Machine learning and optimization

Machine learning is the core algorithmic component behind recent successes in artificial intelligence, relying on training models with vast amounts of data. All major subareas of machine learning are represented within the Prairie Institute, including supervised, unsupervised, and reinforcement learning. Our research extends to new algorithm development and the analysis of their theoretical guarantees, as well as representational issues specific to various data types like text and images.

Within machine learning, optimization plays a critical role, as most modern formulations culminate in optimization problems. Our research prioritizes convex optimization algorithms, with particular emphasis on stochastic and distributed algorithms. We also delve into non-convex optimization, pertinent to large-scale models such as neural networks, as well as challenges where the curse of dimensionality is inevitable.