arXiv research

INCAD clusters and detects anomalies in streaming data without thresholds.

Quantile gradient boosted trees outperform other models in predicting NO2 concentration distributions.

Paper presents a probabilistic model to improve LLM cascade performance.

Data-driven anomaly detection methods typically build a model for the normal behavior of the target system, and score each data instance with respect to this model. A threshold is invariably needed to identify data instances with high (or low) scores as anomalies. This presents a practical limitation on the applicabili…

This project explores several Machine Learning methods to predict movie genres based on plot summaries. Naive Bayes, Word2Vec+XGBoost and Recurrent Neural Networks are used for text classification, while K-binary transformation, rank method and probabilistic classification with learned probability threshold are employe…

New proof shows neural networks can memorize training data with high accuracy.

A new SSL method uses instance-dependent thresholds to improve accuracy.

New method provides calibrated feature importance explanations for regression models.

We present a methodology for probabilistic load forecasting that is based on lasso (least absolute shrinkage and selection operator) estimation. The model considered can be regarded as a bivariate time-varying threshold autoregressive(AR) process for the hourly electric load and temperature. The joint modeling approach…

Paper proposes efficient AL algorithms for optimizing product performance under environmental variability.

We consider the problem of clustering noisy high-dimensional data points into a union of low-dimensional subspaces and a set of outliers. The number of subspaces, their dimensions, and their orientations are unknown. A probabilistic performance analysis of the thresholding-based subspace clustering (TSC) algorithm intr…

The support recovery problem consists of determining a sparse subset of a set of variables that is relevant in generating a set of observations, and arises in a diverse range of settings such as compressive sensing, and subset selection in regression, and group testing. In this paper, we take a unified approach to supp…

Proposes Likelihood Regret for VAEs to improve OOD detection.

Extends probabilistic approach for Kahler-Einstein metrics on Fano manifolds.

Thresholded Lasso bandit minimizes regret in sparse linear bandits.

Transforms distance-based outlier scores into interpretable probabilistic estimates.

We consider the problem of clustering a set of high-dimensional data points into sets of low-dimensional linear subspaces. The number of subspaces, their dimensions, and their orientations are unknown. We propose a simple and low-complexity clustering algorithm based on thresholding the correlations between the data po…

Study compares two methods for predicting extreme atmospheric events.

Deep Gaussian processes can have non-degenerate and non-Gaussian limits.

Verifying probabilistic forecasts for extreme events is a highly active research area because popular media and public opinions are naturally focused on extreme events, and biased conclusions are readily made. In this context, classical verification methods tailored for extreme events, such as thresholded and weighted …

Assessing the fairness of a decision making system with respect to a protected class, such as gender or race, is challenging when class membership labels are unavailable. Probabilistic models for predicting the protected class based on observable proxies, such as surname and geolocation for race, are sometimes used to …

ProSub uses angles in feature space to classify data as in- or out-of-distribution.

Critiques binary classification evaluation methods, advocating for proper scoring rules.

Statistical model checking for PCTL on MDPs using reinforcement learning.

Paper introduces OCRR Score for quantifying DeFi wallet credit risk.

A new procedure is presented for the objective comparison and evaluation of default definitions. This allows the lender to find a default threshold at which the financial loss of a loan portfolio is minimised, in accordance with Basel II. Alternative delinquency measures, other than simply measuring payments in arrears…

TPM improves medical image segmentation by separating foreground and background.

A novel ABC method for high-dimensional inverse problems using generative modeling and subset simulation.

A new method for detecting anomalies in large, high-dimensional data streams using probabilistic forest models.

New method calibrates probabilistic linear solver for online coverage guarantees.

This paper connects ultrametric overlap gap properties to parametric RDT for symmetric binary perceptrons.

Introduces Rashomon Capacity to measure predictive multiplicity in probabilistic classifiers.

A new algorithm reduces graph complexity for better dense subgraph analysis.

Learning β for k-SAT with one sample is hard, especially for low degrees.

We consider training probabilistic classifiers in the case of a large number of classes. The number of classes is assumed too large to perform exact normalisation over all classes. To account for this we consider a simple approach that directly approximates the likelihood. We show that this simple approach works well o…

Confident learning estimates label quality by identifying and correcting noisy data.

Model change detection is studied, in which there are two sets of samples that are independently and identically distributed (i.i.d.) according to a pre-change probabilistic model with parameter $θ$, and a post-change model with parameter $θ'$, respectively. The goal is to detect whether the change in the model is sign…

Adaptive batch sizes improve active learning efficiency and flexibility.

This paper examines transitions in sniping behavior among algorithmic traders, finding new profitable strategies.

We study simultaneous price drops of real stocks and show that for high drop thresholds they follow a power-law distribution. To reproduce these collective downturns, we propose a minimal self-organized model of cascade spreading based on a probabilistic response of the system elements to stress conditions. This model …

Fragility curves which express the failure probability of a structure, or critical components, as function of a loading intensity measure are nowadays widely used (i) in Seismic Probabilistic Risk Assessment studies, (ii) to evaluate impact of construction details on the structural performance of installations under se…

This paper improves conformal prediction to be robust to perturbations.

Optimizes pruning masks for neural networks using probabilistic fine-tuning and PAC-Bayes bounds.

Iterative thresholding algorithms seek to optimize a differentiable objective function over a sparsity or rank constraint by alternating between gradient steps that reduce the objective, and thresholding steps that enforce the constraint. This work examines the choice of the thresholding operator, and asks whether it i…

Traditionally, community detection in graphs can be solved using spectral methods or posterior inference under probabilistic graphical models. Focusing on random graph families such as the stochastic block model, recent research has unified both approaches and identified both statistical and computational detection thr…

Developed a new thresholding method that connects soft and hard thresholding.

Federated Learning with L0 constraint improves sparsity and performance.

Entropy regularization improves sparse model discovery in federated learning.