Haohan Wang

Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives

2023 · arXiv (Cornell University)

The trustworthiness of machine learning has emerged as a critical topic in the field, encompassing various applications and research areas such as robustness, security, interpretability, and fairness. The last decade saw the development of numerous …

BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise Learning

2024 · IEEE Transactions on Pattern Analysis and Machine Intelligence

Label-noise learning (LNL) aims to increase the model's generalization given training data with noisy labels. To facilitate practical LNL algorithms, researchers have proposed different label noise types, ranging from class-conditional to instance-dependent noises. In this …

Signal Modulation Recognition via Bias Adjustment-Based Class Incremental Learning

2024 · IEEE Sensors Journal

Automatic modulation classification (AMC) is crucial for electronic warfare, spectrum monitoring, and cognitive radios. Traditional methods, relying on maximum likelihood estimation and manual feature extraction, face challenges, such as complexity, inaccuracy, and limited adaptability. In …

Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual

2019

Statistical natural language inference (NLI) models are susceptible to learning dataset bias: superficial cues that happen to associate with the label on a particular dataset, but are not useful in general, e.g., negation words indicate …