Article List

The performance of machine learning models relies heavily on the quality of input data, yet real-world applications often face significant data-relate...

A primary challenge facing modern scientific research is the limited availability of gold-standard data, which can be costly, labor-intensive, or inva...

We consider the paradigm of unsupervised anomaly detection, which involves the identification of anomalies within a dataset in the absence of labeled...

Deep neural networks have produced significant progress among machine learning models in terms of accuracy and functionality, but their inner workings...

We consider the constrained Linear Inverse Problem (LIP), where a certain atomic norm (like the $\ell_1 $ norm) is minimized subject to a quadratic co...

Irreducible Cartesian tensors (ICTs) play a crucial role in the design of equivariant graph neural networks, as well as in theoretical chemistry and c...

In differential privacy (DP) mechanisms, it can be beneficial to release "redundant" outputs, where some quantities can be estimated in multiple ways...

We explicitly construct zero loss neural network classifiers. We write the weight matrices and bias vectors in terms of cumulative parameters, which d...

We propose a new method for feature learning and function estimation in supervised learning via regularised empirical risk minimisation. Our approach...

We introduce a data-driven approach to analyze the performance of continuous optimization algorithms using generalization guarantees from statistical...

We study contextual bandits in the presence of a stage-wise constraint when the constraint must be satisfied both with high probability and in expecta...

We present a boosting-based method to learn additive Structural Equation Models (SEMs) from observational data, with a focus on the theoretical aspect...

We establish frequentist properties, i.e., posterior consistency, asymptotic normality, and posterior contraction rates, for the distributed (non-)Bay...

Dynamic treatment regimes or policies are a sequence of decision functions over multiple stages that are tailored to individual features. One importan...

In the last few years, many explanation methods based on the perturbations of input data have been introduced to shed light on the predictions generat...

Autoencoders have found widespread application in both their original deterministic form and in their variational formulation (VAEs). In scientific ap...

We consider the nonparametric regression problem when the covariates are located on an unknown compact submanifold of a Euclidean space. Under definin...

Evolutionary science provides evidence that diversity confers resilience in natural systems. Yet, traditional multi-agent reinforcement learning techn...