2 citations · 4 across the 9 of their papers we have counts for
10 papers
Differentially Private Mixing of Public Datasets Improves Private Learning
Yufei Chen, Tejumade Afonja, Anvith Thudi +1
Many machine learning applications involve sensitive data and therefore require training under differential privacy (DP). However, DP training often degrades model utility. In some…
Self-Improving Tabular Language Models via Iterative Reward-Guided Post-Training
Yunbo Long, Tejumade Afonja, Guangya Hao +2
Tabular language models can generate synthetic tables by modeling rows as token sequences, but they are typically trained once with supervised fine-tuning and then used as static s…
Accept or Deny? Evaluating LLM Fairness and Performance in Loan Approval across Table-to-Text Serialization Approaches
Israel Abebe Azime, Deborah D. Kanubala, Tejumade Afonja +4
Large Language Models (LLMs) are increasingly employed in high-stakes decision-making tasks, such as loan approvals. While their applications expand across domains, LLMs struggle t…
The Human Labour of Data Work: Capturing Cultural Diversity through World Wide Dishes
Siobhan Mackenzie Hall, Samantha Dalal, Raesetje Sefala +8
This paper provides guidance for building and maintaining infrastructure for participatory AI efforts by sharing reflections on building World Wide Dishes (WWD), a bottom-up, commu…
DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators
Tejumade Afonja, Hui-Po Wang, Raouf Kerkouche +1
Generating tabular data under differential privacy (DP) protection ensures theoretical privacy guarantees but poses challenges for training machine learning models, primarily due t…
LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation
Tejumade Afonja, Ivaxi Sheth, Ruta Binkyte +4
Gene regulatory networks (GRNs) represent the causal relationships between transcription factors (TFs) and target genes in single-cell RNA sequencing (scRNA-seq) data. Understandin…
1000 African Voices: Advancing inclusive multi-speaker multi-accent speech synthesis
Sewade Ogun, Abraham T. Owodunni, Tobi Olatunji +6
Recent advances in speech synthesis have enabled many useful applications like audio directions in Google Maps, screen readers, and automated content generation on platforms like T…
Performant ASR Models for Medical Entities in Accented Speech
Tejumade Afonja, Tobi Olatunji, Sewade Ogun +3
Recent strides in automatic speech recognition (ASR) have accelerated their application in the medical domain where their performance on accented medical named entities (NE) such a…
The World Wide recipe: A community-centred framework for fine-grained data collection and regional bias operationalisation
Jabez Magomere, Shu Ishida, Tejumade Afonja +11
We introduce the World Wide recipe, which sets forth a framework for culturally aware and participatory data collection, and the resultant regionally diverse World Wide Dishes eval…
Towards Biologically Plausible and Private Gene Expression Data Generation
Dingfan Chen, Marie Oestreich, Tejumade Afonja +3
Generative models trained with Differential Privacy (DP) are becoming increasingly prominent in the creation of synthetic data for downstream applications. Existing literature, how…