A comprehensive survey on test-time adaptation under distribution shifts
Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …
process that can effectively generalize to test samples, even in the presence of distribution …
Domain generalization: A survey
Generalization to out-of-distribution (OOD) data is a capability natural to humans yet
challenging for machines to reproduce. This is because most learning algorithms strongly …
challenging for machines to reproduce. This is because most learning algorithms strongly …
On the robustness of chatgpt: An adversarial and out-of-distribution perspective
ChatGPT is a recent chatbot service released by OpenAI and is receiving increasing
attention over the past few months. While evaluations of various aspects of ChatGPT have …
attention over the past few months. While evaluations of various aspects of ChatGPT have …
Dataset distillation via factorization
In this paper, we study dataset distillation (DD), from a novel perspective and introduce
a\emph {dataset factorization} approach, termed\emph {HaBa}, which is a plug-and-play …
a\emph {dataset factorization} approach, termed\emph {HaBa}, which is a plug-and-play …
Generalized out-of-distribution detection: A survey
Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …
machine learning systems. For instance, in autonomous driving, we would like the driving …
On the opportunities and risks of foundation models
AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …
Towards out-of-distribution generalization: A survey
Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …
test data follow the same statistical pattern, which is mathematically referred to as …
A holistic approach to undesired content detection in the real world
We present a holistic approach to building a robust and useful natural language
classification system for real-world content moderation. The success of such a system relies …
classification system for real-world content moderation. The success of such a system relies …
Data-free knowledge distillation for heterogeneous federated learning
Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global
server iteratively averages the model parameters of local users without accessing their data …
server iteratively averages the model parameters of local users without accessing their data …