Governing equations are foundations for modelling, predicting, and understanding the Earth
system. The Earth system is undergoing rapid change, and the conventional approaches for …

By linking conceptual theories with observed data, generative models can support
reasoning in complex situations. They have come to play a central role both within and …

We present a novel binary convex reformulation of the sparse regression problem that
constitutes a new duality perspective. We devise a new cutting plane method and provide …

We consider the least squares regression problem, penalized with a combination of the ℓ _ 0
ℓ 0 and squared ℓ _ 2 ℓ 2 penalty functions (aka ℓ _0 ℓ _2 ℓ 0 ℓ 2 regularization). Recent …

Discovering governing equations of complex dynamical systems directly from data is a
central problem in scientific machine learning. In recent years, the sparse identification of …

Sparse system identification is the data-driven process of obtaining parsimonious differential
equations that describe the evolution of a dynamical system, balancing model complexity …

We give safe screening rules to eliminate variables from regression with $\ell_0 $
regularization or cardinality constraint. These rules are based on guarantees that a feature …

Flight delays are major sources of disruptions in airline operations. To mitigate them, day-
ahead aircraft routing aims to create flight sequences that can absorb delays and minimize …

Background There are several models that predict the risk of recurrence following resection
of localised, primary gastrointestinal stromal tumour (GIST). However, assessment of …

Many real-life applications consider nominal categorical predictor variables that have a
hierarchical structure, eg economic activity data in Official Statistics. In this paper, we focus …