Amir Goharshady from Hong Kong University of Science and Technology presented a talk at MBZUAI on martingale-based verification of probabilistic programs. The talk covered using martingale-based approaches for proving termination and synthesizing cost bounds for probabilistic programs, automating program analysis with template-based methods. He also discussed remaining challenges and open problems in the area. Why it matters: Advances in formal verification and analysis of probabilistic programs are crucial for ensuring the reliability and safety of AI systems that rely on randomization.
The article discusses the importance of sample correlations in computer graphics, vision, and machine learning, highlighting how tailored randomness can improve the efficiency of existing models. It covers various correlations studied in computer graphics and tools to characterize them, including the use of neural networks for developing different correlations. Gurprit Singh from the Max Planck Institute for Informatics will be presenting on the topic. Why it matters: Optimizing sampling techniques via understanding and applying correlations can lead to significant advancements and efficiency gains across multiple AI fields.
KAUST hosted the Advances in Uncertainty Quantification Methods, Algorithms and Applications conference (UQAW2016) in January 2016. The event featured 75 presentations and 20 invited speakers from various countries. Professor Raul Tempone presented research on computational approaches to fouling accumulation and wear degradation using stochastic differential equations. Why it matters: This work provides a new computational approach based on stochastic differential equations to predict fouling patterns of heat exchangers which can optimize maintenance operations and reduce engine shut-down periods.
A Marie Curie Fellow from Inria and UIUC presented research on stochastic gradient descent (SGD) through the lens of Markov processes, exploring the relationships between heavy-tailed distributions, generalization error, and algorithmic stability. The research challenges existing theories about the monotonic relationship between heavy tails and generalization error. It introduces a unified approach for proving Wasserstein stability bounds in stochastic optimization, applicable to convex and non-convex losses. Why it matters: The work provides novel insights into the theoretical underpinnings of stochastic optimization, relevant to researchers at MBZUAI and other institutions in the region working on machine learning algorithms.
This talk discusses the asymptotic study of large asymmetric spiked tensor models. It explores connections between these models and equivalent random matrices constructed through contractions of the original tensor. Mohamed El Amine Seddik, currently a senior researcher at TII in Abu Dhabi, presented the work. Why it matters: The research provides theoretical foundations relevant to machine learning algorithms that leverage low-rank tensor structures, potentially impacting AI research and applications in the region.
KAUST Professor Raul Tempone, an expert in Uncertainty Quantification (UQ), has been appointed as an Alexander von Humboldt Professor at RWTH Aachen University in Germany. This professorship will enable him to further his research on mathematics for uncertainty quantification with new collaborators. Tempone believes the KAUST Strategic Initiative for Uncertainty Quantification (SRI-UQ) contributed to this award. Why it matters: This appointment enhances KAUST's visibility and facilitates cross-fertilization between European and KAUST research groups, benefiting both institutions and attracting talent.
This paper introduces Diffusion-BBO, a new online black-box optimization (BBO) framework that uses a conditional diffusion model as an inverse surrogate model. The framework employs an Uncertainty-aware Exploration (UaE) acquisition function to propose scores in the objective space for conditional sampling. The approach is shown theoretically to achieve a near-optimal solution and empirically outperforms existing online BBO baselines across 6 scientific discovery tasks.