Mladen Kolar from the University of Chicago Booth School of Business discussed stochastic optimization with equality constraints at MBZUAI. He presented a stochastic algorithm based on sequential quadratic programming (SQP) using a differentiable exact augmented Lagrangian. The algorithm adapts random stepsizes using a stochastic line search procedure, establishing global "almost sure" convergence. Why it matters: The presentation highlights MBZUAI's role in hosting discussions on advanced optimization techniques, fostering research and knowledge exchange in the field of machine learning.
MBZUAI researchers presented a new second-order method for optimizing neural networks at NeurIPS 2024. The method addresses optimization problems related to variational inequalities common in machine learning. They demonstrated that for monotone inequalities with inexact second-order derivatives, no faster second- or first-order methods can theoretically exist, supporting this with experiments. Why it matters: This research has the potential to reduce the computational cost of training large and complex neural networks, which could accelerate AI development in the region.
Alexander Gasnikov from the Moscow Institute of Physics and Technology presented a talk on open problems in convex optimization. The talk covered stochastic averaging vs stochastic average approximation, saddle-point problems and accelerated methods, homogeneous federated learning, and decentralized optimization. Gasnikov's research focuses on optimization algorithms and he has published in NeurIPS, ICML, EJOR, OMS, and JOTA. Why it matters: While the talk itself isn't directly related to GCC AI, understanding convex optimization is crucial for advancing machine learning algorithms used in the region.
MBZUAI researchers presented a new strategy for handling complex optimization problems in machine learning at ICLR 2024. The study, a collaboration with ISAM, combines zeroth-order methods with hard-thresholding to address specific settings in machine learning. This approach aims to improve convergence, ensuring algorithms reach quality solutions efficiently. Why it matters: Improving optimization techniques is crucial for advancing machine learning models used in various applications, potentially accelerating development and enhancing performance.
The paper introduces Sparse-Quantized Representation (SpQR), a new compression format and quantization technique for large language models (LLMs). SpQR identifies outlier weights and stores them in higher precision while compressing the remaining weights to 3-4 bits. The method achieves less than 1% accuracy loss in perplexity for LLaMA and Falcon LLMs and enables a 33B parameter LLM to run on a single 24GB consumer GPU. Why it matters: This enables near-lossless compression of LLMs, making powerful models accessible on resource-constrained devices and accelerating inference without significant accuracy degradation.
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.
KAUST's Stochastic Numerics Research Group is developing methods for pricing European options. Their approach, detailed in an upcoming Journal of Computational Finance article, focuses on systematically tuning parameters to achieve accuracy while minimizing computational effort. The goal is to enable automated computation of fair prices for options contracts, similar to how insurance companies determine premiums. Why it matters: This research advances computational finance in the region, potentially improving risk management and investment strategies.
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.