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.
Study proposes Local Linear Encoding for better feature discretization.
A novel graph spectral method for mixed categorical and numerical data.
Existing feature selection methods fail to properly account for interactions between features when evaluating feature subsets. In this paper, we attempt to remedy this issue by using orthogonal variance decomposition to evaluate features. The orthogonality of the decomposition allows us to directly calculate the total …
Conventional mutual information (MI) based feature selection (FS) methods are unable to handle heterogeneous feature subset selection properly because of data format differences or estimation methods of MI between feature subset and class label. A way to solve this problem is feature transformation (FT). In this study,…
NGRC shows numerical instabilities with short lags and high-degree polynomials.
Improved BIRCH clustering method to avoid numeric issues.
Enhances FM models for numerical features using function basis encoding.
Bayesian Topic Regression models causal inference with text and numerical data.
Researchers quantify the relationship between feature depth and performance in deep neural networks.
Neural networks in many varieties are touted as very powerful machine learning tools because of their ability to distill large amounts of information from different forms of data, extracting complex features and enabling powerful classification abilities. In this study, we use neural networks to extract features from b…
The paper analyzes how deep models memorize spurious features.
A new method corrects weight values to improve treatment effect estimation.
Multivariate splines linked to infinitely-wide neural networks with improved numerical performance.
This paper deals with the problem of large-scale linear supervised learning in settings where a large number of continuous features are available. We propose to combine the well-known trick of one-hot encoding of continuous features with a new penalization called \emph{binarsity}. In each group of binary features comin…
This work proposes a geometric approach to equivariant message passing on Riemannian manifolds.
New methods reduce extrapolation errors in feature importance.
Feature selection is a technique to screen out less important features. Many existing supervised feature selection algorithms use redundancy and relevancy as the main criteria to select features. However, feature interaction, potentially a key characteristic in real-world problems, has not received much attention. As a…
A new method generates mixed-type features in tabular data with improved realism and accuracy.
Random sinusoidal features are a popular approach for speeding up kernel-based inference in large datasets. Prior to the inference stage, the approach suggests performing dimensionality reduction by first multiplying each data vector by a random Gaussian matrix, and then computing an element-wise sinusoid. Theoretical …
Support Vector Machines have been a popular topic for quite some time now, and as they develop, a need for new methods of feature selection arises. This work presents various approaches SVM feature selection developped using new tools such as entropy measurement and K-medoid clustering. The work focuses on the use of o…
Regularized target encoding beats traditional methods for high cardinality features in ML.
Pattern analysis often requires a pre-processing stage for extracting or selecting features in order to help the classification, prediction, or clustering stage discriminate or represent the data in a better way. The reason for this requirement is that the raw data are complex and difficult to process without extractin…
The paper solves complex swing option pricing equations with numerical methods.
Language models can predict numeric values as strings.
New metrics reveal oversmoothing in GNNs more accurately than traditional methods.
In this Article, a fast numerical numerical algorithm for pricing discrete double barrier option is presented. According to Black-Scholes model, the price of option in each monitoring date can be evaluated by a recursive formula upon the heat equation solution. These recursive solutions are approximated by using Legend…
The goal of this paper is to assess the utility of Reduced-Order Models (ROMs) developed from 3D physics-based models for predicting transient thermal power output for an enhanced geothermal reservoir while explicitly accounting for uncertainties in the subsurface system and site-specific details. Numerical simulations…
Anisotropic neural network selects relevant features from datasets.
Irregular features disrupt the desired classification. In this paper, we consider aggressively modifying scales of features in the original space according to the label information to form well-separated clusters in low-dimensional space. The proposed method exploits spectral clustering to derive scaling factors that a…
Have you ever wondered how your feature space is impacting the prediction of a specific sample in your dataset? In this paper, we introduce Single Sample Feature Importance (SSFI), which is an interpretable feature importance algorithm that allows for the identification of the most important features that contribute to…
Machine learning models predict the behavior of negatively buoyant jets from wastewater.
We consider the problem of improving kernel approximation via randomized feature maps. These maps arise as Monte Carlo approximation to integral representations of kernel functions and scale up kernel methods for larger datasets. Based on an efficient numerical integration technique, we propose a unifying approach that…
Slow feature analysis (SFA) is a method for extracting slowly varying features from a quickly varying multidimensional signal. An open source Matlab-implementation sfa-tk makes SFA easily useable. We show here that under certain circumstances, namely when the covariance matrix of the nonlinearly expanded data does not …
Introduces greedy feature selection for classifier-dependent feature ranking.
A novel Bayesian computation method using importance weighting improves numerical stability and performance.
The paper proposes a method to analyze categorical feature interactions in large datasets using graph covariance and LLMs.
Conventional seismic techniques for detecting the subsurface geologic features are challenged by limited data coverage, computational inefficiency, and subjective human factors. We developed a novel data-driven geological feature detection approach based on pre-stack seismic measurements. Our detection method employs a…
A conjecturing machine uncovers feature relationships in data.
A new method for dynamic feature selection outperforms existing approaches.
SAGE quantifies feature importance in machine learning models.
R2N learns interpretable rules and literals from numerical features.
New framework investigates how fake features affect model generalization.
A new method for efficient nonlinear process monitoring using random Bernoulli features.
We propose a tree regularization framework, which enables many tree models to perform feature selection efficiently. The key idea of the regularization framework is to penalize selecting a new feature for splitting when its gain (e.g. information gain) is similar to the features used in previous splits. The regularizat…
In subgroup discovery, also known as supervised pattern mining, discovering high quality one-dimensional subgroups and refinements of these is a crucial task. For nominal attributes, this is relatively straightforward, as we can consider individual attribute values as binary features. For numerical attributes, the task…
Develops method to assess feature importance in black-box models for unconditional distribution.
Many current approaches to the design of intrusion detection systems apply feature selection in a static, non-adaptive fashion. These methods often neglect the dynamic nature of network data which requires to use adaptive feature selection techniques. In this paper, we present a simple technique based on incremental le…
2L-FUSE enhances feature sparsity through kernel learning.