9 accepted papers
Higher-order ODE solvers have become a standard tool for accelerating diffusion probabilistic model (DPM) sampling, motivating the widespread view that first-order methods are inherently slower and that increasing discretization order is the primary path to faster generation. This paper challenges t…
Score-based diffusion models have achieved remarkable empirical performance in the field of machine learning and artificial intelligence for their ability to generate high-quality new data instances from complex distributions. Improving our understanding of diffusion models, including mainly converg…
Diffusion models have emerged as a powerful framework for generative modeling, with guidance techniques playing a crucial role in enhancing sample quality. Despite their empirical success, a comprehensive theoretical understanding of the guidance effect remains limited. Existing studies only focus o…
The thriving field of multi-agent reinforcement learning (MARL) studies how a group of interacting agents make decisions autonomously in a shared dynamic environment. Existing theoretical studies in this area suffer from at least two of the following obstacles: memory inefficiency, the heavy depende…
We consider the problem of distributed principal component analysis (PCA) where the data samples are dispersed across different agents. Despite the rich literature on this problem under various specific settings, there is still a lack of efficient algorithms that are amenable to decentralized and as…
Subspace clustering has attracted much attention due to its successful application on many data mining and computer vision tasks. However, most subspace clustering algorithms suffer from the scalability and the curse of dimensionality problems. When the volume or the dimension of the datasets become…
Detecting subspace signals is an important problem in radar and sonar signal processing, hyperspectral image processing, wireless communication, and other fields. Among these problems, a typical scenario is that one needs to detect a signal lying in a given target subspace, contaminated by interfere…
Hyperspectral images (HSIs) clustering problem is a challenge and valuable task due to its inherent complexity and abundant spectral information. Sparse subspace clustering (SSC) and SSC-based methods are widely used in this problem and demonstrate excellent performance. However, considering that HS…
Phase retrieval has been an attractive problem, and many algorithms have been proposed. Randomized Kaczmarz method is a fast iterative method with good performance in both convergence rate and computational cost with theoretical analysis. However, they all assume that the iteratively updated variabl…