65 citations · 145 across the 28 of their papers we have counts for
6 papers · 1 filter
Strong Model Collapse
Elvis Dohmatob, Yunzhen Feng, Arjun Subramonian +1
Within the scaling laws paradigm, which underpins the training of large neural networks like ChatGPT and Llama, we consider a supervised regression setting and establish the exista…
An Effective Theory of Bias Amplification
Arjun Subramonian, Samuel J. Bell, Levent Sagun +1
Machine learning models can capture and amplify biases present in data, leading to disparate test performance across social groups. To better understand, evaluate, and mitigate the…
Understanding "Democratization" in NLP and ML Research
Arjun Subramonian, Vagrant Gautam, Dietrich Klakow +1
Recent improvements in natural language processing (NLP) and machine learning (ML) and increased mainstream adoption have led to researchers frequently discussing the "democratizat…
Stop! In the Name of Flaws: Disentangling Personal Names and Sociodemographic Attributes in NLP
Vagrant Gautam, Arjun Subramonian, Anne Lauscher +1
Personal names simultaneously differentiate individuals and categorize them in ways that are important in a given society. While the natural language processing community has thus…
Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation
Yixin Wan, Arjun Subramonian, Anaelia Ovalle +6
The recent advancement of large and powerful models with Text-to-Image (T2I) generation abilities -- such as OpenAI's DALLE-3 and Google's Gemini -- enables users to generate high-…
Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks
Arjun Subramonian, Jian Kang, Yizhou Sun
Graph Neural Networks (GNNs) often perform better for high-degree nodes than low-degree nodes on node classification tasks. This degree bias can reinforce social marginalization by…