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Emmanuel Agu

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3 papers

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NeurIPS Conference 2023 Conference Paper

Debiasing Pretrained Generative Models by Uniformly Sampling Semantic Attributes

Walter Gerych
Kevin Hickey
Luke Buquicchio
Kavin Chandrasekaran
Abdulaziz Alajaji
Elke A. Rundensteiner
Emmanuel Agu

Generative models are being increasingly used in science and industry applications. Unfortunately, they often perpetuate the biases present in their training sets, such as societal biases causing certain groups to be underrepresented in the data. For instance, image generators may overwhelmingly produce images of white people due to few non-white samples in their training data. It is imperative to debias generative models so they synthesize an equal number of instances for each group, while not requiring retraining of the model to avoid prohibitive expense. We thus propose a distribution mapping module that produces samples from a fair noise distribution, such that the pretrained generative model produces semantically uniform outputs - an equal number of instances for each group - when conditioned on these samples. This does not involve retraining the generator, nor does it require any real training data. Experiments on debiasing generators trained on popular real-world datasets show that our method outperforms existing approaches.

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AAAI Conference 2022 Conference Paper

Recovering the Propensity Score from Biased Positive Unlabeled Data

Walter Gerych
Thomas Hartvigsen
Luke Buquicchio
Emmanuel Agu
Elke Rundensteiner

Positive-Unlabeled (PU) learning methods train a classifier to distinguish between the positive and negative classes given only positive and unlabeled data. While traditional PU methods require the labeled positive samples to be an unbiased sample of the positive distribution, in practice the labeled sample is often a biased draw from the true distribution. Prior work shows that if we know the likelihood that each positive instance will be selected for labeling, referred to as the propensity score, then the biased sample can be used for PU learning. Unfortunately, no prior work has been proposed an inference strategy for which the propensity score is identifiable. In this work, we propose two sets of assumptions under which the propensity score can be uniquely determined: one in which no assumption is made on the functional form of the propensity score (requiring assumptions on the data distribution), and the second which loosens the data assumptions while assuming a functional form for the propensity score. We then propose inference strategies for each case. Our empirical study shows that our approach significantly outperforms the state-of-the-art propensity estimation methods on a rich variety of benchmark datasets.

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NeurIPS Conference 2021 Conference Paper

Recurrent Bayesian Classifier Chains for Exact Multi-Label Classification

Walter Gerych
Tom Hartvigsen
Luke Buquicchio
Emmanuel Agu
Elke A. Rundensteiner

Exact multi-label classification is the task of assigning each datapoint a set of class labels such that the assigned set exactly matches the ground truth. Optimizing for exact multi-label classification is important in domains where missing a single label can be especially costly, such as in object detection for autonomous vehicles or symptom classification for disease diagnosis. Recurrent Classifier Chains (RCCs), a recurrent neural network extension of ensemble-based classifier chains, are the state-of-the-art exact multi-label classification method for maximizing subset accuracy. However, RCCs iteratively predict classes with an unprincipled ordering, and therefore indiscriminately condition class probabilities. These disadvantages make RCCs prone to predicting inaccurate label sets. In this work we propose Recurrent Bayesian Classifier Chains (RBCCs), which learn a Bayesian network of class dependencies and leverage this network in order to condition the prediction of child nodes only on their parents. By conditioning predictions in this way, we perform principled and non-noisy class prediction. We demonstrate the effectiveness of our RBCC method on a variety of real-world multi-label datasets, where we routinely outperform the state of the art methods for exact multi-label classification.

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