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 background information on systemic biases in its context. Such assumptions can, in turn, be used to mitigate this bias during training. Yet, a framework for incorporating such assumptions that is simultaneously principled, flexible, and interpretable is currently lacking. Our approach is to formalize bias assumptions as programs in ProbLog, a probabilistic logic programming language that allows for the description of probabilistic causal relationships through logic. Neurosymbolic extensions of ProbLog then allow for easy integration of these assumptions in a neural network's training process. We propose a set of templates to express different types of bias and show the versatility of our approach on synthetic tabular datasets with known biases. Using estimates of the bias distortions present, we also succeed in mitigating algorithmic bias in real-world tabular and image data. We conclude that ProbLog4Fairness outperforms baselines due to its ability to flexibly model the relevant bias assumptions, where other methods typically uphold a fixed bias type or notion of fairness.

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 setting arises when the observed positive labels are a biased sample from the positive distribution. Current approaches either require estimating the propensity scores, which are the instance-specific probabilities that a positive example’s label will be observed, or make overly restricting assumptions about the labeling mechanism. We make a novel assumption about the labeling mechanism which we show is more general than several commonly used existing ones. Moreover, the combination of our novel assumption and theoretical results from robust statistics can simplify the process of learning from biased PU data. Empirically, our approach offers superior predictive and run time performance compared to the state-of-the-art methods.

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 examples. Second, those facts that were selected to be included in the knowledge base, are most likely not an i.i.d. sample of the true facts, due to the way knowledge bases are constructed. In this paper, we focus on rule-based approaches, which traditionally address the first challenge by making assumptions that enable identifying negative examples, which in turn makes it possible to compute a rule's confidence or precision. However, they largely ignore the second challenge, which means that their estimates of a rule's confidence can be biased. This paper approaches rule-based KBC through the lens of PU-learning, which can cope with both challenges. We make three contributions.: (1) We provide a unifying view that formalizes the relationship between multiple existing confidences measures based on (i) what assumption they make about and (ii) how their accuracy depends on the selection mechanism. (2) We introduce two new confidence measures that can mitigate known biases by using propensity scores that quantify how likely a fact is to be included the KB. (3) We show through theoretical and empirical analysis that taking the bias into account improves the confidence estimates, even when the propensity scores are not known exactly.

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 control over the global style of the speech and do not allow fine-grained adjustments. Other techniques create fine-grained prosody embeddings, but these are difficult to manipulate to obtain a desired speaking style. We thus present ConEx, a novel model for expressive speech synthesis, which can produce speech in a certain speaking style, while also allowing local adjustments to the prosody of the generated speech. The model builds upon the non-autoregressive architecture of FastSpeech and includes a reference encoder to learn global prosody embeddings, and a vector quantized variational autoencoder to create fine-grained prosody embeddings. To realize prosody manipulation, a new interactive method is proposed. Experiments on two datasets show that the model enables multi-level prosody control.

For tasks such as medical diagnosis and knowledge base completion, a classifier may only have access to positive and unlabeled examples, where the unlabeled data consists of both positive and negative examples. One way that enables learning from this type of data is knowing the true class prior. In this paper, we propose a simple yet effective method for estimating the class prior, by estimating the probability that a positive example is selected to be labeled. Our key insight is that subdomains of the data give a lower bound on this probability. This lower bound gets closer to the real probability as the ratio of labeled examples increases. Finding such subsets can naturally be done via top-down decision tree induction. Experiments show that our method makes estimates which are equivalently accurate as those of the state of the art methods, and is an order of magnitude faster.

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 assignments~$\xs,\ys$. We propose a tractable learner that guarantees efficient inference for a broader class of queries. It simultaneously learns a Markov network and its tractable circuit representation, in order to guarantee and measure tractability. Our approach differs from earlier work by using Sentential Decision Diagrams (SDD) as the tractable language instead of Arithmetic Circuits (AC). SDDs have desirable properties, which more general representations such as ACs lack, that enable basic primitives for Boolean circuit compilation. This allows us to support a broader class of complex probability queries, including counting, threshold, and parity, in polytime.