15 citations · 17 across the 11 of their papers we have counts for
12 papers
A Sober Look at Agentic Misalignment in Automated Workflows
Wenqian Ye, Bo Yuan, Zhichao Xu +4
We study a class of emergent misalignment in multi-agent systems (MAS), with a focus on automated workflows, which we refer to agentic misalignment. Although these systems can solv…
SAGE: Spuriousness-Aware Guided Prompt Exploration for Mitigating Multimodal Bias
Wenqian Ye, Di Wang, Guangtao Zheng +2
Large vision-language models, such as CLIP, have shown strong zero-shot classification performance by aligning images and text in a shared embedding space. However, CLIP models oft…
Rectifying Shortcut Behaviors in Preference-based Reward Learning
Wenqian Ye, Guangtao Zheng, Aidong Zhang
In reinforcement learning from human feedback, preference-based reward models play a central role in aligning large language models to human-aligned behavior. However, recent studi…
Improving Group Robustness on Spurious Correlation via Evidential Alignment
Wenqian Ye, Guangtao Zheng, Aidong Zhang
Deep neural networks often learn and rely on spurious correlations, i.e., superficial associations between non-causal features and the targets. For instance, an image classifier ma…
ShortcutProbe: Probing Prediction Shortcuts for Learning Robust Models
Guangtao Zheng, Wenqian Ye, Aidong Zhang
Deep learning models often achieve high performance by inadvertently learning spurious correlations between targets and non-essential features. For example, an image classifier may…
NeuronTune: Towards Self-Guided Spurious Bias Mitigation
Guangtao Zheng, Wenqian Ye, Aidong Zhang
Deep neural networks often develop spurious bias, reliance on correlations between non-essential features and classes for predictions. For example, a model may identify objects bas…
Benchmarking Spurious Bias in Few-Shot Image Classifiers
Guangtao Zheng, Wenqian Ye, Aidong Zhang
Few-shot image classifiers are designed to recognize and classify new data with minimal supervision and limited data but often show reliance on spurious correlations between classe…
Spuriousness-Aware Meta-Learning for Learning Robust Classifiers
Guangtao Zheng, Wenqian Ye, Aidong Zhang
Spurious correlations are brittle associations between certain attributes of inputs and target variables, such as the correlation between an image background and an object class. D…
MM-SpuBench: Towards Better Understanding of Spurious Biases in Multimodal LLMs
Wenqian Ye, Bohan Liu, Guangtao Zheng +6
Spurious bias, a tendency to exploit spurious correlations between superficial input attributes and prediction targets, has revealed a severe robustness pitfall in classical machin…
Learning Robust Classifiers with Self-Guided Spurious Correlation Mitigation
Guangtao Zheng, Wenqian Ye, Aidong Zhang
Deep neural classifiers tend to rely on spurious correlations between spurious attributes of inputs and targets to make predictions, which could jeopardize their generalization cap…
The Clever Hans Mirage: A Comprehensive Survey on Spurious Correlations in Machine Learning
Wenqian Ye, Luyang Jiang, Eric Xie +16
Back in the early 20th century, a horse named Hans appeared to perform arithmetic and other intellectual tasks during exhibitions in Germany, while it actually relied solely on inv…
AdvST: Revisiting Data Augmentations for Single Domain Generalization
Guangtao Zheng, Mengdi Huai, Aidong Zhang
Single domain generalization (SDG) aims to train a robust model against unknown target domain shifts using data from a single source domain. Data augmentation has been proven an ef…