arXiv research

The study compares Euclidean and cosine distances in medical drug prescription prediction.

New method uses Cantor embeddings and Wasserstein distances to analyze predictive states in time series data.

The paper studies the distance from calibration in sequential prediction, proving upper and lower bounds.

Predicting RNA base distances using a large language model.

In building intelligent transportation systems such as taxi or rideshare services, accurate prediction of travel time and distance is crucial for customer experience and resource management. Using the NYC taxi dataset, which contains taxi trips data collected from GPS-enabled taxis [23], this paper investigates the use…

A new method estimates uncertainty without explicit prediction models.

A new conformal prediction framework for graph-valued outputs using Z-Gromov-Wasserstein distances.

Enhances graph comparison by incorporating edge features using Fused Gromov-Wasserstein distance.

Learning to predict multi-label outputs is challenging, but in many problems there is a natural metric on the outputs that can be used to improve predictions. In this paper we develop a loss function for multi-label learning, based on the Wasserstein distance. The Wasserstein distance provides a natural notion of dissi…

Study predicts climate data at distant locations using machine learning.

The paper uses distance correlation for brain connectivity and a novel multi-task learning model for age prediction.

Connects robust optimization to conformal prediction for uncertainty sets.

Machine learning is often used in virtual screening to find compounds that are pharmacologically active on a target protein. The weave module is a type of graph convolutional deep neural network that uses not only features focusing on atoms alone (atom features) but also features focusing on atom pairs (pair features);…

The study improves model performance prediction on unseen distributions.

DW-KNN improves KNN by integrating distance and neighbor reliability for better prediction accuracy.

Bayesian neural networks improve uncertainty calibration with DAP priors.

Mahalanobis distance detects anomalies well, but not for classification.

Improves uncertainty estimation and OOD detection in neural networks.

Study predicts U.S. county COVID-19 growth using demographic and social distancing data.

SMERF improves distance learning with decision forests.

Novel neural network approximates exact distance for robust classification.

DE improves GNNs by distinguishing graph substructures, enhancing accuracy.

High-dimensional prediction is a challenging problem setting for traditional statistical models. Although regularization improves model performance in high dimensions, it does not sufficiently leverage knowledge on feature importances held by domain experts. As an alternative to standard regularization techniques, we p…

New method predicts spatial events like hurricanes and earthquakes with uncertainty.

Proposes a new Sliced-Wasserstein distance for covariance matrices in M/EEG signals.

A statistical model predicts generalization in few-shot learning.

Proposes ridge regression on Riemannian manifolds for time-series prediction.

Deep neural networks have gained tremendous success in a broad range of machine learning tasks due to its remarkable capability to learn semantic-rich features from high-dimensional data. However, they often require large-scale labelled data to successfully learn such features, which significantly hinders their adaptio…

The paper proposes a method to assess when automated predictions are reliable.

Nearest Neighbors Algorithm is a Lazy Learning Algorithm, in which the algorithm tries to approximate the predictions with the help of similar existing vectors in the training dataset. The predictions made by the K-Nearest Neighbors algorithm is based on averaging the target values of the spatial neighbors. The selecti…

The paper uses distance covariance to improve fairness in machine learning models.

Multi-label classification is a type of supervised learning where an instance may belong to multiple labels simultaneously. Predicting each label independently has been criticized for not exploiting any correlation between labels. In this paper we propose a novel approach, Nearest Labelset using Double Distances (NLDD)…

The paper explores fair predictors in supervised learning using IPMs and Kolmogorov distance.

SNGP improves single-model deep uncertainty by enhancing distance-awareness.

Travel time estimation is a fundamental problem in transportation science with extensive literature. The study of these techniques has intensified due to availability of many publicly available large trip datasets. Recently developed deep learning based models have improved the generality and performance and have focus…

WR-CP reduces prediction set size and coverage gap under distribution shift.

Enhances forecasting of complex systems using FKMD.

Predictive models are being increasingly used to support consequential decision making at the individual level in contexts such as pretrial bail and loan approval. As a result, there is increasing social and legal pressure to provide explanations that help the affected individuals not only to understand why a predictio…

A robust conformal method for set estimation using non-conformity scores.

A new framework for measuring uncertainty in machine learning models.

Proposes new loss functions for better handling bimodal predictive uncertainty.

The paper studies and mitigates accuracy disparity in regression models.

SHAP Distance assesses semantic fidelity of synthetic tabular data.

Parts of Texas, Oklahoma, and Kansas have experienced increased rates of seismicity in recent years, providing new datasets of earthquake recordings to develop ground motion prediction models for this particular region of the Central and Eastern North America (CENA). This paper outlines a framework for using Artificial…

Energy distance measures feature heterogeneity in federated learning.

A new framework enhances IDW models for complex industrial datasets.

This paper investigates methods for quantifying similarity between audio signals, specifically for the task of of cover song detection. We consider an information-theoretic approach, where we compute pairwise measures of predictability between time series. We compare discrete-valued approaches operating on quantised au…

A method for quickly determining deployment schedules that meet a given fuel cycle demand is presented here. This algorithm is fast enough to perform in situ within low-fidelity fuel cycle simulators. It uses Gaussian process regression models to predict the production curve as a function of time and the number of depl…