Candice Schumann

According to our database, Candice Schumann authored at least 15 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Understanding the Local Geometry of Generative Model Manifolds.

Ahmed Imtiaz Humayun, Ibtihel Amara, Candice Schumann, Golnoosh Farnadi, Negar Rostamzadeh, Mohammad Havaei

CoRR, 2024

Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images.

Hansa Srinivasan, Candice Schumann, Aradhana Sinha, David Madras, Gbolahan Oluwafemi Olanubi, Alex Beutel, Susanna Ricco, Jilin Chen

Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024

2023

Consensus and Subjectivity of Skin Tone Annotation for ML Fairness.

Candice Schumann, Gbolahan O. Olanubi, Auriel Wright, Ellis Monk Jr., Courtney Heldreth, Susanna Ricco

CoRR, 2023

Consensus and Subjectivity of Skin Tone Annotation for ML Fairness.

Candice Schumann, Femi Olanubi, Auriel Wright, Ellis Monk Jr., Courtney Heldreth, Susanna Ricco

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Group Fairness in Bandits with Biased Feedback.

Candice Schumann, Zhi Lang, Nicholas Mattei, John P. Dickerson

Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

2021

A Step Toward More Inclusive People Annotations for Fairness.

Candice Schumann, Susanna Ricco, Utsav Prabhu, Vittorio Ferrari, Caroline Pantofaru

Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

2020

Exploring Diversity and Fairness in Machine Learning.

Candice Schumann

PhD thesis, 2020

Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics.

Debjani Saha, Candice Schumann, Duncan C. McElfresh, John P. Dickerson, Michelle L. Mazurek, Michael Carl Tschantz

Proceedings of the 37th International Conference on Machine Learning, 2020

We Need Fairness and Explainability in Algorithmic Hiring.

Candice Schumann, Jeffrey S. Foster, Nicholas Mattei, John P. Dickerson

Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

Human Comprehension of Fairness in Machine Learning.

Debjani Saha, Candice Schumann, Duncan C. McElfresh, John P. Dickerson, Michelle L. Mazurek, Michael Carl Tschantz

Proceedings of the AIES '20: AAAI/ACM Conference on AI, 2020

2019

Group Fairness in Bandit Arm Selection.

Candice Schumann, Zhi Lang, Nicholas Mattei, John P. Dickerson

CoRR, 2019

Transfer of Machine Learning Fairness across Domains.

Candice Schumann, Xuezhi Wang, Alex Beutel, Jilin Chen, Hai Qian, Ed H. Chi

CoRR, 2019

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

Candice Schumann, Zhi Lang, Jeffrey S. Foster, John P. Dickerson

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

The Diverse Cohort Selection Problem.

Candice Schumann, Samsara N. Counts, Jeffrey S. Foster, John P. Dickerson

Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

2017

The Diverse Cohort Selection Problem: Multi-Armed Bandits with Varied Pulls.

Candice Schumann, Samsara N. Counts, Jeffrey S. Foster, John P. Dickerson

CoRR, 2017