Tootfinder

Continual Release of Densest Subgraphs: Privacy Amplification & Sublinear Space via Subsampling
Felix Zhou
https://arxiv.org/abs/2510.11640 https://arx…

Continual Release of Densest Subgraphs: Privacy Amplification & Sublinear Space via Subsampling
We study the sublinear space continual release model for edge-differentially private (DP) graph algorithms, with a focus on the densest subgraph problem (DSG) in the insertion-only setting. Our main result is the first continual release DSG algorithm that matches the additive error of the best static DP algorithms and the space complexity of the best non-private streaming algorithms, up to constants. The key idea is a refined use of subsampling that simultaneously achieves privacy amplification…

Subsampling Factorization Machine Annealing
Yusuke Hama, Tadashi Kadowaki
https://arxiv.org/abs/2508.08778 https://arxiv.org/pdf/2508.08778

Subsampling Factorization Machine Annealing
Quantum computing and machine learning are state-of-the-art technologies which have been investigated intensively in both academia and industry. The hybrid technology of these two ingredients is expected to be a powerful tool to solve complex problems in many branches of science and engineering such as combinatorial optimization problems and accelerate the creation of next-generation technologies. In this work, we develop an algorithm to solve a black-box optimization problem by improving Facto…

A subsampling approach for large data sets when the Generalised Linear Model is potentially misspecified
Amalan Mahendran, Helen Thompson, James M. McGree
https://arxiv.org/abs/2510.05902

A subsampling approach for large data sets when the Generalised Linear Model is potentially misspecified
Subsampling is a computationally efficient and scalable method to draw inference in large data settings based on a subset of the data rather than needing to consider the whole dataset. When employing subsampling techniques, a crucial consideration is how to select an informative subset based on the queries posed by the data analyst. A recently proposed method for this purpose involves randomly selecting samples from the large dataset based on subsampling probabilities. However, a major drawback…

SubApSnap: Solving parameter-dependent linear systems with a snapshot and subsampling
Eleanor Jones, Yuji Nakatsukasa
https://arxiv.org/abs/2510.04825 https://

SubApSnap: Solving parameter-dependent linear systems with a snapshot and subsampling
A growing number of problems in computational mathematics can be reduced to the solution of many linear systems that are related, often depending smoothly or slowly on a parameter $p$, that is, $A(p)x(p)=b(p)$. We introduce an efficient algorithm for solving such parameter-dependent linear systems for many values of $p$. The algorithm, which we call SubApSnap (for \emph{Sub}sampled $A(p)$ times \emph{Snap}shot), is based on combining ideas from model order reduction and randomised linear algebr…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/9]:
- Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forec...
Schuchardt, Dalirrooyfard, Guzelkabaagac, Schneider, Nevmyvaka, G\"unnemann

Statistical Inference for Differentially Private Stochastic Gradient Descent
Xintao Xia, Linjun Zhang, Zhanrui Cai
https://arxiv.org/abs/2507.20560 https://

Statistical Inference for Differentially Private Stochastic Gradient Descent
Privacy preservation in machine learning, particularly through Differentially Private Stochastic Gradient Descent (DP-SGD), is critical for sensitive data analysis. However, existing statistical inference methods for SGD predominantly focus on cyclic subsampling, while DP-SGD requires randomized subsampling. This paper first bridges this gap by establishing the asymptotic properties of SGD under the randomized rule and extending these results to DP-SGD. For the output of DP-SGD, we show that th…

Generating Difficult-to-Translate Texts
Vil\'em Zouhar, Wenda Xu, Parker Riley, Juraj Juraska, Mara Finkelstein, Markus Freitag, Dan Deutsch
https://arxiv.org/abs/2509.26592

Generating Difficult-to-Translate Texts
Machine translation benchmarks sourced from the real world are quickly obsoleted, due to most examples being easy for state-of-the-art translation models. This limits the benchmark's ability to distinguish which model is better or to reveal models' weaknesses. Current methods for creating difficult test cases, such as subsampling or from-scratch synthesis, either fall short of identifying difficult examples or suffer from a lack of diversity and naturalness. Inspired by the iterative process of…

Optional subsampling for generalized estimating equations in growing-dimensional longitudinal Data
Chunjing Li, Jiahui Zhang, Xiaohui Yuan
https://arxiv.org/abs/2508.20803 https…

