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Candice Schumann

4 accepted papers

2025

What Secrets Do Your Manifolds Hold? Understanding the Local Geometry of Generative Models

ICLR 2025poster

Deep Generative Models are frequently used to learn continuous representations of complex data distributions by training on a finite number of samples. For any generative model, including pre-trained foundation models with Diffusion or Transformer architectures, generation performance can significan…

2023

Consensus and Subjectivity of Skin Tone Annotation for ML Fairness

NeurIPS 2023poster

Understanding different human attributes and how they affect model behavior may become a standard need for all model creation and usage, from traditional computer vision tasks to the newest multimodal generative AI systems. In computer vision specifically, we have relied on datasets augmented with p…

Cited by 29SourcePDFScholar
2020

Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics

ICML 2020poster

Bias in machine learning has manifested injustice in several areas, such as medicine, hiring, and criminal justice. In response, computer scientists have developed myriad definitions of fairness to correct this bias in fielded algorithms. While some definitions are based on established legal and eth…

2019

Making the Cut: A Bandit-based Approach to Tiered Interviewing

NeurIPS 2019poster

Given a huge set of applicants, how should a firm allocate sequential resume screenings, phone interviews, and in-person site visits? In a tiered interview process, later stages (e.g., in-person visits) are more informative, but also more expensive than earlier stages (e.g., resume screenings). Us…