In recent years, prompt tuning has sparked a research surge in adapting pre-trained models.
Unlike the unified pre-training strategy employed in the language field, the graph field …

This paper introduces an efficient fine-tuning method for large pre-trained models, offering
strong in-distribution (ID) and out-of-distribution (OOD) performance. Breaking away from …

We aim at finetuning a vision-language model without hurting its out-of-distribution (OOD)
generalization. We address two types of OOD generalization ie i) domain shift such as …

Generalization to out-of-distribution (OOD) data is a critical challenge in machine learning.
Ensemble-based methods, like weight space ensembles that interpolate model parameters …

Improving out-of-distribution (OOD) generalization during in-distribution (ID) adaptation is a
primary goal of robust fine-tuning of zero-shot models beyond naive fine-tuning. However …

Robust fine-tuning aims to achieve competitive in-distribution (ID) performance while
maintaining the out-of-distribution (OOD) robustness of a pre-trained model when …

Handling distribution shifts from training data, known as out-of-distribution (OOD)
generalization, poses a significant challenge in the field of machine learning. While a pre …

Adapting a pre-trained foundation model on downstream tasks should ensure robustness
against distribution shifts without the need to retrain the whole model. Although existing …

Training machine learning (ML) models for scientific problems is often challenging due to
limited observation data. To overcome this challenge, prior works commonly pre-train ML …

We propose a learning problem involving adapting a pre-trained source model to the target
domain for classifying all classes that appeared in the source data, using target data that …