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Tag: convex

  • Decentralized Online Convex Optimization with Unknown Feedback Delays

    Decentralized Online Convex Optimization with Unknown Feedback Delays arXiv:2601.07901v1 Announce Type: new Abstract: Decentralized online convex optimization (D-OCO), where multiple agents within a network collaboratively learn optimal decisions in real-time, arises naturally in applications such as federated learning, sensor networks, and multi-agent control. In this paper, we study D-OCO under unknown, time-and agent-varying feedback delays.…

  • Convex Clustering Redefined: Robust Learning with the Median of Means Estimator

    Convex Clustering Redefined: Robust Learning with the Median of Means Estimator arXiv:2511.14784v1 Announce Type: new Abstract: Clustering approaches that utilize convex loss functions have recently attracted growing interest in the formation of compact data clusters. Although classical methods like k-means and its wide family of variants are still widely used, all of them require the…

  • A New Framework for Convex Clustering in Kernel Spaces: Finite Sample Bounds, Consistency and Performance Insights

    A New Framework for Convex Clustering in Kernel Spaces: Finite Sample Bounds, Consistency and Performance Insights arXiv:2511.05159v1 Announce Type: new Abstract: Convex clustering is a well-regarded clustering method, resembling the similar centroid-based approach of Lloyd’s $k$-means, without requiring a predefined cluster count. It starts with each data point as its centroid and iteratively merges them.…

  • Convex Regression with a Penalty

    Convex Regression with a Penalty arXiv:2509.19788v1 Announce Type: new Abstract: A common way to estimate an unknown convex regression function $f_0: Omega subset mathbb{R}^d rightarrow mathbb{R}$ from a set of $n$ noisy observations is to fit a convex function that minimizes the sum of squared errors. However, this estimator is known for its tendency to…

  • Differentiable neural network representation of multi-well, locally-convex potentials

    Differentiable neural network representation of multi-well, locally-convex potentials arXiv:2506.17242v1 Announce Type: new Abstract: Multi-well potentials are ubiquitous in science, modeling phenomena such as phase transitions, dynamic instabilities, and multimodal behavior across physics, chemistry, and biology. In contrast to non-smooth minimum-of-mixture representations, we propose a differentiable and convex formulation based on a log-sum-exponential (LSE) mixture of…

  • Multi-Attribute Graph Estimation with Sparse-Group Non-Convex Penalties

    Multi-Attribute Graph Estimation with Sparse-Group Non-Convex Penalties arXiv:2505.11984v1 Announce Type: new Abstract: We consider the problem of inferring the conditional independence graph (CIG) of high-dimensional Gaussian vectors from multi-attribute data. Most existing methods for graph estimation are based on single-attribute models where one associates a scalar random variable with each node. In multi-attribute graphical models,…

  • Sample and Map from a Single Convex Potential: Generation using Conjugate Moment Measures

    Sample and Map from a Single Convex Potential: Generation using Conjugate Moment Measures arXiv:2503.10576v1 Announce Type: new Abstract: A common approach to generative modeling is to split model-fitting into two blocks: define first how to sample noise (e.g. Gaussian) and choose next what to do with it (e.g. using a single map or flows). We…

  • Asymptotics of Non-Convex Generalized Linear Models in High-Dimensions: A proof of the replica formula

    Asymptotics of Non-Convex Generalized Linear Models in High-Dimensions: A proof of the replica formula arXiv:2502.20003v1 Announce Type: new Abstract: The analytic characterization of the high-dimensional behavior of optimization for Generalized Linear Models (GLMs) with Gaussian data has been a central focus in statistics and probability in recent years. While convex cases, such as the LASSO,…