This thesis makes considerable contributions to the realm of machine
learning, specifically in the context of open-world scenarios where systems
face previously unseen data and contexts. Traditional machine learning models
are usually trained and tested within a fixed and known set of classes, a
condition known as the closed-world setting. While this assumption works in
controlled environments, it falls short in real-world applications where new
classes or categories of data can emerge dynamically and unexpectedly. To
address this, our research investigates two intertwined steps essential for
open-world machine learning: Out-of-distribution (OOD) Detection and Open-world
Representation Learning (ORL). OOD detection focuses on identifying instances
from unknown classes that fall outside the model's training distribution. This
process reduces the risk of making overly confident, erroneous predictions
about unfamiliar inputs. Moving beyond OOD detection, ORL extends the
capabilities of the model to not only detect unknown instances but also learn
from and incorporate knowledge about these new classes. By delving into these
research problems of open-world learning, this thesis contributes both
algorithmic solutions and theoretical foundations, which pave the way for
building machine learning models that are not only performant but also reliable
in the face of the evolving complexities of the real world.

这篇论文在机器学习领域做出了重要贡献，特别是在面对前所未见的数据和情境的开放世界情况下。通过研究开放世界机器学习中的 Out-of-distribution (OOD) Detection 和 Open-world Representation Learning (ORL) 两个关键步骤，该论文提出了算法解决方案和理论基础，为构建性能卓越且在不断变化的复杂实际世界中可靠的机器学习模型铺平了道路。