Optional subsampling for generalized estimating equations in growing-dimensional longitudinal Data
As a powerful tool for longitudinal data analysis, the generalized estimating equations have been widely studied in the academic community. However, in large-scale settings, this approach faces pronounced computational and storage challenges. In this paper, we propose an optimal Poisson subsampling algorithm for generalized estimating equations in large-scale longitudinal data with diverging covariate dimension, and establish the asymptotic properties of the resulting estimator. We further deri…

Robust Data Fusion via Subsampling
Jing Wang, HaiYing Wang, Kun Chen
https://arxiv.org/abs/2508.12048 https://arxiv.org/pdf/2508.12048

Robust Data Fusion via Subsampling
Data fusion and transfer learning are rapidly growing fields that enhance model performance for a target population by leveraging other related data sources or tasks. The challenges lie in the various potential heterogeneities between the target and external data, as well as various practical concerns that prevent a naïve data integration. We consider a realistic scenario where the target data is limited in size while the external data is large but contaminated with outliers; such data contami…

Core-elements Subsampling for Alternating Least Squares
Dunyao Xue, Mengyu Li, Cheng Meng, Jingyi Zhang
https://arxiv.org/abs/2509.18024 https://arxiv.org/…

Core-elements Subsampling for Alternating Least Squares
In this paper, we propose a novel element-wise subset selection method for the alternating least squares (ALS) algorithm, focusing on low-rank matrix factorization involving matrices with missing values, as commonly encountered in recommender systems. While ALS is widely used for providing personalized recommendations based on user-item interaction data, its high computational cost, stemming from repeated regression operations, poses significant challenges for large-scale datasets. To enhance t…

Sub-sampled Trust-Region Methods with Deterministic Worst-Case Complexity Guarantees
Max L. N. Goncalves, Geovani N. Grapiglia
https://arxiv.org/abs/2507.17556 https://

Sub-sampled Trust-Region Methods with Deterministic Worst-Case Complexity Guarantees
In this paper, we develop and analyze sub-sampled trust-region methods for solving finite-sum optimization problems. These methods employ subsampling strategies to approximate the gradient and Hessian of the objective function, significantly reducing the overall computational cost. We propose a novel adaptive procedure for deterministically adjusting the sample size used for gradient (or gradient and Hessian) approximations. Furthermore, we establish worst-case iteration complexity bounds for o…

Optimizing Prognostic Biomarker Discovery in Pancreatic Cancer Through Hybrid Ensemble Feature Selection and Multi-Omics Data
John Zobolas, Anne-Marie George, Alberto L\'opez, Sebastian Fischer, Marc Becker, Tero Aittokallio
https://arxiv.org/abs/2509.02648

Optimizing Prognostic Biomarker Discovery in Pancreatic Cancer Through Hybrid Ensemble Feature Selection and Multi-Omics Data
Prediction of patient survival using high-dimensional multi-omics data requires systematic feature selection methods that ensure predictive performance, sparsity, and reliability for prognostic biomarker discovery. We developed a hybrid ensemble feature selection (hEFS) approach that combines data subsampling with multiple prognostic models, integrating both embedded and wrapper-based strategies for survival prediction. Omics features are ranked using a voting-theory-inspired aggregation mechan…

Error Propagation in Dynamic Programming: From Stochastic Control to Option Pricing
Andrea Della Vecchia, Damir Filipovi\'c
https://arxiv.org/abs/2509.20239 https://

Error Propagation in Dynamic Programming: From Stochastic Control to Option Pricing
This paper investigates theoretical and methodological foundations for stochastic optimal control (SOC) in discrete time. We start formulating the control problem in a general dynamic programming framework, introducing the mathematical structure needed for a detailed convergence analysis. The associate value function is estimated through a sequence of approximations combining nonparametric regression methods and Monte Carlo subsampling. The regression step is performed within reproducing kernel…

Crosslisted article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
- Optional subsampling for generalized estimating equations in growing-dimensional longitudinal Data
Chunjing Li, Jiahui Zhang, Xiaohui Yuan

Piecewise Deterministic Sampling for Constrained Distributions
Jo\"el Tatang Demano, Paul Dobson, Konstantinos Zygalakis
https://arxiv.org/abs/2508.05462 https://

Piecewise Deterministic Sampling for Constrained Distributions
In this paper, we propose a novel class of Piecewise Deterministic Markov Processes (PDMP) that are designed to sample from constrained probability distributions $π$ supported on a convex set $\mathcal{M}$. This class of PDMPs adapts the concept of a mirror map from convex optimisation to address sampling problems. Such samplers provides unbiased algorithms that respect the constraints and, moreover, allow for exact subsampling. We demonstrate the advantages of these algorithms on a range of c…