arXiv research

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

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.

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 …

New shuffling methods improve convergence without Lipschitz smoothness.

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.

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

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 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…

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.

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

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…

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.

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

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

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-…

A novel RNN model with shuffled hidden states.

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.

New algorithms for private generalized linear contextual bandits.

This paper improves privacy bounds for DP algorithms using $f$-DP.

The paper explores privacy-preserving methods for counting unique elements in distributed settings.

OmniMatch algorithm perfectly matches graphs without edge correlation.

Study phase transitions in shuffled regression problems.

Paper tackles sparse recovery with shuffled labels, establishing statistical and computational limits.

DS-Sync improves distributed DNN training efficiency by 94% with minimal accuracy loss.

While many multiple graph inference methodologies operate under the implicit assumption that an explicit vertex correspondence is known across the vertex sets of the graphs, in practice these correspondences may only be partially or errorfully known. Herein, we provide an information theoretic foundation for understand…

Ranking metrics are a family of metrics largely used to evaluate recommender systems. However they typically suffer from the fact the reward is affected by the order in which recommended items are displayed to the user. A classical way to overcome this position bias is to uniformly shuffle a proportion of the recommend…

This study proposes the segmentation procedure of univariate time series based on Fisher's exact test. We show that an adequate change point can be detected as the minimum value of p-value. It is shown that the proposed procedure can detect change points for an artificial time series. We apply the proposed method to fi…