21 citations · 173 across the 53 of their papers we have counts for
11 papers · 1 filter
Does Machine Unlearning Truly Remove Knowledge?
Haokun Chen, Yueqi Zhang, Yuan Bi +9
In recent years, Large Language Models (LLMs) have achieved remarkable advancements, drawing significant attention from the research community. Their capabilities are largely attri…
FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models
Haokun Chen, Hang Li, Yao Zhang +7
One-Shot Federated Learning (OSFL), a special decentralized machine learning paradigm, has recently gained significant attention. OSFL requires only a single round of client data o…
Provably Better Explanations with Optimized Aggregation of Feature Attributions
Thomas Decker, Ananta R. Bhattarai, Jindong Gu +2
Using feature attributions for post-hoc explanations is a common practice to understand and verify the predictions of opaque machine learning models. Despite the numerous technique…
Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak Attacks?
Shuo Chen, Zhen Han, Bailan He +5
Various jailbreak attacks have been proposed to red-team Large Language Models (LLMs) and revealed the vulnerable safeguards of LLMs. Besides, some methods are not limited to the t…
FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning
Haokun Chen, Yao Zhang, Denis Krompass +2
Recently, foundation models have exhibited remarkable advancements in multi-modal learning. These models, equipped with millions (or billions) of parameters, typically require a su…
FedPop: Federated Population-based Hyperparameter Tuning
Haokun Chen, Denis Krompass, Jindong Gu +1
Federated Learning (FL) is a distributed machine learning (ML) paradigm, in which multiple clients collaboratively train ML models without centralizing their local data. Similar to…
Attacking Adversarial Attacks as A Defense
Boxi Wu, Heng Pan, Li Shen +6
It is well known that adversarial attacks can fool deep neural networks with imperceptible perturbations. Although adversarial training significantly improves model robustness, fai…
Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning
Zhiliang Wu, Yinchong Yang, Jindong Gu +1
Deep neural networks are increasingly being used for the analysis of medical images. However, most works neglect the uncertainty in the model's prediction. We propose an uncertaint…
Neural Network Memorization Dissection
Jindong Gu, Volker Tresp
Deep neural networks (DNNs) can easily fit a random labeling of the training data with zero training error. What is the difference between DNNs trained with random labels and the o…
Contextual Prediction Difference Analysis for Explaining Individual Image Classifications
Jindong Gu, Volker Tresp
Much effort has been devoted to understanding the decisions of deep neural networks in recent years. A number of model-aware saliency methods were proposed to explain individual cl…
Understanding Bias in Machine Learning
Jindong Gu, Daniela Oelke
Bias is known to be an impediment to fair decisions in many domains such as human resources, the public sector, health care etc. Recently, hope has been expressed that the use of m…