arXiv research

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

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

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.…

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 …

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

Study phase transitions in shuffled regression problems.

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…

New algorithms for private generalized linear contextual bandits.

In this paper, we propose a unified convergence analysis for a class of generic shuffling-type gradient methods for solving finite-sum optimization problems. Our analysis works with any sampling without replacement strategy and covers many known variants such as randomized reshuffling, deterministic or randomized singl…

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

SMG combines shuffling and momentum for non-convex optimization.

A new algorithm improves convergence rates for convex optimization problems.

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

Improves privacy amplification by shuffling for differential privacy.

New shuffling methods improve convergence without Lipschitz smoothness.

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

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…

Simpler, faster algorithm for uniformity testing in the shuffle model.

BUDS balances privacy and utility by shuffling data, achieving strong privacy with minimal loss.

New convergence rates for shuffling gradient methods without strong convexity.

Optimal SGD rates achieved with shuffling, covering non-convex and convex cases.

New convergence bounds for shuffling-based SGD methods in distributed learning.

Proposes methods to recover labels from shuffled networks using graph averages.

An exciting new development in differential privacy is the shuffled model, in which an anonymous channel enables non-interactive, differentially private protocols with error much smaller than what is possible in the local model, while relying on weaker trust assumptions than in the central model. In this paper, we stud…

Improved shuffling technique amplifies privacy guarantees for anonymous data contributions.

Improved shuffling gradient methods converge faster for nonsmooth convex optimization.

Enhanced financial reward with shuffled feature CNN-DRL.

New neural model processes 2D data with long-range dependencies efficiently.

We study the performance of stochastic gradient descent (SGD) on smooth and strongly-convex finite-sum optimization problems. In contrast to the majority of existing theoretical works, which assume that individual functions are sampled with replacement, we focus here on popular but poorly-understood heuristics, which i…

In recent work, Cheu et al. (Eurocrypt 2019) proposed a protocol for $n$-party real summation in the shuffle model of differential privacy with $O_{ε, δ}(1)$ error and $Θ(ε\sqrt{n})$ one-bit messages per party. In contrast, every local model protocol for real summation must incur error $Ω(1/\sqrt{n})$, and there exist …

Federated learning promises to make machine learning feasible on distributed, private datasets by implementing gradient descent using secure aggregation methods. The idea is to compute a global weight update without revealing the contributions of individual users. Current practical protocols for secure aggregation work…

Power of network tests degrades when vertices are misaligned.

Sampling without replacement speeds up optimization in minimax problems.

Paper proves privacy guarantees for shuffled and online PNSGD, reducing noise over time.

The paper analyzes the variance of different shuffling methods in stochastic gradient descent.

Proof of injection from double shuffle to Kashiwara-Vergne Lie algebra.

Balls-and-Bins sampling improves DP-SGD privacy and utility.

A novel RNN model with shuffled hidden states.

This paper develops a path-first theory using signatures and jump lifts for self-exiting processes.

ShuffleNet is a state-of-the-art light weight convolutional neural network architecture. Its basic operations include group, channel-wise convolution and channel shuffling. However, channel shuffling is manually designed empirically. Mathematically, shuffling is a multiplication by a permutation matrix. In this paper, …

Random Reshuffling outperforms Stochastic Gradient Descent in smooth convex optimization.

Paper improves privacy bounds for shuffle model using novel numerical techniques.