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Amanda Coston
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2020 – today
- 2024
- [j2]George H. Chen
, Linhong Li, Ren Zuo
Neural topic models with survival supervision: Jointly predicting time-to-event outcomes and learning how clinical features relate. Artif. Intell. Medicine 154: 102898 (2024) - [j1]Anna Kawakami
Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Use. Proc. ACM Hum. Comput. Interact. 8(CSCW2): 1-24 (2024) - [c14]Anna Kawakami
The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder, Early-stage Deliberations Around Public Sector AI Proposals. CHI 2024: 749:1-749:22 - [c13]Luke Guerdan, Amanda Coston, Ken Holstein, Steven Wu:
Predictive Performance Comparison of Decision Policies Under Confounding. ICML 2024 - [i17]Anna Kawakami, Amanda Coston, Haiyi Zhu, Hoda Heidari, Kenneth Holstein:
The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder Early-stage Deliberations Around Public Sector AI Proposals. CoRR abs/2402.18774 (2024) - [i16]Luke Guerdan, Amanda Coston, Kenneth Holstein, Zhiwei Steven Wu:
Predictive Performance Comparison of Decision Policies Under Confounding. CoRR abs/2404.00848 (2024) - [i15]Anna Kawakami, Amanda Coston, Hoda Heidari, Kenneth Holstein, Haiyi Zhu:
Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Use. CoRR abs/2405.12458 (2024) - 2023
- [c12]Luke Guerdan
Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making. FAccT 2023: 688-704 - [c11]Anjalie Field
Examining risks of racial biases in NLP tools for child protective services. FAccT 2023: 1479-1492 - [c10]Luke Guerdan
Counterfactual Prediction Under Outcome Measurement Error. FAccT 2023: 1584-1598 - [c9]Amanda Coston, Anna Kawakami, Haiyi Zhu
A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms. SaTML 2023: 690-704 - [i14]Luke Guerdan, Amanda Coston, Zhiwei Steven Wu, Kenneth Holstein:
Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making. CoRR abs/2302.06503 (2023) - [i13]Luke Guerdan, Amanda Coston, Kenneth Holstein, Zhiwei Steven Wu:
Counterfactual Prediction Under Outcome Measurement Error. CoRR abs/2302.11121 (2023) - [i12]Anna Kawakami, Amanda Coston, Haiyi Zhu, Hoda Heidari, Kenneth Holstein:
Recentering Validity Considerations through Early-Stage Deliberations Around AI and Policy Design. CoRR abs/2303.14602 (2023) - [i11]Anjalie Field, Amanda Coston, Nupoor Gandhi, Alexandra Chouldechova, Emily Putnam-Hornstein, David Steier, Yulia Tsvetkov:
Examining risks of racial biases in NLP tools for child protective services. CoRR abs/2305.19409 (2023) - 2022
- [i10]Amanda Coston, Anna Kawakami, Haiyi Zhu, Ken Holstein, Hoda Heidari:
A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms. CoRR abs/2206.14983 (2022) - [i9]Ashesh Rambachan, Amanda Coston, Edward H. Kennedy:
Counterfactual Risk Assessments under Unmeasured Confounding. CoRR abs/2212.09844 (2022) - 2021
- [c8]Amanda Coston
Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy. FAccT 2021: 173-184 - [c7]Amanda Coston, Ashesh Rambachan, Alexandra Chouldechova:
Characterizing Fairness Over the Set of Good Models Under Selective Labels. ICML 2021: 2144-2155 - [i8]Amanda Coston, Ashesh Rambachan, Alexandra Chouldechova:
Characterizing Fairness Over the Set of Good Models Under Selective Labels. CoRR abs/2101.00352 (2021) - 2020
- [c6]Linhong Li
Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features Relate. AIME 2020: 371-381 - [c5]Amanda Coston
Counterfactual risk assessments, evaluation, and fairness. FAT* 2020: 582-593 - [c4]Han Zhao, Amanda Coston, Tameem Adel, Geoffrey J. Gordon:
Conditional Learning of Fair Representations. ICLR 2020 - [c3]Amanda Coston, Edward H. Kennedy, Alexandra Chouldechova:
Counterfactual Predictions under Runtime Confounding. NeurIPS 2020 - [i7]Maria De-Arteaga, Tejumade Afonja, Amanda Coston:
Proceedings of NeurIPS 2019 Workshop on Machine Learning for the Developing World: Challenges and Risks of ML4D. CoRR abs/2001.00249 (2020) - [i6]Amanda Coston, Edward H. Kennedy, Alexandra Chouldechova:
Counterfactual Predictions under Runtime Confounding. CoRR abs/2006.16916 (2020) - [i5]Linhong Li, Ren Zuo, Amanda Coston, Jeremy C. Weiss, George H. Chen:
Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features Relate. CoRR abs/2007.07796 (2020) - [i4]Amanda Coston, Neel Guha, Derek Ouyang, Lisa Lu, Alexandra Chouldechova, Daniel E. Ho:
Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy. CoRR abs/2011.07194 (2020)
2010 – 2019
- 2019
- [c2]Amanda Coston
Fair Transfer Learning with Missing Protected Attributes. AIES 2019: 91-98 - [c1]Amanda Coston:
Risk Assessments and Fairness Under Missingness and Confounding. AIES 2019: 531 - [i3]Amanda Coston, Alexandra Chouldechova, Edward H. Kennedy:
Counterfactual Risk Assessments, Evaluation, and Fairness. CoRR abs/1909.00066 (2019) - [i2]Han Zhao, Amanda Coston, Tameem Adel, Geoffrey J. Gordon:
Conditional Learning of Fair Representations. CoRR abs/1910.07162 (2019) - 2018
- [i1]Maria De-Arteaga, Amanda Coston, William Herlands:
Proceedings of NeurIPS 2018 Workshop on Machine Learning for the Developing World: Achieving Sustainable Impact. CoRR abs/1812.10398 (2018)
Coauthor Index
aka: Ken Holstein
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last updated on 2024-12-02 22:27 CET by the dblp team
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