arXiv research

Paper proposes an active learning method for surgical workflow recognition using long-range temporal dependency.

In recent years, probabilistic forecasting is an emerging topic, which is why there is a growing need of suitable methods for the evaluation of multivariate predictions. We analyze the sensitivity of the most common scoring rules, especially regarding quality of the forecasted dependency structures. Additionally, we pr…

Measuring dependence between two random variables is very important, and critical in many applied areas such as variable selection, brain network analysis. However, we do not know what kind of functional relationship is between two covariates, which requires the dependence measure to be equitable. That is, it gives sim…

We propose a theoretical framework for thinking about score normalization, which confirms that normalization is not needed under (admittedly fragile) ideal conditions. If, however, these conditions are not met, e.g. under data-set shift between training and runtime, our theory reveals dependencies between scores that c…

This paper examines how ESG scores can indicate riskiness.

New method disentangles feature importance scores in machine learning.

A new method simplifies sampling from complex distributions without using diffusions.

Signature kernel scoring rule improves weather forecasting by capturing temporal and spatial dependencies.

Survival regression method improves log-likelihood scores.

In learning with noisy labels, for every instance, its label can randomly walk to other classes following a transition distribution which is named a noise model. Well-studied noise models are all instance-independent, namely, the transition depends only on the original label but not the instance itself, and thus they a…

Improved sampling efficiency for inverse problems using variance-reduced diffusion methods.

Improved diffusion models for generative tasks without dimensionality constraints.

Null-Calibrated Conformal Selection via Target-Membership Scores

Training-free model learns SDE dynamics without training, accelerating parameter studies.

Random features provide a practical framework for large-scale kernel approximation and supervised learning. It has been shown that data-dependent sampling of random features using leverage scores can significantly reduce the number of features required to achieve optimal learning bounds. Leverage scores introduce an op…

Study improves KRR for non-i.i.d. data, with applications in denoising.

Paper proposes an algorithm to learn DAGs with indirect dependencies.

This work analyzes SGD for SGMs, providing convergence rates and error bounds.

The paper tackles fairness in scoring functions for binary classification.

This work extends diffusion models to handle heavy-tailed targets, improving score estimation and sampling guarantees.

Quaternion self-attention reduces computational cost and improves performance.

Estimates system parameters from a single observation using kernel-based score.

New method reduces confidence interval sizes for causal inference.

VLM judges rank well but score poorly; task difficulty and annotation quality affect interval width.

Traditional approaches to ranking in web search follow the paradigm of rank-by-score: a learned function gives each query-URL combination an absolute score and URLs are ranked according to this score. This paradigm ensures that if the score of one URL is better than another then one will always be ranked higher than th…

This work uncovers algorithm-dependent regularisation in diffusion models.

Modeling complex systems with multi-resolution data and causal dependencies.

Topological anomaly scores predict return curves in S&P 500 stocks

Study compares multivariate scoring rules for distribution forecasts.

Polynomial convergence proved for SGM, improving over previous methods.

A new activation function improves credit scoring accuracy for imbalanced datasets.

Binary testing for softmax models requires many samples, similar to leverage score models.

Generative model uses SDEs to transform data distributions.

Binary classification rules based on covariates typically depend on simple loss functions such as zero-one misclassification. Some cases may require more complex loss functions. For example, individual-level monitoring of HIV-infected individuals on antiretroviral therapy (ART) requires periodic assessment of treatment…

New method for PU learning with instance-dependent propensity scores.

We introduce SADs to reveal how network architecture shapes score-based generative models.

SRMC framework reduces Monte Carlo variance by history-based sampling in high-dimensional spaces.

ST-BCP narrows the coverage gap in BCP by transforming nonconformity scores.

New bounds for score matching in polynomial exponential families.

Algorithm learns non-Gaussian graphical models via Hessian scores and triangular transport.

In this work, we addressed the issue of combining linear classifiers using their score functions. The value of the scoring function depends on the distance from the decision boundary. Two score functions have been tested and four different combination strategies were investigated. During the experimental study, the pro…

Wavelet SGM accelerates generative modeling with linear time complexity.

In statistical analysis, measuring a score of predictive performance is an important task. In many scientific fields, appropriate scores were tailored to tackle the problems at hand. A proper score is a popular tool to obtain statistically consistent forecasts. Furthermore, a mathematical characterization of the proper…

A new score measures data reliability without ground truth.

SMRL uses score matching for efficient RL with exponential family models.

New method optimizes portfolios by dynamically integrating ESG constraints.

Diffusion Transformer captures spatial-temporal dependencies in sequential data.

Improved sampling from high-dimensional Gaussians using smoothed scores.