Continual learning (CL) aims to develop techniques by which a single model adapts to an
increasing number of tasks encountered sequentially, thereby potentially leveraging …

We study (differentially) private federated learning (FL) of language models. The language
models in cross-device FL are relatively small, which can be trained with meaningful formal …

Bayesian models are powerful tools for studying complex data, allowing the analyst to
encode rich hierarchical dependencies and leverage prior information. Most importantly …

A Bayesian coreset is a small, weighted subset of data that replaces the full dataset during
inference in order to reduce computational cost. However, state of the art methods for tuning …

A Bayesian coreset is a small, weighted subset of data that replaces the full dataset during
Bayesian inference, with the goal of reducing computational cost. Although past work has …

Bayesian coresets approximate a posterior distribution by building a small weighted subset
of the data points. Any inference procedure that is too computationally expensive to be run …

With the development of data-centric artificial intelligence, more and more people pay
attention to the importance of image information quality. Based on the core idea that images …

Recent advances in coreset methods have shown that a selection of representative
datapoints can replace massive volumes of data for Bayesian inference, preserving the …

Bayesian models are a powerful tool for studying complex data, allowing the analyst to
encode rich hierarchical dependencies and leverage prior information. Most importantly …

In the recent era of artificial intelligence, Large Language Models (LLMs) have achieved
unprecedented success in a wide range of Natural Language Processing (NLP) tasks …