As climate change intensifies environmental and socio-economic vulnerabilities across Africa, there is a growing need for innovative, scalable solutions to support climate resilience. This study critically examines the role of Artificial Intelligence (AI) in enhancing climate adaptation and mitigation efforts on the co...
Ogunade Josiah Ayodele· WORLD JOURNAL OF INNOVATION...· 1 citation
With the growing demands for efficient and scalable food analysis, agriculture, and healthcare applications, the need for improved data acquisition and processing techniques has become increasingly significant. Near Infrared Spectroscopy has emerged as a powerful tool for non-invasive analysis in these domains, providi...
Robert Alexander Williamson· Research Portal (Queen's Uni...· 0 citations
We propose an active learning algorithm for linear system identification with optimal centered noise excitation. Notably, our algorithm, based on ordinary least squares and semidefinite programming, attains the minimal sample complexity while allowing for efficient computation of an estimate of a system matrix. More sp...
An optimal transport approach to alternating minimization called BalLOT is introduced, and it is shown that it delivers a fast and effective solution to the fundamental problem of balanced $k$-means clustering.
Wen-Yan Luo, D. Mixon· arXiv.org· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
We develop a flexible feature selection framework based on deep neural networks that approximately controls the false discovery rate (FDR), a measure of Type-I error. The method applies to architectures whose first layer is fully connected. From the second layer onward, it accommodates multilayer perceptrons (MLPs) of...
We study a structured class of Markov Decision Processes, known as Exo-MDPs, in which the state space is partitioned into exogenous and endogenous components. Exogenous states evolve stochastically, independent of the agent's actions, while endogenous states evolve deterministically based on both state components and a...
Jia Wan, Sean R. Sinclair, Devavrat Shah et al.· 0 citations
Orthogonal and Stiefel layers give neural weights exact spectral control, but they also impose a strong modeling constraint: all represented singular values are fixed at one. Many settings that benefit from an orthonormal basis still need direction-dependent attenuation or amplification. We introduce ManifoldFlow, a mi...
Causal representation learning for time series has developed strong identifiability results in discrete-time latent causal models, but identifiability in continuous-time latent stochastic differential equation (SDE) models remains largely open. We address this gap using environment-induced shifts in diffusion covarianc...
Yuanyuan Wang, Wenjie Wang, Haoxuan Li et al.· 0 citations
Neural Controlled Differential Equations (NCDE) provide a powerful continuous-time framework for forecasting time series, but standard graph-based extensions typically learn spatial structure purely from data, even in settings where a directed graph structure is known a priori. We introduce Informed Neural controlled D...
Michael Detzel, Gabriel Nobis, Kristiyan Blagov et al.· 0 citations
Implicit-process priors define distributions over functions through flexible generative mechanisms, making them attractive for Bayesian function-space modelling. However, performing posterior inference with such priors is challenging because their induced function-space distributions are typically not available in clos...
Luis A. Ortega, Andr\'es R. Masegosa, Thomas D. Nielsen· 0 citations
This work introduces CASCADE (Calibrated Adaptive Scaling via Conformal And Distributional Estimation), a novel conformal prediction framework that propagates epistemic uncertainty from a screening classifier to adapt downstream predictions and achieves continuous risk adaptation.
Ricardo Diaz-Rincon, Mu-Xuan Liang, A. Ramirez-Zamora et al.· arXiv.org· 0 citations
We study downlink beam and rate adaptation in a multi-user mmWave MISO system where multiple base stations (BSs), each using analog beamforming from finite codebooks, serve multiple single-antenna user equipments (UEs) with a unique beam per UE and discrete data transmission rates. BSs learn about transmission success...
Emre \"Ozy{\i}ld{\i}r{\i}m, Bar{\i}\c{s} Yayc{\i}, Umut Eren Akturk et al.· 0 citations
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
