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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
Tue 7:00 Fair Learning with Private Demographic Data
Hussein Mozannar · Mesrob Ohannessian · Nati Srebro
Tue 8:00 Feature Noise Induces Loss Discrepancy Across Groups
Fereshte Khani · Percy Liang
Tue 8:00 Detecting Out-of-Distribution Examples with Gram Matrices
Chandramouli Shama Sastry · Sageev Oore
Tue 9:00 Two Simple Ways to Learn Individual Fairness Metrics from Data
Debarghya Mukherjee · Mikhail Yurochkin · Moulinath Banerjee · Yuekai Sun
Wed 5:00 Causal Modeling for Fairness In Dynamical Systems
Elliot Creager · David Madras · Toniann Pitassi · Richard Zemel
Wed 5:00 Fair k-Centers via Maximum Matching
Matthew Jones · Huy Nguyen · Thy Nguyen
Wed 5:00 Data preprocessing to mitigate bias: A maximum entropy based approach
L. Elisa Celis · Vijay Keswani · Nisheeth K. Vishnoi
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
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
Wed 10:00 How to Solve Fair k-Center in Massive Data Models
Ashish Chiplunkar · Sagar Kale · Sivaramakrishnan Natarajan Ramamoorthy
Wed 11:00 DeBayes: a Bayesian Method for Debiasing Network Embeddings
Maarten Buyl · Tijl De Bie