MBZUAI researchers developed AirCast, a novel AI model for improved air pollution forecasting, which won the best paper award at the TerraBytes workshop during ICML. AirCast fuses weather and chemistry data using a Vision Transformer and frequency-weighted MAE to better predict extreme events like Saharan dust storms. In tests across the Middle East and North Africa, AirCast reduced PM2.5 error by 33% compared to a persistence baseline and outperformed the CAMS physics model. Why it matters: Accurate air pollution forecasting is critical for public health in the GCC region, and this research demonstrates a significant advancement using AI to address this challenge.
MBZUAI researchers Nils Lukas and Toluwani Samuel Aremu will present a paper at ICML 2025 demonstrating the vulnerability of current watermarking techniques in LLMs. Their research shows that adaptive paraphrasers can evade detection from watermarks with negligible impact on text quality, costing less than $10 of GPU compute. The attack involves fine-tuning a small open-weight model to rewrite sentences until surrogate keys no longer trigger detection. Why it matters: This work highlights critical weaknesses in current AI provenance methods, suggesting the need for more robust watermarking techniques to maintain trust in the authenticity of AI-generated content.
MBZUAI's Samuel Horváth presented a new framework called Maestro at ICML 2024 for efficiently training machine learning models in federated settings. Maestro identifies and removes redundant components of a model through trainable decomposition to increase efficiency on edge devices. The approach decomposes layers into low-dimensional approximations, discarding unused aspects to reduce model size. Why it matters: This research addresses the challenge of running complex models on resource-constrained devices, crucial for expanding AI applications while preserving data privacy.
MBZUAI Professor Kun Zhang is developing machine learning techniques to identify hidden causal variables, which are underlying concepts driving cause-and-effect relationships. Zhang and colleagues from Carnegie Mellon University are presenting a new approach for this at ICML 2024. Their method, causal representation learning, assumes that measured variables are generated by unobserved latent variables. Why it matters: Uncovering hidden causal relationships can significantly advance understanding in various fields by revealing the underlying mechanisms driving observed phenomena.
MBZUAI researchers presented a study at ICML 2024 examining how data aggregation distorts causal discovery. The study argues that current methods are misled because real-world interactions happen at a micro level while observations are aggregated. Using the example of ice cream sales and temperature, they highlight how aggregation introduces "instantaneous causality" where time-lags exist. Why it matters: The research identifies a fundamental limitation in current causal discovery methods, potentially impacting disciplines relying on accurate causal inference from observational data.
MBZUAI and KAUST researchers collaborated to present new optimization methods at ICML 2024 for composite and distributed machine learning settings. The study addresses challenges in training large models due to data size and computational power. Their work focuses on minimizing the "loss function" by adjusting internal trainable parameters, using techniques like gradient clipping. Why it matters: This research contributes to the ongoing advancement of machine learning optimization, crucial for improving the performance and efficiency of AI models in the region and globally.
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.
MBZUAI Department Chair Le Song served as a program co-chair at the 39th International Conference on Machine Learning (ICML). MBZUAI faculty, researchers, and students had 7 papers accepted at ICML 2022. Song noted the increasing focus on biomedicine and other science areas within the AI research community. Why it matters: Song's leadership role at ICML and MBZUAI's strong presence highlights the university's growing influence in the global machine learning landscape.