Learning from human preferences or preference-based learning has been critical to major advancements in AI and machine learning. Since human beings are naturally more reliable at providing feedback on a relative scale compared to numerical values, collecting preference feedback is more budget-friendly and involves less bias. The broad objective of this workshop is twofold:1) Bring together different communities where preference-based learning has played a major role. This includes dueling bandits, multi-agent games, econometrics, social choice theory, reinforcement learning, optimization, robotics and many more, for which we aim to create a suitable forum to exchange techniques, ideas, learn from each other and potentially create new and innovative research questions. 2) Connect theory to practice by identifying real-world systems which can benefit from incorporating preference feedback, such as marketing, revenue management, search engine optimization, recommender systems, healthcare, language modeling, interactive chatbots, text summarization, robotics, and so on.We will consider our workshop a success if it inspires researchers to embark on novel insights in the general area of preference-based learning: Bringing attention from different communities to foster dissemination, cross-fertilization and discussion at scale. Especially, building bridges between experimental researchers and theorists towards developing better models and practical algorithms, and encouraging participants to propose, sketch, and discuss new starting points, questions or applications.