arXiv research

A new method for learning function parameters in operators using data-adaptive RKHS.

A new data-adaptive prior stabilizes kernel learning in operators.

Fairness of classification and regression has received much attention recently and various, partially non-compatible, criteria have been proposed. The fairness criteria can be enforced for a given classifier or, alternatively, the data can be adapated to ensure that every classifier trained on the data will adhere to d…

Generative models learn better with data-adaptive noise.

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlar…

Enhances patient failure prediction using dynamic survival models.

New method for valid inference from ML-predicted data.

Orthogonal Matching Pursuit (OMP) plays an important role in data science and its applications such as sparse subspace clustering and image processing. However, the existing OMP-based approaches lack of data adaptiveness so that the data cannot be represented well enough and may lose the accuracy. This paper proposes a…

DALTON improves ODE parameter estimation by learning from noisy data.

X-VAE uses data-adaptive Gaussian priors to improve latent space modeling.

DFIV uses deep features for IV regression, achieving optimal rates.

We address the problem of defining a network graph on a large collection of classes. Each class is comprised of a collection of data points, sampled in a non i.i.d. way, from some unknown underlying distribution. The application we consider in this paper is a large scale high dimensional survey of people living in the …

New algorithms for model selection in linear contextual bandits without feature diversity conditions.

Proposes a new method for kernel density estimation using stagewise minimization and a simple dictionary.

AdaDPSyn generates synthetic examples to protect private data in ICL.

HS-MoE selects sparse experts using adaptive priors and data-adaptive gating.

Proposes a robust Q-learning method to improve treatment strategy estimation.

The consistency of doubly robust estimators relies on consistent estimation of at least one of two nuisance regression parameters. In moderate to large dimensions, the use of flexible data-adaptive regression estimators may aid in achieving this consistency. However, $n^{1/2}$-consistency of doubly robust estimators is…

TSCI estimates treatment effects using machine learning and data-adaptive methods for invalid instruments.

Oblique BART improves tree-based predictions.

DoubleAdapt improves stock trend forecasting by adapting models to evolving data.

Proposes a new method for estimating non-pathwise differentiable functional parameters.

We consider the problem of estimating a regression function in the common situation where the number of features is small, where interpretability of the model is a high priority, and where simple linear or additive models fail to provide adequate performance. To address this problem, we present Maximum Variance Total V…

FONT clusters patients across health systems with privacy and efficiency.

New insights on bias in multi-armed bandits under conditional sampling.

Study identifies unique minimizers for interaction kernels in particle systems.

Linear regression is arguably the most prominent among statistical inference methods, popular both for its simplicity as well as its broad applicability. On par with data-intensive applications, the sheer size of linear regression problems creates an ever growing demand for quick and cost efficient solvers. Fortunately…

Improved forecasting for irregularly-sampled time series using kernel flows.

Improves numerical solution of ill-conditioned linear systems for machine learning.

Method learns SDEs from one trajectory using GP priors and randomized cross-validation.

Current statistical inference problems in areas like astronomy, genomics, and marketing routinely involve the simultaneous testing of thousands -- even millions -- of null hypotheses. For high-dimensional multivariate distributions, these hypotheses may concern a wide range of parameters, with complex and unknown depen…

Gradient descent reshapes the function space of neural networks.

Iterative shrinkage/thresholding algorithm (ISTA) is a well-studied method for finding sparse solutions to ill-posed inverse problems. In this letter, we present a data-driven scheme for learning optimal thresholding functions for ISTA. The proposed scheme is obtained by relating iterations of ISTA to layers of a simpl…

Proposes new methods for inference in GLMs without assuming model correctness.

Consider the problem: given the data pair $(\mathbf{x}, \mathbf{y})$ drawn from a population with $f_*(x) = \mathbf{E}[\mathbf{y} | \mathbf{x} = x]$, specify a neural network model and run gradient flow on the weights over time until reaching any stationarity. How does $f_t$, the function computed by the neural network…

Machine learning improves trial analysis precision by adjusting for prognostic variables.

We present the first adaptive strategy for active learning in the setting of classification with smooth decision boundary. The problem of adaptivity (to unknown distributional parameters) has remained opened since the seminal work of Castro and Nowak (2007), which first established (active learning) rates for this sett…

HAR regression improves performance on small datasets.

ADML combines debiased learning with data-driven model selection for efficient inference.

We introduce Interpolation Consistency Training (ICT), a simple and computation efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm. ICT encourages the prediction at an interpolation of unlabeled points to be consistent with the interpolation of the predictions at those points…

Novel method for time-series prediction with tighter confidence intervals.

2L-FUSE enhances feature sparsity through kernel learning.

We consider empirical risk minimization of linear predictors with convex loss functions. Such problems can be reformulated as convex-concave saddle point problems, and thus are well suitable for primal-dual first-order algorithms. However, primal-dual algorithms often require explicit strongly convex regularization in …

This work proves convergence of adaptive resampling for random Fourier features.

Efficiently constructs prediction bands with minimal assumptions.

We consider the problem of comparing probability densities between two groups. A new probabilistic tensor product smoothing spline framework is developed to model the joint density of two variables. Under such a framework, the probability density comparison is equivalent to testing the presence/absence of interactions.…

Adapts Exp3 to adversarial bandits with delays and data.

Paper proposes a new method for imputing missing biomedical data.