arXiv research

The paper explores how low-degree polynomials can detect shuffled linear regression models.

A new method resolves permutation issues in shuffled linear regression for large-scale applications.

The multivariate linear regression model with shuffled data and additive Gaussian noise arises in various correspondence estimation and matching problems. Focusing on the denoising aspect of this problem, we provide a characterization the minimax error rate that is sharp up to logarithmic factors. We also analyze the p…

Is it possible to perform linear regression on datasets whose labels are shuffled with respect to the inputs? We explore this question by proposing several estimators that recover the weights of a noisy linear model from labels that are shuffled by an unknown permutation. We show that the analog of the classical least-…

We consider the problem of inference in a linear regression model in which the relative ordering of the input features and output labels is not known. Such datasets naturally arise from experiments in which the samples are shuffled or permuted during the protocol. In this work, we propose a framework that treats the un…

The paper proposes an efficient estimator for linear regression with shuffled labels.

The paper analyzes the generalizability of linear autoencoders and multivariate linear regression.

Extends online linear regression to handle multivariate data.

Develops shuffling gradient-based methods for nonconvex-concave minimax optimization.

ROBOT framework solves regression without correspondence for large data and complex models.

Linear regression without correspondences is the problem of performing a linear regression fit to a dataset for which the correspondences between the independent samples and the observations are unknown. Such a problem naturally arises in diverse domains such as computer vision, data mining, communications and biology.…

TSRGA scales multivariate linear regression for feature-distributed data.

Study GLS estimator properties in multivariate regression with heteroskedastic and autocorrelated errors.

When using stochastic gradient descent to solve large-scale machine learning problems, a common practice of data processing is to shuffle the training data, partition the data across multiple machines if needed, and then perform several epochs of training on the re-shuffled (either locally or globally) data. The above …

Recently, there has been significant interest in linear regression in the situation where predictors and responses are not observed in matching pairs corresponding to the same statistical unit as a consequence of separate data collection and uncertainty in data integration. Mismatched pairs can considerably impact the …

Robust multivariate linear regression methods for online and offline use.

Random shuffle method boosts HF dataset size 10-21 times.

We consider the topic of multivariate regression on manifold-valued output, that is, for a multivariate observation, its output response lies on a manifold. Moreover, we propose a new regression model to deal with the presence of grossly corrupted manifold-valued responses, a bottleneck issue commonly encountered in pr…

Study phase transitions in shuffled regression problems.

Unified analysis for shuffling-type gradient methods in optimization.

Proposes a new model for non-linear regression of multivariate time series data.

This paper studies robust regression in the settings of Huber's $ε$-contamination models. We consider estimators that are maximizers of multivariate regression depth functions. These estimators are shown to achieve minimax rates in the settings of $ε$-contamination models for various regression problems including nonpa…

New algorithms for private generalized linear contextual bandits.

How does one find dimensions in multivariate data that are reliably expressed across repetitions? For example, in a brain imaging study one may want to identify combinations of neural signals that are reliably expressed across multiple trials or subjects. For a behavioral assessment with multiple ratings, one may want …

Improves MARS for nonparametric multivariate regression with dimension reduction.

In this paper, we consider a generalized multivariate regression problem where the responses are monotonic functions of linear transformations of predictors. We propose a semi-parametric algorithm based on the ordering of the responses which is invariant to the functional form of the transformation function. We prove t…

Improved robustness in multivariate regression and classification with DRO under Wasserstein metric.

We tackle permutation in linear regression with a new inference framework.

Efficiently finds sparse solutions to max-plus equations for convex regression.

In this paper, we investigate a multivariate multi-response (MVMR) linear regression problem, which contains multiple linear regression models with differently distributed design matrices, and different regression and output vectors. The goal is to recover the support union of all regression vectors using $l_1/l_2$-reg…

Combines MCTM and NF for flexible multivariate density regression with interpretable marginals.

Regularizes ML algorithms for robust multivariate analysis against distribution shifts.

Study shows the corrected Akaike criterion is inadmissible for estimating Kullback-Leibler discrepancy.

SMG combines shuffling and momentum for non-convex optimization.

We consider the problem of predicting several response variables using the same set of explanatory variables. This setting naturally induces a group structure over the coefficient matrix, in which every explanatory variable corresponds to a set of related coefficients. Most of the existing methods that utilize this gro…

Software helps teach latent variable methods in multivariate data analytics.

Develops fast approximations for conditional Shapley values in linear and polynomial models.

A new algorithm improves convergence rates for convex optimization problems.

A new Gaussian mechanism for differential privacy in the shuffle model is introduced.

A family of parsimonious Gaussian cluster-weighted models is presented. This family concerns a multivariate extension to cluster-weighted modelling that can account for correlations between multivariate responses. Parsimony is attained by constraining parts of an eigen-decomposition imposed on the component covariance …

Improves privacy amplification by shuffling for differential privacy.

New method uses exponential family priors to handle shuffled data problems.

New shuffling methods improve convergence without Lipschitz smoothness.

A new clustering method for functional data using skewed distributions.

Technology and collaboration enable dramatic increases in the size of psychological and psychiatric data collections, but finding structure in these large data sets with many collected variables is challenging. Decision tree ensembles like random forests (Strobl, Malley, and Tutz, 2009) are a useful tool for finding st…

We propose a nonconvex estimator for joint multivariate regression and precision matrix estimation in the high dimensional regime, under sparsity constraints. A gradient descent algorithm with hard thresholding is developed to solve the nonconvex estimator, and it attains a linear rate of convergence to the true regres…

This paper reviews SDR methods for multivariate response regression.

This work studies differential privacy in the context of the recently proposed shuffle model. Unlike in the local model, where the server collecting privatized data from users can track back an input to a specific user, in the shuffle model users submit their privatized inputs to a server anonymously. This setup yields…