5 accepted papers
Operationalizing definitions of fairness is difficult in practice, as multiple definitions can be incompatible while each being arguably desirable. Instead, it may be easier to directly describe algorithmic bias through ad-hoc assumptions specific to a particular real-world task, e.g., based on back
Learning from positive and unlabeled data (PU learning) aims to train a binary classification model when only positive and unlabeled examples are available. Typically, learners assume that there is a labeling mechanism that determines which positive labels are observed. A particularly challenging s…
Recent neural-based text-to-speech (TTS) models are able to produce highly natural speech. To synthesize expressive speech, the prosody of the speech has to be modeled, and predicted/controlled during synthesis. However, intuitive control over prosody remains elusive. Some techniques only allow cont…
Methods for Knowledge Base Completion (KBC) reason about a knowledge base (KB) in order to derive new facts that should be included in the KB. This is challenging for two reasons. First, KBs only contain positive examples. This complicates model evaluation which needs both positive and negative exam…
Tractable learning aims to learn probabilistic models where inference is guaranteed to be efficient. However, the particular class of queries that is tractable depends on the model and underlying representation. Usually this class is MPE or conditional probabilities $\Pr(\xs|\ys)$ for joint assignm…