The AI Ethics community faces an imperative to empower stakeholders and impacted
community members so that they can scrutinize and influence the design, development, and …

To deal with ambiguities in partial label learning (PLL), state-of-the-art strategies implement
disambiguations by identifying the ground-truth label directly from the candidate label set …

To deal with noises in partial label learning (PLL), existing approaches try to perform
disambiguation either by identifying the ground-truth label or by averaging the candidate …

To deal with ambiguities in partial label learning (PLL), the existing PLL methods implement
disambiguations, by either identifying the ground-truth label or averaging the candidate …

One simple strategy to deal with ambiguity in partial label learning (PLL) is to regard all
candidate labels equally as the ground-truth label, and then solve the PLL problem using …

Partially labeled data learning (PLDL), including partial label learning (PLL) and partial multi-
label learning (PML), has been widely used in nowadays data science. Researchers attempt …

We present consistent algorithms for multiclass learning with complex performance metrics
and constraints, where the objective and constraints are defined by arbitrary functions of the …

What is a fair performance metric? We consider the choice of fairness metrics through the
lens of metric elicitation--a principled framework for selecting performance metrics that best …

Existing observational approaches for learning human preferences, such as inverse
reinforcement learning, usually make strong assumptions about the observability of the …

We consider learning to optimize a classification metric defined by a black-box function of
the confusion matrix. Such black-box learning settings are ubiquitous, for example, when the …