activity
20192026
most citedDMLR: Data-centric Machine Learning Research -- Past, Present and Future

6 citations · 15 across the 24 of their papers we have counts for

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9 papers · 1 filter

cs.LG2025

Machine Learning for Sustainable Rice Production: Region-Scale Monitoring of Water-Saving Practices in Punjab, India

Ando Shah, Rajveer Singh, Akram Zaytar +6

Rice cultivation supplies half the world's population with staple food, while also being a major driver of freshwater depletion--consuming roughly a quarter of global freshwater--a…

cs.LG2023

Efficient Representation of the Activation Space in Deep Neural Networks

Tanya Akumu, Celia Cintas, Girmaw Abebe Tadesse +3

The representations of the activation space of deep neural networks (DNNs) are widely utilized for tasks like natural language processing, anomaly detection and speech recognition.…

cs.LG2023★ 6 cited

DMLR: Data-centric Machine Learning Research -- Past, Present and Future

Luis Oala, Manil Maskey, Lilith Bat-Leah +35

Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure developm…

cs.LG2023★ 1 cited

Domain-agnostic and Multi-level Evaluation of Generative Models

Girmaw Abebe Tadesse, Jannis Born, Celia Cintas +4

While the capabilities of generative models heavily improved in different domains (images, text, graphs, molecules, etc.), their evaluation metrics largely remain based on simplifi…

cs.LG2022

Model-free feature selection to facilitate automatic discovery of divergent subgroups in tabular data

Girmaw Abebe Tadesse, William Ogallo, Celia Cintas +1

Data-centric AI encourages the need of cleaning and understanding of data in order to achieve trustworthy AI. Existing technologies, such as AutoML, make it easier to design and tr…

cs.LG2022

Sparsity-based Feature Selection for Anomalous Subgroup Discovery

Girmaw Abebe Tadesse, William Ogallo, Catherine Wanjiru +5

Anomalous pattern detection aims to identify instances where deviation from normalcy is evident, and is widely applicable across domains. Multiple anomalous detection techniques ha…

cs.LG2021

Post-discovery Analysis of Anomalous Subsets

Isaiah Onando Mulang', William Ogallo, Girmaw Abebe Tadesse +1

Analyzing the behaviour of a population in response to disease and interventions is critical to unearth variability in healthcare as well as understand sub-populations that require…

cs.LG2021

Automated Supervised Feature Selection for Differentiated Patterns of Care

Catherine Wanjiru, William Ogallo, Girmaw Abebe Tadesse +3

An automated feature selection pipeline was developed using several state-of-the-art feature selection techniques to select optimal features for Differentiating Patterns of Care (D…

cs.LG2020

Prediction of neonatal mortality in Sub-Saharan African countries using data-level linkage of multiple surveys

Girmaw Abebe Tadesse, Celia Cintas, Skyler Speakman +1

Existing datasets available to address crucial problems, such as child mortality and family planning discontinuation in developing countries, are not ample for data-driven approach…