activity
20162024
most citedCLIP the Bias: How Useful is Balancing Data in Multimodal Learning?

3 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2024

Proxy Methods for Domain Adaptation

Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen +5

We study the problem of domain adaptation under distribution shift, where the shift is due to a change in the distribution of an unobserved, latent variable that confounds both the…

cs.LG20243 cited

CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?

Ibrahim Alabdulmohsin, Xiao Wang, Andreas Steiner +3

We study the effectiveness of data-balancing for mitigating biases in contrastive language-image pretraining (CLIP), identifying areas of strength and limitation. First, we reaffir…

cs.LG2023

When does Privileged Information Explain Away Label Noise?

Guillermo Ortiz-Jimenez, Mark Collier, Anant Nawalgaria +4

Leveraging privileged information (PI), or features available during training but not at test time, has recently been shown to be an effective method for addressing label noise. Ho…

cs.LG20221 cited

Boosting the interpretability of clinical risk scores with intervention predictions

Eric Loreaux, Ke Yu, Jonas Kemp +8

Machine learning systems show significant promise for forecasting patient adverse events via risk scores. However, these risk scores implicitly encode assumptions about future inte…

stat.AP2016

Meta-Analytics: Tools for Understanding the Statistical Properties of Sports Metrics

Alexander Franks, Alexander D'Amour, Daniel Cervone +1

In sports, there is a constant effort to improve metrics which assess player ability, but there has been almost no effort to quantify and compare existing metrics. Any individual m…