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.

To foster the spirit of innovation and collaboration, a goal of this workshop, OPT 2023 will focus the contributed talks on research in "Optimization in the Wild"; this title is meant to encompass the new challenges that traditional optimization theory and algorithms face with the growth and variety of novel ML applications.

Successful applications of both theory and algorithms from optimization to ML frequently require a profound redesign or even entirely new approaches. This becomes apparent in settings where the classical (empirical) risk minimization approach is no longer sufficient to address the challenges of learning. As motivating examples, we consider the case of learning under (group or individual) fairness in distributed scenarios, learning under differential privacy, robustness, multi-task and transfer learning, as well as sampling from log-concave distributions. On the other hand, novel neural network architectures (such as transformers) require exploiting its structures for efficient optimization in crucial ways. For these models and problems: What is the role of optimization? What synergies can be exploited with the insights coming from these particular areas towards more efficient and reliable solutions? We will foster discussions directed at developing understanding of these challenges, and raising awareness of the capabilities and risks of using optimization in each of these areas.