arXiv research

DynForest R package predicts outcomes with time-dependent predictors.

A new method accounts for predictor dependencies in XAI feature rankings.

The article compares predictor importance in classification problems with categorical outcomes.

CDPs visualize causal dependencies in AI models.

Paper introduces SUEL model for integrating predictors without labeled data.

New bounds explain deterministic non-smooth deep nets without large Lipschitz constants.

Kernel Three-Pass Regression Filter improves forecasting efficiency for nonlinear dependencies.

We consider a problem of data integration. Consider determining which genes affect a disease. The genes, which we call predictor objects, can be measured in different experiments on the same individual. We address the question of finding which genes are predictors of disease by any of the experiments. Our formulation i…

Although there is a rich literature on methods for allowing the variance in a univariate regression model to vary with predictors, time and other factors, relatively little has been done in the multivariate case. Our focus is on developing a class of nonparametric covariance regression models, which allow an unknown p …

Proposes a method to create fair, robust predictors that remain consistent across different scenarios.

Overparameterized MLR fits hyper-curves, improving model robustness.

Fairness measures fail in predictive settings that intentionally shift outcomes.

The Partial Information Decomposition (PID) [arXiv:1004.2515] provides a theoretical framework to characterize and quantify the structure of multivariate information sharing. A new method (Idep) has recently been proposed for computing a two-predictor PID over discrete spaces. [arXiv:1709.06653] A lattice of maximum en…

Learning linear predictors with the logistic loss---both in stochastic and online settings---is a fundamental task in machine learning and statistics, with direct connections to classification and boosting. Existing "fast rates" for this setting exhibit exponential dependence on the predictor norm, and Hazan et al. (20…

New sample complexity bounds for linear predictors and neural networks, focusing on initialization.

The paper improves PAC-Bayes bounds for data-dependent predictors.

Study shows how to improve contextual bandits with loss predictors.

Adaptive kernels from neural networks improve model performance.

New method predicts spatio-temporal data with short and long-range dependence.

New method uses quotient predictor space for better PAC-Bayes bounds, reducing KL divergence and improving model performance.

Split conformal prediction works well for time series despite temporal dependence.

The paper explores why a specific type of predictor works well in noisy data.

PAC-Bayes bound requires prior to place mass on high-performing predictors.

The paper develops a deep neural network estimator for weakly dependent processes with various loss functions.

Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors far greater than the sample size. In order to identify more novel biomarkers and understand biological mechanisms, it is vital to detect signals weakly associated with outcomes among ultrahigh-dimensional predictor…

Study shows linear predictors fail with missing data, but simpler approximations and neural networks can work.

Neural network model forecasts extreme flood risk.

In this article, we derive concentration inequalities for the cross-validation estimate of the generalization error for stable predictors in the context of risk assessment. The notion of stability has been first introduced by \cite{DEWA79} and extended by \cite{KEA95}, \cite{BE01} and \cite{KUNIY02} to characterize cla…

Deep neural networks estimate regression functions on manifolds.

Bayesian neural networks explore rare fluctuations for better feature learning.

We study online linear regression problems in a distributed setting, where the data is spread over a network. In each round, each network node proposes a linear predictor, with the objective of fitting the \emph{network-wide} data. It then updates its predictor for the next round according to the received local feedbac…

New methods for CI testing under model misspecification.

New theory explains how noisy, high-dimensional data can still lead to robust predictions.

Study shows time-varying stock returns across economic states.

DynForest predicts event probabilities from longitudinal data, handling endogenous predictors.

In this work, we propose the kernel Pitman-Yor process (KPYP) for nonparametric clustering of data with general spatial or temporal interdependencies. The KPYP is constructed by first introducing an infinite sequence of random locations. Then, based on the stick-breaking construction of the Pitman-Yor process, we defin…

Bayes predictor remains robust to ignorable missingness shifts.

GIDS reduces high-dimensional response and predictor spaces, improving interpretability and computational efficiency.

Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for more than a few predictors which has limited their application mostl…

Detection of protein-protein interactions (PPIs) plays a vital role in molecular biology. Particularly, infections are caused by the interactions of host and pathogen proteins. It is important to identify host-pathogen interactions (HPIs) to discover new drugs to counter infectious diseases. Conventional wet lab PPI pr…

This study addresses the issue of predicting the glaucomatous visual field loss from patient disease datasets. Our goal is to accurately predict the progress of the disease in individual patients. As very few measurements are available for each patient, it is difficult to produce good predictors for individuals. A rece…

New bounds show linear predictors rarely overfit with certain optimization methods.

Sparse model selection by structural risk minimization leads to a set of a few predictors, ideally a subset of the true predictors. This selection clearly depends on the underlying loss function $\tilde L$. For linear regression with square loss, the particular (functional) Gradient Boosting variant $L_2-$Boosting exce…

This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment effects, and structural derivatives, based on modern machine learning (ML) tools. We represent this structural function as a conditional expec…

Decision trees are consistent for regression and classification tasks even with many predictors.

The paper decomposes probabilistic scores into reliability, uncertainty, and information loss.

Interpreting a nonparametric regression model with many predictors is known to be a challenging problem. There has been renewed interest in this topic due to the extensive use of machine learning algorithms and the difficulty in understanding and explaining their input-output relationships. This paper develops a unifie…

DFR models dynamic distributional data with weighted Fréchet means.