Language models have been shown to perform better with an increase in scale on a wide
variety of tasks via the in-context learning paradigm. In this paper, we investigate the …

Despite the rich existing literature about minimax optimization in continuous settings, only
very partial results of this kind have been obtained for combinatorial settings. In this paper …

The deployment of Deep Neural Networks (DNNs) on edge devices is hindered by the
substantial gap between performance requirements and available computational power …

Feature selection is the problem of selecting a subset of features for a machine learning
model that maximizes model quality subject to a budget constraint. For neural networks …

The Lottery Ticket Hypothesis (LTH) states that a dense neural network model contains a
highly sparse subnetwork (ie, winning tickets) that can achieve even better performance …

In recent years, the integration of Machine Learning (ML) models with Operation Research
(OR) tools has gained popularity across diverse applications, including cancer treatment …

In this paper, we consider the optimization problem Submodular Cover (SCP), which is to
find a minimum cardinality subset of a finite universe $ U $ such that the value of a …

Many recent breakthroughs in machine learning have been enabled by the pre-trained
foundation models. By scaling up model parameters, training data, and computation …

Regularization techniques such as $\mathcal {L} _1 $ and $\mathcal {L} _2 $ regularizers
are effective in sparsifying neural networks (NNs). However, to remove a certain neuron or …

In machine learning, artificial neural networks (ANNs) are a very powerful tool, broadly used
in many applications. Often, the selected (deep) architectures include many layers, and …