In-context learning (ICL) is a type of prompting where a transformer model operates on a
sequence of (input, output) examples and performs inference on-the-fly. In this work, we …

The world provides us with data of multiple modalities. Intuitively, models fusing data from
different modalities outperform their uni-modal counterparts, since more information is …

Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …

This paper studies few-shot learning via representation learning, where one uses $ T $
source tasks with $ n_1 $ data per task to learn a representation in order to reduce the …

Linear scalarization, ie, combining all loss functions by a weighted sum, has been the
default choice in the literature of multi-task learning (MTL) since its inception. In recent years …

Improving the generalization of deep networks is an important open challenge, particularly
in domains without plentiful data. The mixup algorithm improves generalization by linearly …

We provide new statistical guarantees for transfer learning via representation learning--
when transfer is achieved by learning a feature representation shared across different tasks …

Abstract The Federated Averaging (FedAvg) algorithm, which consists of alternating
between a few local stochastic gradient updates at client nodes, followed by a model …

Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …

Transfer learning provides a powerful tool for incorporating data from related studies into a
target study of interest. In epidemiology and medical studies, the classification of a target …