Researchers are exploring methods for evaluating the outcome of actions using off-policy observations where the context is noisy or anonymized. They employ proxy causal learning, using two noisy views of the context to recover the average causal effect of an action without explicitly modeling the hidden context. The implementation uses learned neural net representations for both action and context, and demonstrates outperformance compared to an autoencoder-based alternative. Why it matters: This research addresses a key challenge in applying AI in real-world scenarios where data privacy or bandwidth limitations necessitate working with noisy or anonymized data.
MBZUAI's AI Quorum workshop featured Yale biostatistics professor Heping Zhang discussing the challenges of using AI and statistics to analyze noisy biological data for health insights. Zhang highlighted the need to develop methods to extract meaningful stories from noisy data to understand brain function and genetic roles in disease regulation. Harvard's Xihong Lin presented recommendations for building an ecosystem using AI and statistics to improve understanding of the relationship between genome sequences and biological functions. Why it matters: This discussion underscores the importance of AI and statistical methods in addressing the complexities of biological data, particularly in understanding neurological diseases like Alzheimer's, and highlights the need for centralized data infrastructure.
MBZUAI researchers will present 20 papers at the 40th International Conference on Machine Learning (ICML) in Honolulu. Visiting Associate Professor Tongliang Liu leads with seven publications, followed by Kun Zhang with six. One paper investigates semi-supervised learning vs. model-based methods for noisy data annotation in deep neural networks. Why it matters: The research addresses the critical issue of data quality and accessibility in machine learning, particularly for organizations with limited resources for data annotation.
This article discusses methods for handling label noise in deep learning, including extracting confident examples and modeling label noise. Tongliang Liu from the University of Sydney presented these approaches. The talk aimed to provide participants with a basic understanding of learning with noisy labels. Why it matters: As AI models are increasingly trained on large, noisy datasets, techniques for robust learning become crucial for reliable real-world performance.