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Related papers: Basis Pursuit Denoise with Nonsmooth Constraints

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In this paper, we will outline a novel data-driven method for estimating functions in a multivariate nonparametric regression model based on an adaptive knot selection for B-splines. The underlying idea of our approach for selecting knots…

Methodology · Statistics 2024-01-26 Mary E. Savino , Céline Lévy-Leduc

Data-parallel (DP) training with synchronous all-reduce is a dominant paradigm for full-parameter fine-tuning of large language models (LLMs). While parameter synchronization guarantees numerical equivalence of model weights after each…

Machine Learning · Computer Science 2026-02-25 Hong Li , Zhen Zhou , Honggang Zhang , Yuping Luo , Xinyue Wang , Han Gong , Zhiyuan Liu

We consider the problem of estimating a deterministic sparse vector x from underdetermined measurements Ax+w, where w represents white Gaussian noise and A is a given deterministic dictionary. We analyze the performance of three sparse…

Statistics Theory · Mathematics 2015-05-13 Zvika Ben-Haim , Yonina C. Eldar , Michael Elad

$L_0$-smoothness, which has been pivotal to advancing decentralized optimization theory, is often fairly restrictive for modern tasks like deep learning. The recent advent of relaxed $(L_0,L_1)$-smoothness condition enables improved…

Optimization and Control · Mathematics 2025-08-13 Zhanhong Jiang , Aditya Balu , Soumik Sarkar

We developed a Nonlinear Level-set Learning (NLL) method for dimensionality reduction in high-dimensional function approximation with small data. This work is motivated by a variety of design tasks in real-world engineering applications,…

Functional Analysis · Mathematics 2019-06-20 Guannan Zhang , Jiaxin Zhang , Jacob Hinkle

Surface-based data is commonly observed in diverse practical applications spanning various fields. In this paper, we introduce a novel nonparametric method to discover the underlying signals from data distributed on complex surface-based…

Methodology · Statistics 2024-03-12 Zhiling Gu , Shan Yu , Guannan Wang , Ming-Jun Lai , Li Wang

Stochastic optimization lies at the core of most statistical learning models. The recent great development of stochastic algorithmic tools focused significantly onto proximal gradient iterations, in order to find an efficient approach for…

Machine Learning · Computer Science 2020-03-31 Andrei Patrascu , Ciprian Paduraru , Paul Irofti

Motivated by distributed machine learning settings such as Federated Learning, we consider the problem of fitting a statistical model across a distributed collection of heterogeneous data sets whose similarity structure is encoded by a…

Statistics Theory · Mathematics 2021-11-30 Dominic Richards , Sahand N. Negahban , Patrick Rebeschini

Recently, various algorithms for data-driven simulation and control have been proposed based on the Willems' fundamental lemma. However, when collected data are noisy, these methods lead to ill-conditioned data-driven model structures. In…

Systems and Control · Electrical Eng. & Systems 2023-03-20 Mingzhou Yin , Andrea Iannelli , Roy S. Smith

This paper proposes an adaptive near-field beam training method to enhance performance in multi-user and multipath environments. The approach identifies multiple strongest beams through beam sweeping and linearly combines their received…

Signal Processing · Electrical Eng. & Systems 2025-05-14 Zijun Wang , Rama Kiran , Jinesh Nair , Chien-Hua Chen , Tzu-Han Chou , Shawn Tsai , Rui Zhang

In this work, we propose an optimization framework for estimating a sparse robust one-dimensional subspace. Our objective is to minimize both the representation error and the penalty, in terms of the l1-norm criterion. Given that the…

Machine Learning · Statistics 2024-03-07 Xiao Ling , Paul Brooks

The denoising process of diffusion models can be interpreted as an approximate projection of noisy samples onto the data manifold. Moreover, the noise level in these samples approximates their distance to the underlying manifold. Building…

Computer Vision and Pattern Recognition · Computer Science 2025-06-03 Abulikemu Abuduweili , Chenyang Yuan , Changliu Liu , Frank Permenter

Likelihood-based deep generative models have recently been shown to exhibit pathological behaviour under the manifold hypothesis as a consequence of using high-dimensional densities to model data with low-dimensional structure. In this…

Sampling from unnormalized densities is analogous to the generative modeling problem, but the target distribution is defined by a known energy function instead of data samples. Because evaluating the energy function is often costly, a…

Machine Learning · Computer Science 2026-05-06 Aaron Havens , Brian Karrer , Neta Shaul

Burst denoising methods are crucial for enhancing images captured on handheld devices, but they often struggle with large motion or suffer from prohibitive computational costs. In this paper, we propose DenoiseGS, the first framework to…

Computer Vision and Pattern Recognition · Computer Science 2025-12-02 Yongsen Cheng , Yuanhao Cai , Yulun Zhang

This paper proposes a new parametric level set method for topology optimization based on Deep Neural Network (DNN). In this method, the fully connected deep neural network is incorporated into the conventional level set methods to construct…

Optimization and Control · Mathematics 2021-01-12 Hao Deng , Albert C. To

In this paper we consider Basis Pursuit De-Noising (BPDN) problems in which the sparse original signal is drawn from a finite alphabet. To solve this problem we propose an iterative message passing algorithm, which capitalises not only on…

Information Theory · Computer Science 2016-11-17 Andreas Muller , Dino Sejdinovic , Robert Piechocki

This paper studies the immersion and invariance (I&I) adaptive tracking problem for a class of nonlinear systems with nonlinear parameterization in the ISS framework. Under some mild assumptions, a novel I&I adaptive control algorithm is…

Optimization and Control · Mathematics 2021-03-01 Lei Wang , Christopher M. Kellett

We present a novel regularization approach to train neural networks that enjoys better generalization and test error than standard stochastic gradient descent. Our approach is based on the principles of cross-validation, where a validation…

Computer Vision and Pattern Recognition · Computer Science 2018-09-06 Simon Jenni , Paolo Favaro

This paper focuses on minimizing a smooth function combined with a nonsmooth regularization term on a compact Riemannian submanifold embedded in the Euclidean space under a decentralized setting. Typically, there are two types of approaches…

Optimization and Control · Mathematics 2025-07-16 Lei Wang , Le Bao , Xin Liu
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