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Replaced 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

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

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…

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…

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…

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

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…