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1. Research Replication Prediction Using Weakly Supervised Learning

   https://doi.org/10.18653/v1/2020.findings-emnlp.132  

   Luo, Tianyi; Li, Xingyu; Wang, Hainan; Liu, Yang (January 2020, In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings)

   null (Ed.)

   Knowing whether a published research result can be replicated is important. Carrying out direct replication of published research incurs a high cost. There are efforts tried to use machine learning aided methods to predict scientific claims’ replicability. However, existing machine learning aided approaches use only hand-extracted statistics features such as p-value, sample size, etc. without utilizing research papers’ text information and train only on a very small size of annotated data without making the most use of a large number of unlabeled articles. Therefore, it is desirable to develop effective machine learning aided automatic methods which can automatically extract text information as features so that we can benefit from Natural Language Processing techniques. Besides, we aim for an approach that benefits from both labeled and the large number of unlabeled data. In this paper, we propose two weakly supervised learning approaches that use automatically extracted text information of research papers to improve the prediction accuracy of research replication using both labeled and unlabeled datasets. Our experiments over real-world datasets show that our approaches obtain much better prediction performance compared to the supervised models utilizing only statistic features and a small size of labeled dataset. Further, we are able to achieve an accuracy of 75.76% for predicting the replicability of research. 

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2. Robust Fairness under Covariate Shift.

   Rezaei, Ashkan; Liu, Anqi; Memarrast, Omid; Ziebart, Brian D. (February 2021, Proceedings of the AAAI Conference on Artificial Intelligence)

   Making predictions that are fair with regard to protected attributes (race, gender, age, etc.) has become an important requirement for classification algorithms. Existing techniques derive a fair model from sampled labeled data relying on the assumption that training and testing data are identically and independently drawn (iid) from the same distribution. In practice, distribution shift can and does occur between training and testing datasets as the characteristics of individuals interacting with the machine learning system change. We investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution remains the same. We seek fair decisions under these assumptions on target data with unknown labels. We propose an approach that obtains the predictor that is robust to the worst-case testing performance while satisfying target fairness requirements and matching statistical properties of the source data. We demonstrate the benefits of our approach on benchmark prediction tasks. 

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3. Vision for Prosthesis Control Using Unsupervised Labeling of Training Data

   Rai, V; Rombokas, E (July 2021, 2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids))

   null (Ed.)

   Transitioning from one activity to another is oneof the key challenges of prosthetic control. Vision sensorsprovide a glance into the environment’s desired and futuremovements, unlike body sensors (EMG, mechanical). This couldbe employed to anticipate and trigger transitions in prosthesisto provide a smooth user experience.A significant bottleneck in using vision sensors has beenthe acquisition of large labeled training data. Labeling theterrain in thousands of images is labor-intensive; it would beideal to simply collect visual data for long periods withoutneeding to label each frame. Toward that goal, we apply anunsupervised learning method to generate mode labels forkinematic gait cycles in training data. We use these labels withimages from the same training data to train a vision classifier.The classifier predicts the target mode an average of 2.2 secondsbefore the kinematic changes. We report 96.6% overall and99.5% steady-state mode classification accuracy. These resultsare comparable to studies using manually labeled data. Thismethod, however, has the potential to dramatically scale withoutrequiring additional labeling. 

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4. Synthesizing class labels for highly imbalanced credit card fraud detection data

   https://doi.org/10.1186/s40537-024-00897-7  

   Kennedy, Robert_K L; Villanustre, Flavio; Khoshgoftaar, Taghi M; Salekshahrezaee, Zahra (December 2024, Journal of Big Data)

   Furht, Borko; Khoshgoftaar, Taghi (Ed.)

   Acquiring labeled datasets often incurs substantial costs primarily due to the requirement of expert human intervention to produce accurate and reliable class labels. In the modern data landscape, an overwhelming proportion of newly generated data is unlabeled. This paradigm is especially evident in domains such as fraud detection and datasets for credit card fraud detection. These types of data have their own difficulties associated with being highly class imbalanced, which poses its own challenges to machine learning and classification. Our research addresses these challenges by extensively evaluating a novel methodology for synthesizing class labels for highly imbalanced credit card fraud data. The methodology uses an autoencoder as its underlying learner to effectively learn from dataset features to produce an error metric for use in creating new binary class labels. The methodology aims to automatically produce new labels with minimal expert input. These class labels are then used to train supervised classifiers for fraud detection. Our empirical results show that the synthesized labels are of high enough quality to produce classifiers that significantly outperform a baseline learner comparison when using area under the precision-recall curve (AUPRC). We also present results of varying levels of positive-labeled instances and their effect on classifier performance. Results show that AUPRC performance improves as more instances are labeled positive and belong to the minority class. Our methodology thereby effectively addresses the concerns of high class imbalance in machine learning by creating new and effective class labels. 

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5. Cross-prediction-powered inference

   https://doi.org/10.1073/pnas.2322083121  

   Zrnic, Tijana; Candès, Emmanuel J (April 2024, Proceedings of the National Academy of Sciences)

   While reliable data-driven decision-making hinges on high-quality labeled data, the acquisition of quality labels often involves laborious human annotations or slow and expensive scientific measurements. Machine learning is becoming an appealing alternative as sophisticated predictive techniques are being used to quickly and cheaply produce large amounts of predicted labels; e.g., predicted protein structures are used to supplement experimentally derived structures, predictions of socioeconomic indicators from satellite imagery are used to supplement accurate survey data, and so on. Since predictions are imperfect and potentially biased, this practice brings into question the validity of downstream inferences. We introduce cross-prediction: a method for valid inference powered by machine learning. With a small labeled dataset and a large unlabeled dataset, cross-prediction imputes the missing labels via machine learning and applies a form of debiasing to remedy the prediction inaccuracies. The resulting inferences achieve the desired error probability and are more powerful than those that only leverage the labeled data. Closely related is the recent proposal of prediction-powered inference [A. N. Angelopoulos, S. Bates, C. Fannjiang, M. I. Jordan, T. Zrnic,Science382, 669–674 (2023)], which assumes that a good pretrained model is already available. We show that cross-prediction is consistently more powerful than an adaptation of prediction-powered inference in which a fraction of the labeled data is split off and used to train the model. Finally, we observe that cross-prediction gives more stable conclusions than its competitors; its CIs typically have significantly lower variability. 

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