OPT 2025: Optimization for Machine Learning

Optimization lies at the heart of many machine learning algorithms and enjoys great interest in our community. Indeed, this intimate relation of optimization with ML is the key motivation for the OPT series of workshops. We aim to foster discussion, discovery, and dissemination of state-of-the-art research in optimization relevant to ML.

The focus of OPT 2025 is on "Statistics Meets Optimization". Since its inception, stochastic optimization has been grounded in statistical principles. Today, many of the most pressing challenges in machine learning—such as generalization bounds, the training dynamics of overparameterized models, and the development of generative models—are directly inspired by statistical thinking. At the same time, the scale and complexity of modern datasets, along with the increasingly rich model classes used to represent them, pose new questions about how optimization algorithms interact with these structures—both computationally and statistically. For example, what role do data symmetries play in shaping optimization trajectories? How do statistical properties of the data affect the adaptivity and efficiency of learning algorithms? And how can optimization approaches be designed to scale with data while still preserving desirable statistical behavior? OPT 2025 will explore these questions with the goal of building bridges between the statistics and optimization communities, and highlighting their shared impact on the theory and practice of machine learning.

We are looking forward to seeing you all at OPT 2025, which will take place at the San Diego Convention Center!