Tootfinder

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…

PCA-Guided Quantile Sampling: Preserving Data Structure in Large-Scale Subsampling
Foo Hui-Mean, Yuan-chin Ivan Chang
https://arxiv.org/abs/2506.18249 http…

PCA-Guided Quantile Sampling: Preserving Data Structure in Large-Scale Subsampling
We introduce Principal Component Analysis guided Quantile Sampling (PCA QS), a novel sampling framework designed to preserve both the statistical and geometric structure of large scale datasets. Unlike conventional PCA, which reduces dimensionality at the cost of interpretability, PCA QS retains the original feature space while using leading principal components solely to guide a quantile based stratification scheme. This principled design ensures that sampling remains representative without di…

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…

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…

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…

Uncertainty-Aware Strategies: A Model-Agnostic Framework for Robust Financial Optimization through Subsampling
Hans Buehler, Blanka Horvath, Yannick Limmer, Thorsten Schmidt
https://arxiv.org/abs/2506.07299

Uncertainty-Aware Strategies: A Model-Agnostic Framework for Robust Financial Optimization through Subsampling
This paper addresses the challenge of model uncertainty in quantitative finance, where decisions in portfolio allocation, derivative pricing, and risk management rely on estimating stochastic models from limited data. In practice, the unavailability of the true probability measure forces reliance on an empirical approximation, and even small misestimations can lead to significant deviations in decision quality. Building on the framework of Klibanoff et al. (2005), we enhance the conventional ob…

Strong Detection Threshold for Correlated Erd\H{o}s-R\'enyi Graphs with Constant Average Degree
Chenxu Feng
https://arxiv.org/abs/2506.12752 https://…

Strong Detection Threshold for Correlated Erdős-Rényi Graphs with Constant Average Degree
Consider a pair of correlated Erdős-Rényi graphs $\mathcal G(n,\tfracλ{n};s)$ that are subsampled from a common parent Erdős-Rényi graph with average degree $λ$ and subsampling probability $s$. We establish a sharp information-theoretic threshold for the detection problem between this model and two independent Erdős-Rényi graphs $\mathcal G(n,\tfracλ{n})$, showing that strong detection is information-theoretically possible if and only if $s>\min\{ \tfrac{1}{\sqrtλ}, \sqrtα \}$ where …

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

FERRET: Private Deep Learning Faster And Better Than DPSGD
David Zagardo
https://arxiv.org/abs/2506.05416 https://arxiv.org/pdf/2506.…

FERRET: Private Deep Learning Faster And Better Than DPSGD
We revisit 1-bit gradient compression through the lens of mutual-information differential privacy (MI-DP). Building on signSGD, we propose FERRET--Fast and Effective Restricted Release for Ethical Training--which transmits at most one sign bit per parameter group with Bernoulli masking. Theory: We prove each fired group leaks at most ln 2 nats; after subsampling with rate s, the total privacy loss of G groups trained for T steps with firing probability p is epsilon = G * T * s * p * ln 2. Thu…

Testing Suffixient Sets
Davide Cenzato, Francisco Olivares, Nicola Prezza
https://arxiv.org/abs/2506.08225 https://arxiv.org/pdf/2506…

Testing Suffixient Sets
Suffixient sets are a novel prefix array (PA) compression technique based on subsampling PA (rather than compressing the entire array like previous techniques used to do): by storing very few entries of PA (in fact, a compressed number of entries), one can prove that pattern matching via binary search is still possible provided that random access is available on the text. In this paper, we tackle the problems of determining whether a given subset of text positions is (1) a suffixient set or (2)…

Scalable Climate Data Analysis: Balancing Petascale Fidelity and Computational Cost
Aashish Panta, Amy Gooch, Giorgio Scorzelli, Michela Taufer, Valerio Pascucci
https://arxiv.org/abs/2507.08006

Scalable Climate Data Analysis: Balancing Petascale Fidelity and Computational Cost
The growing resolution and volume of climate data from remote sensing and simulations pose significant storage, processing, and computational challenges. Traditional compression or subsampling methods often compromise data fidelity, limiting scientific insights. We introduce a scalable ecosystem that integrates hierarchical multiresolution data management, intelligent transmission, and ML-assisted reconstruction to balance accuracy and efficiency. Our approach reduces storage and computational …

Revisiting Randomization in Greedy Model Search
Xin Chen, Jason M. Klusowski, Yan Shuo Tan, Chang Yu
https://arxiv.org/abs/2506.15643 https://

Revisiting Randomization in Greedy Model Search
Combining randomized estimators in an ensemble, such as via random forests, has become a fundamental technique in modern data science, but can be computationally expensive. Furthermore, the mechanism by which this improves predictive performance is poorly understood. We address these issues in the context of sparse linear regression by proposing and analyzing an ensemble of greedy forward selection estimators that are randomized by feature subsampling -- at each iteration, the best feature is s…

Robust Scaling in Human Brain Dynamics Despite Latent Variables and Limited Sampling Distortions
Rub\'en Calvo, Carles Martorell, Adri\'an Roig, Miguel A. Mu\~noz
https://arxiv.org/abs/2506.03640

Robust Scaling in Human Brain Dynamics Despite Latent Variables and Limited Sampling Distortions
The idea that information-processing systems operate near criticality to enhance computational performance is supported by scaling signatures in brain activity. However, external signals raise the question of whether this behavior is intrinsic or input-driven. We show that autocorrelated inputs and temporal resolution influence observed scaling exponents in simple neural models. We also demonstrate analytically that under subsampling, non-critical systems driven by independent autocorrelated si…

Demographic Distribution Matching between real world and virtual phantom population
Dhrubajyoti Ghosh, Fakrul Islam Tushar, Lavsen Dahal, Liesbeth Vancoillie, Kyle J. Lafata, Ehsan Samei, Joseph Y. Lo, Sheng Luo
https://arxiv.org/abs/2507.11511

Demographic Distribution Matching between real world and virtual phantom population
Virtual imaging trials (VITs) offer scalable and cost-effective tools for evaluating imaging systems and protocols. However, their translational impact depends on rigorous comparability between virtual and real-world populations. This study introduces DISTINCT (Distributional Subsampling for Covariate-Targeted Alignment), a statistical framework for selecting demographically aligned subsamples from large clinical datasets to support robust comparisons with virtual cohorts. We applied DISTINCT t…

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…