Tootfinder

Replaced article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[1/1]:
- A Mini-Batch Quasi-Newton Proximal Method for Constrained Total-Variation Nonlinear Image Reconst...
Tao Hong, Thanh-an Pham, Irad Yavneh, Michael Unser

Online Quantum State Tomography via Stochastic Gradient Descent
Jian-Feng Cai, Yuling Jiao, Yinan Li, Xiliang Lu, Jerry Zhijian Yang, Juntao You
https://arxiv.org/abs/2507.07601

Online Quantum State Tomography via Stochastic Gradient Descent
We initiate the study of online quantum state tomography (QST), where the matrix representation of an unknown quantum state is reconstructed by sequentially performing a batch of measurements and updating the state estimate using only the measurement statistics from the current round. Motivated by recent advances in non-convex optimization algorithms for solving low-rank QST, we propose non-convex mini-batch stochastic gradient descent (SGD) algorithms to tackle online QST, which leverage the l…

Boosting Data Utilization for Multilingual Dense Retrieval
Chao Huang, Fengran Mo, Yufeng Chen, Changhao Guan, Zhenrui Yue, Xinyu Wang, Jinan Xu, Kaiyu Huang
https://arxiv.org/abs/2509.09459

Boosting Data Utilization for Multilingual Dense Retrieval
Multilingual dense retrieval aims to retrieve relevant documents across different languages based on a unified retriever model. The challenge lies in aligning representations of different languages in a shared vector space. The common practice is to fine-tune the dense retriever via contrastive learning, whose effectiveness highly relies on the quality of the negative sample and the efficacy of mini-batch data. Different from the existing studies that focus on developing sophisticated model arc…

Mini-Batch Robustness Verification of Deep Neural Networks
Saar Tzour-Shaday, Dana Drachsler Cohen
https://arxiv.org/abs/2508.15454 https://arxiv.org/pdf/2…

Mini-Batch Robustness Verification of Deep Neural Networks
Neural network image classifiers are ubiquitous in many safety-critical applications. However, they are susceptible to adversarial attacks. To understand their robustness to attacks, many local robustness verifiers have been proposed to analyze $ε$-balls of inputs. Yet, existing verifiers introduce a long analysis time or lose too much precision, making them less effective for a large set of inputs. In this work, we propose a new approach to local robustness: group local robustness verificatio…

Hierarchical Rectified Flow Matching with Mini-Batch Couplings
Yichi Zhang, Yici Yan, Alex Schwing, Zhizhen Zhao
https://arxiv.org/abs/2507.13350 https://

Hierarchical Rectified Flow Matching with Mini-Batch Couplings
Flow matching has emerged as a compelling generative modeling approach that is widely used across domains. To generate data via a flow matching model, an ordinary differential equation (ODE) is numerically solved via forward integration of the modeled velocity field. To better capture the multi-modality that is inherent in typical velocity fields, hierarchical flow matching was recently introduced. It uses a hierarchy of ODEs that are numerically integrated when generating data. This hierarchy …

Stochastic versus Deterministic in Stochastic Gradient Descent
Runze Li, Jintao Xu, Wenxun Xing
https://arxiv.org/abs/2509.02912 https://arxiv.org/pdf/2509…

Stochastic versus Deterministic in Stochastic Gradient Descent
This paper considers the mini-batch stochastic gradient descent (SGD) for a structured minimization problem involving a finite-sum function with its gradient being stochastically approximated, and an independent term with its gradient being deterministically computed. We focus on the stochastic versus deterministic behavior of the mini-batch SGD for this setting. A convergence analysis is provided that captures the different roles of these two parts. Linear convergence of the algorithm to a nei…

From Static to Dynamic: A Streaming RAG Approach to Real-time Knowledge Base
Yuzhou Zhu
https://arxiv.org/abs/2508.05662 https://arxiv.org/pdf/2508.05662…

From Static to Dynamic: A Streaming RAG Approach to Real-time Knowledge Base
Dynamic streams from news feeds, social media, sensor networks, and financial markets challenge static RAG frameworks. Full-scale indices incur high memory costs; periodic rebuilds introduce latency that undermines data freshness; naive sampling sacrifices semantic coverage. We present Streaming RAG, a unified pipeline that combines multi-vector cosine screening, mini-batch clustering, and a counter-based heavy-hitter filter to maintain a compact prototype set. We further prove an approximation…

Crosslisted article(s) found for cs.PL. https://arxiv.org/list/cs.PL/new
[1/1]:
- Mini-Batch Robustness Verification of Deep Neural Networks
Saar Tzour-Shaday, Dana Drachsler Cohen

Fisher-Orthogonal Projection Methods for Natural Gradient Descent with Large Batches
Yishun Lu, Wesley Armour
https://arxiv.org/abs/2508.13898 https://arxi…

Fisher-Orthogonal Projection Methods for Natural Gradient Descent with Large Batches
Modern GPUs are equipped with large amounts of high-bandwidth memory, enabling them to support mini-batch sizes of up to tens of thousands of training samples. However, most existing optimizers struggle to perform effectively at such a large batch size. As batch size increases, gradient noise decreases due to averaging over many samples, limiting the ability of first-order methods to escape sharp or suboptimal minima and reach the global minimum. Meanwhile, second-order methods like the natural…

ASPEN: An Additional Sampling Penalty Method for Finite-Sum Optimization Problems with Nonlinear Equality Constraints
Nata\v{s}a Kreji\'c, Nata\v{s}a Krklec Jerinki\'c, Tijana Ostoji\'c, Nemanja Vu\v{c}i\'cevi\'c
https://arxiv.org/abs/2508.02299

ASPEN: An Additional Sampling Penalty Method for Finite-Sum Optimization Problems with Nonlinear Equality Constraints
We propose a novel algorithm for solving non-convex, nonlinear equality-constrained finite-sum optimization problems. The proposed algorithm incorporates an additional sampling strategy for sample size update into the well-known framework of quadratic penalty methods. Thus, depending on the problem at hand, the resulting method may exhibit a sample size strategy ranging from a mini-batch on one end, to increasing sample size that achieves the full sample eventually, on the other end of the spec…