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Tag: low

  • Low-Degree Method Fails to Predict Robust Subspace Recovery

    Low-Degree Method Fails to Predict Robust Subspace Recovery arXiv:2603.02594v1 Announce Type: new Abstract: The low-degree polynomial framework has been highly successful in predicting computational versus statistical gaps for high-dimensional problems in average-case analysis and machine learning. This success has led to the low-degree conjecture, which posits that this method captures the power and limitations of…

  • Low-Dimensional Adaptation of Rectified Flow: A New Perspective through the Lens of Diffusion and Stochastic Localization

    Low-Dimensional Adaptation of Rectified Flow: A New Perspective through the Lens of Diffusion and Stochastic Localization arXiv:2601.15500v1 Announce Type: new Abstract: In recent years, Rectified flow (RF) has gained considerable popularity largely due to its generation efficiency and state-of-the-art performance. In this paper, we investigate the degree to which RF automatically adapts to the intrinsic…

  • The Hidden Opportunity in AI Workflow Automation with n8n for Low-Tech Companies

    The Hidden Opportunity in AI Workflow Automation with n8n for Low-Tech Companies How to use n8n with multimodal AI and optimisation tools to help companies with low data maturity accelerate their digital transformation. The post The Hidden Opportunity in AI Workflow Automation with n8n for Low-Tech Companies appeared first on Towards Data Science. Samir Saci…

  • Robust low-rank estimation with multiple binary responses using pairwise AUC loss

    Robust low-rank estimation with multiple binary responses using pairwise AUC loss arXiv:2601.08618v1 Announce Type: new Abstract: Multiple binary responses arise in many modern data-analytic problems. Although fitting separate logistic regressions for each response is computationally attractive, it ignores shared structure and can be statistically inefficient, especially in high-dimensional and class-imbalanced regimes. Low-rank models offer a…

  • Simplifying Optimal Transport through Schatten-$p$ Regularization

    Simplifying Optimal Transport through Schatten-$p$ Regularization arXiv:2510.11910v1 Announce Type: new Abstract: We propose a new general framework for recovering low-rank structure in optimal transport using Schatten-$p$ norm regularization. Our approach extends existing methods that promote sparse and interpretable transport maps or plans, while providing a unified and principled family of convex programs that encourage low-dimensional…

  • A Probabilistic Basis for Low-Rank Matrix Learning

    A Probabilistic Basis for Low-Rank Matrix Learning arXiv:2510.05447v1 Announce Type: new Abstract: Low rank inference on matrices is widely conducted by optimizing a cost function augmented with a penalty proportional to the nuclear norm $Vert cdot Vert_*$. However, despite the assortment of computational methods for such problems, there is a surprising lack of understanding of…

  • Low-Rank Adaptation of Evolutionary Deep Neural Networks for Efficient Learning of Time-Dependent PDEs

    Low-Rank Adaptation of Evolutionary Deep Neural Networks for Efficient Learning of Time-Dependent PDEs arXiv:2509.16395v1 Announce Type: new Abstract: We study the Evolutionary Deep Neural Network (EDNN) framework for accelerating numerical solvers of time-dependent partial differential equations (PDEs). We introduce a Low-Rank Evolutionary Deep Neural Network (LR-EDNN), which constrains parameter evolution to a low-rank subspace, thereby…

  • Low-degree lower bounds via almost orthonormal bases

    Low-degree lower bounds via almost orthonormal bases arXiv:2509.09353v1 Announce Type: new Abstract: Low-degree polynomials have emerged as a powerful paradigm for providing evidence of statistical-computational gaps across a variety of high-dimensional statistical models [Wein25]. For detection problems — where the goal is to test a planted distribution $mathbb{P}’$ against a null distribution $mathbb{P}$ with independent…

  • Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective

    Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective arXiv:2505.14808v1 Announce Type: new Abstract: This work aims to demystify the out-of-distribution (OOD) capabilities of in-context learning (ICL) by studying linear regression tasks parameterized with low-rank covariance matrices. With such a parameterization, we can model distribution shifts as a varying angle between the subspace of the…

  • Algorithmic contiguity from low-degree conjecture and applications in correlated random graphs

    Algorithmic contiguity from low-degree conjecture and applications in correlated random graphs arXiv:2502.09832v1 Announce Type: new Abstract: In this paper, assuming a natural strengthening of the low-degree conjecture, we provide evidence of computational hardness for two problems: (1) the (partial) matching recovery problem in the sparse correlated ErdH{o}s-R’enyi graphs $mathcal G(n,q;rho)$ when the edge-density $q=n^{-1+o(1)}$ and…

  • Decentralized Inference for Distributed Geospatial Data Using Low-Rank Models

    Decentralized Inference for Distributed Geospatial Data Using Low-Rank Models arXiv:2502.00309v1 Announce Type: new Abstract: Advancements in information technology have enabled the creation of massive spatial datasets, driving the need for scalable and efficient computational methodologies. While offering viable solutions, centralized frameworks are limited by vulnerabilities such as single-point failures and communication bottlenecks. This paper presents…

  • Guaranteed Nonconvex Low-Rank Tensor Estimation via Scaled Gradient Descent

    Guaranteed Nonconvex Low-Rank Tensor Estimation via Scaled Gradient Descent arXiv:2501.01696v1 Announce Type: new Abstract: Tensors, which give a faithful and effective representation to deliver the intrinsic structure of multi-dimensional data, play a crucial role in an increasing number of signal processing and machine learning problems. However, tensor data are often accompanied by arbitrary signal corruptions,…

  • Low-Rank Correction for Quantized LLMs

    Low-Rank Correction for Quantized LLMs arXiv:2412.07902v1 Announce Type: new Abstract: We consider the problem of model compression for Large Language Models (LLMs) at post-training time, where the task is to compress a well-trained model using only a small set of calibration input data. In this work, we introduce a new low-rank approach to correct for…

  • Training-Free Bayesianization for Low-Rank Adapters of Large Language Models

    Training-Free Bayesianization for Low-Rank Adapters of Large Language Models arXiv:2412.05723v1 Announce Type: new Abstract: Estimating the uncertainty of responses of Large Language Models~(LLMs) remains a critical challenge. While recent Bayesian methods have demonstrated effectiveness in quantifying uncertainty through low-rank weight updates, they typically require complex fine-tuning or post-training procedures. In this paper, we propose Training-Free…