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Poster
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Tue 7:00 |
Collaborative Machine Learning with Incentive-Aware Model Rewards Rachael Hwee Ling Sim · Yehong Zhang · Mun Choon Chan · Bryan Kian Hsiang Low |
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Poster
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Tue 7:00 |
Fair Learning with Private Demographic Data Hussein Mozannar · Mesrob Ohannessian · Nati Srebro |
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Poster
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Tue 8:00 |
Feature Noise Induces Loss Discrepancy Across Groups Fereshte Khani · Percy Liang |
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Poster
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Tue 8:00 |
Detecting Out-of-Distribution Examples with Gram Matrices Chandramouli Shama Sastry · Sageev Oore |
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Poster
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Tue 9:00 |
Two Simple Ways to Learn Individual Fairness Metrics from Data Debarghya Mukherjee · Mikhail Yurochkin · Moulinath Banerjee · Yuekai Sun |
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Poster
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Wed 5:00 |
Causal Modeling for Fairness In Dynamical Systems Elliot Creager · David Madras · Toniann Pitassi · Richard Zemel |
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Poster
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Wed 5:00 |
Fair k-Centers via Maximum Matching Matthew Jones · Huy Nguyen · Thy Nguyen |
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Poster
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Wed 5:00 |
Data preprocessing to mitigate bias: A maximum entropy based approach L. Elisa Celis · Vijay Keswani · Nisheeth K. Vishnoi |
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Poster
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Wed 5:00 |
Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics Debjani Saha · Candice Schumann · Duncan McElfresh · John P Dickerson · Michelle Mazurek · Michael Tschantz |
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Poster
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Wed 9:00 |
Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning Esther Rolf · Max Simchowitz · Sarah Dean · Lydia T. Liu · Daniel Bjorkegren · Moritz Hardt · Joshua Blumenstock |
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Poster
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Wed 10:00 |
How to Solve Fair k-Center in Massive Data Models Ashish Chiplunkar · Sagar Kale · Sivaramakrishnan Natarajan Ramamoorthy |
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Poster
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Wed 11:00 |
DeBayes: a Bayesian Method for Debiasing Network Embeddings Maarten Buyl · Tijl De Bie |