arXiv research

Paper develops DLTF to learn optimized dictionaries for efficient thresholded feature recovery.

The article examines different thresholding methods for improving PAM algorithm in cancer classification.

NeuralFDR learns optimal discovery thresholds from hypothesis features.

Multi-task feature learning aims to identity the shared features among tasks to improve generalization. It has been shown that by minimizing non-convex learning models, a better solution than the convex alternatives can be obtained. Therefore, a non-convex model based on the capped-$\ell_{1},\ell_{1}$ regularization wa…

System classifies lung CT scans into normal or COVID-19 using machine learning.

We study graph matching with correlated Gaussian features and find thresholds for exact recovery.

CIT and CIF improve feature selection for downstream prediction.

Aligns databases with Gaussian features using MAP estimation and thresholding.

Thresholded Lasso bandit minimizes regret in sparse linear bandits.

Lower bound proves ridgeless regression performs poorly near interpolation threshold.

Consider a two-class classification problem where the number of features is much larger than the sample size. The features are masked by Gaussian noise with mean zero and covariance matrix $Σ$, where the precision matrix $Ω=Σ^{-1}$ is unknown but is presumably sparse. The useful features, also unknown, are sparse and e…

In active learning, the user sequentially chooses values for feature $X$ and an oracle returns the corresponding label $Y$. In this paper, we consider the effect of feature noise in active learning, which could arise either because $X$ itself is being measured, or it is corrupted in transmission to the oracle, or the o…

Optimizes SSVEP-based BCI performance by automating threshold selection.

Study connects database alignment and planted matching using Gaussian features.

It has recently been observed that certain extremely simple feature encoding techniques are able to achieve state of the art performance on several standard image classification benchmarks including deep belief networks, convolutional nets, factored RBMs, mcRBMs, convolutional RBMs, sparse autoencoders and several othe…

Proposes ACLAE-DT for unsupervised anomaly detection in multivariate time series.

HARFE approximates sparse additive functions using random features and ridge regression.

Detects adversarial examples with feature attribution differences.

New method provides calibrated feature importance explanations for regression models.

Neural regression trees convert regression to classification more effectively.

The paper tackles fair classification with multiple sensitive features.

FILTER model uses fusion penalized logistic threshold regression for high-dimensional data with unknown cut points.

Improves signature verification accuracy using deep CNN features.

Proposes an ensemble framework for outlier detection with embedded feature selection.

A new algorithm detects out-of-distribution samples by concentrating them in feature space.

STAT-SVD method reduces high-dimensional data sparsity, achieving optimal estimation.

A hierarchical model shows how scaling laws emerge from sequential feature recovery.

Optimizes waiting thresholds for node health states in Azure.

New algorithm adds discretized features to improve predictive models.

Develops a fair classifier for deep learning models.

Study shows 'Ordinal Neural Collapse' in deep OR tasks, revealing simple geometric relationships.

DeepAM optimizes deep neural networks for image super-resolution.

LTP learns per-layer thresholds for efficient pruning of deep networks.

New feature selection method DRPT reduces genomic datasets by removing irrelevant features and detecting correlations.

Develops a statistical test for IV, improving feature selection reliability.

Critical volatility triggers log-normal to power-law transitions in interconnected systems.

One-Class Classification (OCC) has been prime concern for researchers and effectively employed in various disciplines. But, traditional methods based one-class classifiers are very time consuming due to its iterative process and various parameters tuning. In this paper, we present six OCC methods based on extreme learn…

The robust PCA problem, wherein, given an input data matrix that is the superposition of a low-rank matrix and a sparse matrix, we aim to separate out the low-rank and sparse components, is a well-studied problem in machine learning. One natural question that arises is that, as in the inductive setting, if features are…

The study examines when to trust confidence thresholding in pseudo-labelling regression.

DNF-Net tackles tabular data challenges with neural architecture.

We present a novel distribution-free approach, the data-driven threshold machine (DTM), for a fundamental problem at the core of many learning tasks: choose a threshold for a given pre-specified level that bounds the tail probability of the maximum of a (possibly dependent but stationary) random sequence. We do not ass…

Supervised linear feature extraction can be achieved by fitting a reduced rank multivariate model. This paper studies rank penalized and rank constrained vector generalized linear models. From the perspective of thresholding rules, we build a framework for fitting singular value penalized models and use it for feature …

This work efficiently learns linear threshold functions from label proportions using Gaussian distributions.

A new method monitors unstructured 3D shapes without registration.

Decision forests learn to model text by evaluating categorical-set conditions.

SISR improves feature attribution in complex payoff schemes.

Paper uses deep learning for accurate, monotonic cardinality estimation.

Deep CNNs improve technical forecasting accuracy.