Begoña García Malaxechebarría

Understanding the behavior of stochastic gradient methods is a central problem in modern machine learning. Recent work has highlighted diagonal linear networks as a simplified yet expressive setting for analyzing the optimization and generalization properties of neural models. In this work, we show that in the high-dimensional regime, stochastic gradient descent on diagonal linear networks is well-approximated by continuous dynamics governed by a stochastic differential equation (SDE), which explicitly decouples the drift from the gradient noise. We further derive a deterministic partial differential equation whose solution propagates the relevant state of the iterates and characterizes the time evolution of a broad class of observable statistics, including the risk, curvature, and other metrics for optimality. Finally, we show that, under a suitable parametrization, the stochastic dynamics are globally well posed and converge exponentially fast to zero risk with high probability, yielding a fully explicit non-asymptotic description of their long-time behavior. Numerical simulations corroborate our theoretical findings.

We develop a framework for analyzing the training and learning rate dynamics on a large class of high-dimensional optimization problems, which we call the high line, trained using one-pass stochastic gradient descent (SGD) with adaptive learning rates. We give exact expressions for the risk and learning rate curves in terms of a deterministic solution to a system of ODEs. We then investigate in detail two adaptive learning rates – an idealized exact line search and AdaGrad-Norm – on the least squares problem. When the data covariance matrix has strictly positive eigenvalues, this idealized exact line search strategy can exhibit arbitrarily slower convergence when compared to the optimal fixed learning rate with SGD. Moreover we exactly characterize the limiting learning rate (as time goes to infinity) for line search in the setting where the data covariance has only two distinct eigenvalues. For noiseless targets, we further demonstrate that the AdaGrad-Norm learning rate converges to a deterministic constant inversely proportional to the average eigenvalue of the data covariance matrix, and identify a phase transition when the covariance density of eigenvalues follows a power law distribution. We provide our code for evaluation at https://github.com/amackenzie1/highline2024.

Let \(p\) be an odd prime. Algebraic lattices of full diversity in dimension \(p\) are obtained from ramified cyclic extensions of degree \(p\). The \(3\), \(5\), and \(7\)-dimensional lattices are optimal with respect to sphere packing density and therefore are isometric to laminated lattices in those dimensions.

Edge deployment monitoring studies how machine learning models behave after being transferred from cloud development environments to local edge devices near the data source. In this project, we designed a prototype architecture for monitoring edge AI deployments in remote settings, where devices may operate with intermittent connectivity and where model performance depends not only on predictive quality but also on device-specific constraints such as latency, throughput, and real-time inference viability. Our scenario considered radar-based drone-versus-bird detection using synthetic radar signals, spectrogram generation, and a computer vision model deployed on an edge device. We focused on identifying critical evaluation points across the inference pipeline, including signal processing, image preprocessing, and model prediction, in order to benchmark performance and detect potential bottlenecks. The project emphasized the importance of distinguishing device-independent metrics, such as model version and training accuracy, from device-dependent metrics, such as inference latency and throughput. By combining local benchmarking with centralized aggregation of inference reports, the proposed workflow enables site-specific insights to inform global model updates and operational decisions. The project highlights how systematic monitoring, human-in-the-loop update strategies, and performance profiling can support more reliable and scalable edge AI systems in real-world, resource-constrained environments.

Generative Adversarial Networks (GANs) have emerged as a powerful and versatile tool for generative modeling. In recent years, they have gained significant popularity in various research domains, particularly in image generation, enabling researchers to address challenging problems by generating realistic samples from complex data distributions. In the art industry, where tasks often require significant investments of time, labor, and creativity, there is a growing need for more efficient approaches that can streamline subsequent project phases. To this end, we present MonetGAN, a pioneering framework based on the Least Squares Deep Convolutional CycleGAN, specifically tailored to generate images in the distinctive style of 19th-century renowned painter Claude Monet. After achieving non-trivial results with our baseline model, we explore the integration of advanced techniques and architectures such as ResNet Generators, a Progressive Growth Mechanism, Differential Augmentation, and Dual-Objective Discriminators. Through our research, we find that we can achieve superior results with small changes to gradually build up a successful model, rather than adding too many varying complexities all at once. These outcomes underscore the intricacies involved in the training of GANs, while also opening up promising avenues for further advancements at the intersection of art and artificial intelligence.