Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression

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Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression

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Accelerating Spectral Clustering under Fairness Constraints

Self-supervised and few-shot learning for robust bioaerosol monitoring

Distribution Testing with a Confused Collector

OASIS: An integrated optimisation framework for activity scheduling

Infinite-width limit of deep linear neural networks

Spectral Estimators for High-Dimensional Matrix Inference

Stochastic pairwise preference convergence in Bayesian agents

Multi-Scale and Multimodal Species Distribution Modeling

Why computational complexity may set impenetrable barriers for epistemic reductionism

Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring

The Societal and Scientific Importance of Inclusivity, Diversity, and Equity in Machine Learning for Chemistry

Squall: Scalable Real-time Analytics using Efficient, Skew-resilient Join Operators

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

Dynamical low rank approximation for uncertainty quantification of time-dependent problems

Self-Exciting Point Processes: Identification and Control

Multi-Level Monte Carlo Methods for Uncertainty Quantification and Risk-Averse Optimisation

Training Techniques for Presence-Only Habitat Suitability Mapping with Deep Learning