Abstract Machine learning typically presupposes classical probability theory which implies
that aggregation is built upon expectation. There are now multiple reasons to motivate …

Uncertainty is prevalent in engineering design, statistical learning, and decision making
broadly. Due to inherent risk-averseness and ambiguity about assumptions, it is common to …

Expected risk minimization (ERM) is at the core of many machine learning systems. This
means that the risk inherent in a loss distribution is summarized using a single number-its …

Abstract Domain adaptation (DA) has recently emerged as an effective paradigm for training
the target model with labeled source knowledge. When knowledge transfer in DA …

The estimation of cumulative distribution functions (CDFs) is an important learning task with
a great variety of downstream applications, such as risk assessments in predictions and …

Sign Language is a language that prioritizes manual communication, body language, and
lip movements, instead of sound, to communicate. Deaf people are the main group who use …

Virtually all machine learning tasks are characterized using some form of loss function, and
“good performance” is typically stated in terms of a sufficiently small average loss, taken over …

Estimating cumulative distribution functions (CDFs) of context-dependent random variables
is a central statistical task underpinning numerous applications in machine learning and …

Supervised learning models are increasingly used in algorithmic decision-making. The
traditional assumption on the training and testing data being independently and identically …

Building artificial intelligence systems from a human-centered perspective is increasingly
urgent, as large-scale machine learning systems ranging from personalized recommender …