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2020 – today
- 2024
[c9]Hansa Srinivasan
, Candice Schumann, Aradhana Sinha, David Madras, Gbolahan Oluwafemi Olanubi, Alex Beutel, Susanna Ricco, Jilin Chen:
Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images. FAccT 2024: 797-821- [i9]Hansa Srinivasan, Candice Schumann, Aradhana Sinha, David Madras, Gbolahan Oluwafemi Olanubi, Alex Beutel, Susanna Ricco, Jilin Chen:
Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images. CoRR abs/2401.14322 (2024) - [i8]Ahmed Imtiaz Humayun, Ibtihel Amara, Candice Schumann, Golnoosh Farnadi, Negar Rostamzadeh, Mohammad Havaei:
Understanding the Local Geometry of Generative Model Manifolds. CoRR abs/2408.08307 (2024) - 2023
- [c8]Candice Schumann, Femi Olanubi, Auriel Wright, Ellis Monk Jr., Courtney Heldreth, Susanna Ricco:
Consensus and Subjectivity of Skin Tone Annotation for ML Fairness. NeurIPS 2023 - [i7]Candice Schumann, Gbolahan O. Olanubi, Auriel Wright, Ellis Monk Jr., Courtney Heldreth, Susanna Ricco:
Consensus and Subjectivity of Skin Tone Annotation for ML Fairness. CoRR abs/2305.09073 (2023) - 2022
- [c7]Candice Schumann, Zhi Lang, Nicholas Mattei, John P. Dickerson:
Group Fairness in Bandits with Biased Feedback. AAMAS 2022: 1155-1163 - 2021
- [c6]Candice Schumann, Susanna Ricco, Utsav Prabhu, Vittorio Ferrari, Caroline Pantofaru:
A Step Toward More Inclusive People Annotations for Fairness. AIES 2021: 916-925 - [i6]Candice Schumann, Susanna Ricco, Utsav Prabhu, Vittorio Ferrari, Caroline Pantofaru:
A Step Toward More Inclusive People Annotations for Fairness. CoRR abs/2105.02317 (2021) - 2020
- [b1]Candice Schumann:
Exploring Diversity and Fairness in Machine Learning. University of Maryland, College Park, MD, USA, 2020 - [c5]Debjani Saha, Candice Schumann, Duncan C. McElfresh, John P. Dickerson, Michelle L. Mazurek, Michael Carl Tschantz:
Human Comprehension of Fairness in Machine Learning. AIES 2020: 152 - [c4]Candice Schumann, Jeffrey S. Foster, Nicholas Mattei, John P. Dickerson:
We Need Fairness and Explainability in Algorithmic Hiring. AAMAS 2020: 1716-1720 - [c3]Debjani Saha, Candice Schumann, Duncan C. McElfresh, John P. Dickerson, Michelle L. Mazurek, Michael Carl Tschantz:
Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics. ICML 2020: 8377-8387 - [i5]Debjani Saha, Candice Schumann, Duncan C. McElfresh, John P. Dickerson, Michelle L. Mazurek, Michael Carl Tschantz:
Human Comprehension of Fairness in Machine Learning. CoRR abs/2001.00089 (2020)
2010 – 2019
- 2019
- [c2]Candice Schumann, Samsara N. Counts, Jeffrey S. Foster, John P. Dickerson:
The Diverse Cohort Selection Problem. AAMAS 2019: 601-609 - [c1]Candice Schumann, Zhi Lang, Jeffrey S. Foster, John P. Dickerson:
Making the Cut: A Bandit-based Approach to Tiered Interviewing. NeurIPS 2019: 4641-4651 - [i4]Candice Schumann, Zhi Lang, Jeffrey S. Foster, John P. Dickerson:
Making the Cut: A Bandit-based Approach to Tiered Interviewing. CoRR abs/1906.09621 (2019) - [i3]Candice Schumann, Xuezhi Wang, Alex Beutel, Jilin Chen, Hai Qian, Ed H. Chi:
Transfer of Machine Learning Fairness across Domains. CoRR abs/1906.09688 (2019) - [i2]Candice Schumann, Zhi Lang, Nicholas Mattei, John P. Dickerson:
Group Fairness in Bandit Arm Selection. CoRR abs/1912.03802 (2019) - 2017
- [i1]Candice Schumann, Samsara N. Counts, Jeffrey S. Foster, John P. Dickerson:
The Diverse Cohort Selection Problem: Multi-Armed Bandits with Varied Pulls. CoRR abs/1709.03441 (2017)
Coauthor Index
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