Dr. Maxim Panov from TII Abu Dhabi will give a talk on uncertainty estimation in neural networks, covering model calibration, ensemble methods, and Bayesian approaches. The talk will focus on efficient single-network methods for quantifying prediction confidence, without requiring ensembles or major training changes. Panov's background includes experience at Skolkovo Institute of Science and Technology and DATADVANCE Company. Why it matters: Improving uncertainty estimation is crucial for deploying reliable AI systems in critical applications across the GCC region.
This paper analyzes the impact of device uncertainties on deep neural networks (DNNs) in emerging device-based Computing-in-memory (CiM) systems. The authors propose UAE, an uncertainty-aware Neural Architecture Search scheme, to identify DNN models robust to these uncertainties. The goal is to mitigate accuracy drops when deploying trained models on real-world platforms.
MBZUAI's Maxim Panov is developing uncertainty quantification methods to improve the reliability of language models. His work focuses on providing insights into the confidence level of machine learning models' predictions, especially in scenarios where accuracy is critical, such as medicine. Panov is working on post-processing techniques that can be applied to already-trained models. Why it matters: This research aims to address the issue of "hallucinations" in language models, enhancing their trustworthiness and applicability in sensitive domains within the region and globally.
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 a Bayesian optimization method for estimating tire parameters and their uncertainty, addressing a gap in existing literature. The methodology uses Stochastic Variational Inference to estimate parameters and uncertainties, and it is validated against a Nelder-Mead algorithm. The approach is applied to real-world data from the Abu Dhabi Autonomous Racing League, revealing uncertainties in identifying curvature and shape parameters due to insufficient excitation. Why it matters: The research provides a practical tool for assessing tire model parameters in real-world conditions, with implications for autonomous racing and vehicle dynamics modeling in the GCC region.
Munther Dahleh from MIT gave a talk on information design under uncertainty, focusing on the challenges of creating an information marketplace. The talk addressed the externality faced by firms when information is allocated to competitors, and considered two models for this externality. The presentation included mechanisms for both models and highlighted the impact of competition on the revenue collected by the seller. Why it matters: The research advances understanding of information markets and mechanism design, relevant to the growing data economy in the GCC region.
A new framework for constructing confidence sets for causal orderings within structural equation models (SEMs) is presented. It leverages a residual bootstrap procedure to test the goodness-of-fit of causal orderings, quantifying uncertainty in causal discovery. The method is computationally efficient and suitable for medium-sized problems while maintaining theoretical guarantees as the number of variables increases. Why it matters: This offers a new dimension of uncertainty quantification that enhances the robustness and reliability of causal inference in complex systems, but there is no indication of connection to the Middle East.
A presentation will demonstrate the construction of well-calibrated, distribution-free neural Temporal Point Process (TPP) models from multiple event sequences using conformal prediction. The method builds a distribution-free joint prediction region for event arrival time and type with a finite-sample coverage guarantee. The refined method is based on the highest density regions, derived from the joint predictive density of event arrival time and type to address the challenge of creating a joint prediction region for a bivariate response that includes both continuous and discrete data types. Why it matters: This research from a KAUST postdoc improves uncertainty quantification in neural TPPs, which are crucial for modeling continuous-time event sequences, with applications in various fields, by providing more reliable prediction regions.