"Data-IQ" is a versatile framework enabling the systematic stratification of training data into outcome-based subgroups using predictive confidence & aleatoric uncertainty, aiding in feature acquisition, dataset selection, & reliable model usage. By: Nabeel Seedat @Cambridge_Uni
@snorkelai
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JoinBoost: SQL-based tree model training library
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"JoinBoost" is a Python library that enables the training of tree models on normalized databases using pure SQL queries, offering portability, scalability, and competitive performance. Submitted by: Zachary Huang @Columbia
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DC-Check: Data Quality Framework for Reliable ML Systems
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"DC-Check" is a checklist-style framework aimed at guiding the creation of reliable ML systems by emphasizing data quality and preparation throughout all stages of the machine learning pipeline. Submitted by: Nabeel Seedat @Cambridge_Uni
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Machine Learning Analyzes Climate Change Infrastructure Impacts
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"Analyzing the impact of climate change on critical infrastructure from the scientific literature" uses a weakly supervised machine learning approach to efficiently analyze a large corpus of research on climate change impacts on infrastructure. By: Tanwi Mallick @argonne
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Lossy Compression for Large Scientific Dataset Training
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"The Bearable Lightness of Big Data" presents the use of lossy compression algorithms to reduce the size of large scientific datasets while maintaining data fidelity for training deep learning models. Submitted by: Wai Tong Chung @Stanford
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Nonlinear Dimensionality Reduction for Fluid Flow Modeling
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"Comparing nonlinear dimensionality reduction for data-driven unsteady fluid flow modeling" explores various NDR techniques, ie manifold learning & deep learning, noting superior spatial reconstruction & physically interpretable modes. Submitted by: Hunor Csala @UUtah
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FILA: Optimal Online Auditing Technique for ML Model Accuracy
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"FILA" is an optimal online auditing technique for ML model accuracy that uses a sampling-based approach and Thompson Sampling to estimate accuracy under a finite labeling budget. Submitted by: Naiqing Guan @UofT
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Counterfactual Fairness Reduces Dataset Bias in Weak Supervision
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"Mitigating Source Bias for Fairer Weak Supervision" introduces a counterfactual fairness-based method to reduce dataset bias, improving accuracy and fairness in models trained via weak supervision. Submitted by: Changho Shin @UWMadison
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AutoWS-Bench-101: Weak Supervision Evaluation Framework
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"AutoWS-Bench-101" provides an evaluation of Automated Weak Supervision against zero-shot or few-shot learners, utilizing 100 labels for training models with limited labeled data. Submitted by: Nicholas Roberts @UWMadison
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Data-Centric AI Conference Poster Competition Now Open
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The poster competition at The Future of Data-Centric AI conf is now live! Get inspired by researchers pushing #AI and #ML boundaries. Thanks to @lambdaAPI for sponsoring the fantastic prizes. Head over to https://
future.snorkel.ai/poster/ to connect with the authors.