98 citations · 213 across the 18 of their papers we have counts for
20 papers
SparseEval: Efficient Evaluation of Large Language Models by Sparse Optimization
Taolin Zhang, Hang Guo, Wang Lu +3
As large language models (LLMs) continue to scale up, their performance on various downstream tasks has significantly improved. However, evaluating their capabilities has become in…
HAROOD: A Benchmark for Out-of-distribution Generalization in Sensor-based Human Activity Recognition
Wang Lu, Yao Zhu, Jindong Wang
Sensor-based human activity recognition (HAR) mines activity patterns from the time-series sensory data. In realistic scenarios, variations across individuals, devices, environment…
Self-Ensemble Post Learning for Noisy Domain Generalization
Wang Lu, Jindong Wang
While computer vision and machine learning have made great progress, their robustness is still challenged by two key issues: data distribution shift and label noise. When domain ge…
UniGame: Turning a Unified Multimodal Model Into Its Own Adversary
Zhaolong Su, Wang Lu, Hao Chen +2
Unified Multimodal Models (UMMs) have shown impressive performance in both understanding and generation with a single architecture. However, UMMs still exhibit a fundamental incons…
Harmonizing Intra-coherence and Inter-divergence in Ensemble Attacks for Adversarial Transferability
Zhaoyang Ma, Zhihao Wu, Wang Lu +6
The development of model ensemble attacks has significantly improved the transferability of adversarial examples, but this progress also poses severe threats to the security of dee…
Optimal Transport for Brain-Image Alignment: Unveiling Redundancy and Synergy in Neural Information Processing
Yang Xiao, Wang Lu, Jie Ji +4
The design of artificial neural networks (ANNs) is inspired by the structure of the human brain, and in turn, ANNs offer a potential means to interpret and understand brain signals…
Enhancing Few-shot CLIP with Semantic-Aware Fine-Tuning
Yao Zhu, Yuefeng Chen, Wei Wang +7
Learning generalized representations from limited training samples is crucial for applying deep neural networks in low-resource scenarios. Recently, methods based on Contrastive La…
ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning
Wang Lu, Hao Yu, Jindong Wang +6
When personalized federated learning (FL) meets large foundation models, new challenges arise from various limitations in resources. In addition to typical limitations such as data…
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization
Wang Lu, Jindong Wang, Xinwei Sun +4
Time series remains one of the most challenging modalities in machine learning research. The out-of-distribution (OOD) detection and generalization on time series tend to suffer du…
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning
Xin Qin, Jindong Wang, Shuo Ma +4
Human activity recognition (HAR) is a time series classification task that focuses on identifying the motion patterns from human sensor readings. Adequate data is essential but a m…
FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning
Wang Lu, Xixu Hu, Jindong Wang +1
Federated learning (FL) has emerged as a new paradigm for privacy-preserving computation in recent years. Unfortunately, FL faces two critical challenges that hinder its actual per…
FIXED: Frustratingly Easy Domain Generalization with Mixup
Wang Lu, Jindong Wang, Han Yu +4
Domain generalization (DG) aims to learn a generalizable model from multiple training domains such that it can perform well on unseen target domains. A popular strategy is to augme…
Out-of-Distribution Representation Learning for Time Series Classification
Wang Lu, Jindong Wang, Xinwei Sun +2
Time series classification is an important problem in real world. Due to its non-stationary property that the distribution changes over time, it remains challenging to build models…
Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution
Wang Lu, Jindong Wang, Yidong Wang +1
The distribution shifts between training and test data typically undermine the performance of models. In recent years, lots of work pays attention to domain generalization (DG) whe…
Domain Generalization for Activity Recognition via Adaptive Feature Fusion
Xin Qin, Jindong Wang, Yiqiang Chen +2
Human activity recognition requires the efforts to build a generalizable model using the training datasets with the hope to achieve good performance in test datasets. However, in r…
Domain-invariant Feature Exploration for Domain Generalization
Wang Lu, Jindong Wang, Haoliang Li +2
Deep learning has achieved great success in the past few years. However, the performance of deep learning is likely to impede in face of non-IID situations. Domain generalization (…
Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition
Wang Lu, Jindong Wang, Yiqiang Chen +3
It is expensive and time-consuming to collect sufficient labeled data to build human activity recognition (HAR) models. Training on existing data often makes the model biased towar…
MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare
Yiqiang Chen, Wang Lu, Xin Qin +2
Federated learning has attracted increasing attention to building models without accessing the raw user data, especially in healthcare. In real applications, different federations…
Personalized Federated Learning with Adaptive Batchnorm for Healthcare
Wang Lu, Jindong Wang, Yiqiang Chen +4
There is a growing interest in applying machine learning techniques to healthcare. Recently, federated learning (FL) is gaining popularity since it allows researchers to train powe…
Generalizing to Unseen Domains: A Survey on Domain Generalization
Jindong Wang, Cuiling Lan, Chang Liu +6
Machine learning systems generally assume that the training and testing distributions are the same. To this end, a key requirement is to develop models that can generalize to unsee…