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Related papers: L-SR1: Learned Symmetric-Rank-One Preconditioning

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This paper presents an efficient algorithm for the approximation of the rank-one convex hull in the context of nonlinear solid mechanics. It is based on hierarchical rank-one sequences and simultaneously provides first and second derivative…

Computational Engineering, Finance, and Science · Computer Science 2024-05-28 Maximilian Köhler , Timo Neumeier , Malte. A. Peter , Daniel Peterseim , Daniel Balzani

Reinforcement learning (RL) is a classical tool to solve network control or policy optimization problems in unknown environments. The original Q-learning suffers from performance and complexity challenges across very large networks. Herein,…

Machine Learning · Computer Science 2024-09-02 Talha Bozkus , Urbashi Mitra

Medical report generation aims to automatically produce radiology-style reports from medical images, supporting efficient and accurate clinical decision-making.However, existing approaches predominately rely on token-level likelihood…

Computation and Language · Computer Science 2026-03-30 Pengyu Wang , Shuchang Ye , Usman Naseem , Jinman Kim

Reinforcement learning (RL) has emerged as an effective post-training paradigm for enhancing the reasoning capabilities of multimodal large language model (MLLM). However, current RL pipelines often suffer from training inefficiencies…

Machine Learning · Computer Science 2026-03-04 Linghao Zhu , Yiran Guan , Dingkang Liang , Jianzhong Ju , Zhenbo Luo , Bin Qin , Jian Luan , Yuliang Liu , Xiang Bai

Despite their high accuracy, complex neural networks demand significant computational resources, posing challenges for deployment on resource constrained devices such as mobile phones and embedded systems. Compression algorithms have been…

Machine Learning · Computer Science 2025-09-23 Ali Aghababaei-Harandi , Massih-Reza Amini

The recent success of supervised learning methods on ever larger offline datasets has spurred interest in the reinforcement learning (RL) field to investigate whether the same paradigms can be translated to RL algorithms. This research…

Machine Learning · Computer Science 2021-02-12 Mengjiao Yang , Ofir Nachum

Learning-augmented algorithms have emerged as a powerful paradigm to surpass traditional worst-case lower bounds by integrating potentially noisy predictions. While this framework has seen success in online scheduling, existing work…

Machine Learning · Computer Science 2026-05-25 Mugen Blue , Sungjin Im , Alexander Lindermayr

In this paper, we consider two formulations for Linear Matrix Inequalities (LMIs) under Slater type constraint qualification assumption, namely, SDP smooth and non-smooth formulations. We also propose two first-order linearly convergent…

Optimization and Control · Mathematics 2013-09-10 Cong D. Dang , Guanghui Lan

Creating impact in real-world settings requires artificial intelligence techniques to span the full pipeline from data, to predictive models, to decisions. These components are typically approached separately: a machine learning model is…

Machine Learning · Computer Science 2018-11-22 Bryan Wilder , Bistra Dilkina , Milind Tambe

Learning to rank (LTR) plays a crucial role in various Information Retrieval (IR) tasks. Although supervised LTR methods based on fine-grained relevance labels (e.g., document-level annotations) have achieved significant success, their…

Information Retrieval · Computer Science 2025-08-21 Yiteng Tu , Zhichao Xu , Tao Yang , Weihang Su , Yujia Zhou , Yiqun Liu , Fen Lin , Qin Liu , Qingyao Ai

Approaches for compressing large-language models using low-rank decomposition have made strides, particularly with the introduction of activation and loss-aware SVD, which improves the trade-off between decomposition rank and downstream…

Machine Learning · Computer Science 2025-12-17 Sidhant Sundrani , Francesco Tudisco , Pasquale Minervini

Many previous proposals for adversarial training of deep neural nets have included di- rectly modifying the gradient, training on a mix of original and adversarial examples, using contractive penalties, and approximately optimizing…

Machine Learning · Computer Science 2016-08-01 Alexander G. Ororbia , C. Lee Giles , Daniel Kifer

Efficient numerical optimization methods can improve performance and reduce the environmental impact of computing in many applications. This work presents a proof-of-concept study combining primitive state representations and…

Machine Learning · Computer Science 2025-01-30 R. Sala

Semi-supervised learning is a promising way to reduce the annotation cost for text-classification. Combining with pre-trained language models (PLMs), e.g., BERT, recent semi-supervised learning methods achieved impressive performance. In…

Computation and Language · Computer Science 2022-05-23 Hai-Ming Xu , Lingqiao Liu , Ehsan Abbasnejad

Pre-trained representation is one of the key elements in the success of modern deep learning. However, existing works on continual learning methods have mostly focused on learning models incrementally from scratch. In this paper, we explore…

Machine Learning · Computer Science 2022-08-18 Hyounguk Shon , Janghyeon Lee , Seung Hwan Kim , Junmo Kim

In the field of large language model (LLM) compression, singular value decomposition (SVD) is a widely studied and adopted low-rank decomposition technique. Since SVD operates exclusively on linear modules, and these modules in LLMs are…

Machine Learning · Computer Science 2025-10-23 Lin Xv , Jingsheng Gao , Xian Gao , Ting Liu , Yuzhuo Fu

Bilevel optimization (BO) is useful for solving a variety of important machine learning problems including but not limited to hyperparameter optimization, meta-learning, continual learning, and reinforcement learning. Conventional BO…

Machine Learning · Computer Science 2022-09-20 Mao Ye , Bo Liu , Stephen Wright , Peter Stone , Qiang Liu

The linear Support Vector Machine (SVM) is a classic classification technique in machine learning. Motivated by applications in modern high dimensional statistics, we consider penalized SVM problems involving the minimization of a…

Machine Learning · Statistics 2021-08-31 Antoine Dedieu , Rahul Mazumder , Haoyue Wang

The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set. Embedding based approaches make training and…

Machine Learning · Computer Science 2015-07-13 Kush Bhatia , Himanshu Jain , Purushottam Kar , Prateek Jain , Manik Varma

We consider several classes of highly important semidefinite optimization problems that involve both a convex objective function (smooth or nonsmooth) and additional linear or nonlinear smooth and convex constraints, which are ubiquitous in…

Optimization and Control · Mathematics 2025-04-08 Dan Garber , Atara Kaplan