arXiv research

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

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

AdaDPSyn generates synthetic examples to protect private data in ICL.

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…

FONT clusters patients across health systems with privacy and efficiency.

Enhances patient failure prediction using dynamic survival models.

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…

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.

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

Mixup improves model robustness and generalization by convexly combining examples.

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.

Efficiently constructs prediction bands with minimal assumptions.

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.

In this paper, we attempt to improve Statistical Machine Translation (SMT) systems on a very diverse set of language pairs (in both directions): Czech - English, Vietnamese - English, French - English and German - English. To accomplish this, we performed translation model training, created adaptations of training sett…

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…

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

New classifiers for HDLSS data classify without tuning, robustly.

This paper proposes and analyzes a novel clustering algorithm that combines graph-based diffusion geometry with techniques based on density and mode estimation. The proposed method is suitable for data generated from mixtures of distributions with densities that are both multimodal and have nonlinear shapes. A crucial …

Adapts Exp3 to adversarial bandits with delays and data.

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.

ALPE improves mid-price forecasting in HFT with real-time data.

A new autoencoder architecture captures multiscale data.

GraphPPD models graph-level uncertainty using GNN embeddings.

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

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

Radio tomographic imaging (RTI) is an emerging technology for localization of physical objects in a geographical area covered by wireless networks. With attenuation measurements collected at spatially distributed sensors, RTI capitalizes on spatial loss fields (SLFs) measuring the absorption of radio frequency waves at…

Gradient descent reshapes the function space of neural networks.

Novel framework predicts cell responses to perturbations using GRNs.

Adaptive PCR improves panel data analysis with uniform guarantees.

Deep neural networks improve proximal inference for causal effects.

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.