This paper proposes the RFFBCGA algorithm, a random Fourier feature based bias-compensated filter that mitigates input noise interference and enhances robustness in nonlinear adaptive filtering.

This paper proposes an adaptive momentum term for the ASSS-MGDFxLMS algorithm in distributed multichannel active noise control systems to accelerate convergence while maintaining robustness under comm…

A modular differentiable digital signal processing framework is presented for closed-loop adaptive room equalization, providing more stable adaptation than classical methods.

The paper analyzes a new class of asynchronous adaptive first-order optimization methods and proves their stochastic convergence rate is O(1/sqrt{t}) for non-convex functions.

This paper investigates the necessity of interaction for order-optimal 1-bit mean estimation in nonparametric finite-moment classes.

This paper presents the topology-independent distributed multichannel Wiener filter (TI-dMWF) algorithm for distributed node-specific signal estimation in wireless acoustic sensor networks, enabling o…

This paper analyzes the statistical and computational limits of learning bounded linear operators between Sobolev spaces from noisy data, and constructs a finite-resolution blockwise least-squares est…

Adaptive data selection significantly improves wearable prediction performance, particularly for individuals with poor baseline health metrics, suggesting that selective data sampling should be tailor…

This paper theoretically justifies the strong performance of linear recurrent neural networks as memory units in partially observable reinforcement learning by constructing specific linear filters tha…

TailLoR is a new parameter-efficient finetuning method that uses the singular bases of pre-trained weights to learn low-rank updates, specifically penalizing updates along dominant directions to impro…

The paper addresses the failure of fixed-price inference in resource-constrained pricing controllers by developing a target-aware controller that tracks local densities and provides certified, shrinki…

This paper introduces Curvature-Weighted Gradient Diversity (CWGD), a geometry-aware measure for optimization noise that reduces the asymptotic optimization error floor by up to a factor of two compar…

The Autonomous Rectified Flow framework is proposed to improve generative speech enhancement by eliminating explicit time-step conditioning and inferring denoising directions from spatial relationship…

This paper establishes a direct connection between Approximate Message Passing (AMP) and the Convex Gaussian Min-max Theorem (CGMT) for regularized linear regression and M-estimation.

This paper introduces a mechanistic neuronal network model for multilayer learning, offering biological insights and an alternative to backpropagation.