (GAM)
result interpretable Deep Learning model with accurate feature contributions
HiPart offers an efficient, interactive tool for hierarchical clustering.
problem Efficient and interpretable hierarchical clustering for Big Data.
method Divisive hierarchical clustering algorithms with interactive visualizations.
result High computational efficiency and interpretability in Big Data applications.
R package GAS for analyzing time series with GAS models.
problem Analyzing time-varying parameters in nonlinear models.
method Using the score function to drive time variation in parameters.
result Estimation of time-varying conditional densities of financial assets.
Python package for estimating intrinsic dimensionality of datasets.
problem Estimating intrinsic dimensionality for machine learning applications.
method Implementation of various intrinsic dimension estimators in Python.
result Benchmarking of ID estimation methods on real and synthetic data.
Python package ajdmom simplifies moment formula derivation for jump diffusions.
problem Deriving moment formulae for complex jump diffusion processes.
method Automatically generates closed-form expressions and derivatives for any order of moments.
result Enhances usability and usability of affine jump diffusion models.
R package abn fits Bayesian models to observational data.
problem Analyzing complex observational datasets with Bayesian networks.
method Bayesian and information theoretic scoring, exact and greedy search algorithms.
result Effective modeling of mixed data types and prior knowledge integration.
TML package uses tropical geometry for machine learning tasks.
problem Statistical learning problems.
method Tropical convexity computations, Hit and Run sampler, tropical metrics.
result First R package for tropical geometric machine learning.
sparsebn learns large Bayesian networks from high-dimensional data.
problem Learning graphical models from large, high-dimensional datasets with interventions.
method Focuses on scalability and consistency in high-dimensional settings, learning causal networks from data.
result Achieves the goal of learning a causal network from data.
R package for online forecasting in various fields.
problem Frequent updates of forecasts in online settings.
method Generalized setup for time-adaptive fitting of dynamical and non-linear models.
result Effective use of forecasts as model inputs in operational settings.
BiDAG R package learns and samples Bayesian network structures efficiently.
problem Efficiently learning and sampling Bayesian network structures.
method Hybrid approach combining PC algorithm, iterative order MCMC, and partition MCMC.
result BiDAG can handle both discrete and continuous data.
latrend simplifies longitudinal clustering for numeric measurements.
problem Clustering of longitudinal data to identify common trends over time.
method Unified framework for applying various clustering methods.
result Facilitates comparison and rapid prototyping of new methods.