29 Results

Poster
Tue 7:00 Fair Generative Modeling via Weak Supervision
Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, Stefano Ermon
Poster
Tue 7:00 Collaborative Machine Learning with Incentive-Aware Model Rewards
Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan, Bryan Kian Hsiang Low
Poster
Tue 7:00 Individual Fairness for k-Clustering
Sepideh Mahabadi, Ali Vakilian
Poster
Tue 7:00 Fair Learning with Private Demographic Data
Hussein Mozannar, Mesrob Ohannessian, Nati Srebro
Poster
Tue 7:00 Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach
Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard Zemel, Craig Boutilier
Poster
Tue 8:00 Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards
Umer Siddique, Paul Weng, Matthieu Zimmer
Poster
Tue 8:00 Feature Noise Induces Loss Discrepancy Across Groups
Fereshte Khani, Percy Liang
Poster
Tue 9:00 Fiduciary Bandits
Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown, Moshe Tennenholtz
Poster
Tue 9:00 Two Simple Ways to Learn Individual Fairness Metrics from Data
Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun
Poster
Wed 5:00 Fair k-Centers via Maximum Matching
Matthew Jones, Huy Nguyen, Thy Nguyen
Poster
Wed 5:00 Causal Modeling for Fairness In Dynamical Systems
Elliot Creager, David Madras, Toniann Pitassi, Richard Zemel
Poster
Wed 5:00 Data preprocessing to mitigate bias: A maximum entropy based approach
L. Elisa Celis, Vijay Keswani, Nisheeth K. Vishnoi
Poster
Wed 5:00 Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective
Ruixiang ZHANG, Masanori Koyama, Katsuhiko Ishiguro
Poster
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
Poster
Wed 8:00 Optimizing Black-box Metrics with Adaptive Surrogates
Qijia Jiang, Olaoluwa Adigun, Harikrishna Narasimhan, Mahdi Milani Fard, Maya Gupta
Poster
Wed 8:00 Individual Calibration with Randomized Forecasting
Shengjia Zhao, Tengyu Ma, Stefano Ermon
Poster
Wed 8:00 Predictive Multiplicity in Classification
Charles Marx, Flavio Calmon, Berk Ustun
Poster
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
Poster
Wed 10:00 How to Solve Fair k-Center in Massive Data Models
Ashish Chiplunkar, Sagar Kale, Sivaramakrishnan Natarajan Ramamoorthy
Poster
Wed 11:00 DeBayes: a Bayesian Method for Debiasing Network Embeddings
Maarten Buyl, Tijl De Bie
Poster
Wed 12:00 Too Relaxed to Be Fair
Michael Lohaus, Michaƫl Perrot, Ulrike von Luxburg
Poster
Wed 13:00 Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge
Laura Rieger, Chandan Singh, William Murdoch, Bin Yu
Poster
Thu 6:00 A Pairwise Fair and Community-preserving Approach to k-Center Clustering
Brian Brubach, Darshan Chakrabarti, John P Dickerson, Samir Khuller, Aravind Srinivasan, Leonidas Tsepenekas
Poster
Thu 6:00 FR-Train: A Mutual Information-Based Approach to Fair and Robust Training
Yuji Roh, Kangwook Lee, Steven Whang, Changho Suh
Poster
Thu 6:00 FACT: A Diagnostic for Group Fairness Trade-offs
Joon Kim, Jiahao Chen, Ameet Talwalkar
Poster
Thu 7:00 Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing
Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, Kush Varshney
Poster
Thu 8:00 Minimax Pareto Fairness: A Multi Objective Perspective
Natalia Martinez Gil, Martin Bertran, Guillermo Sapiro
Poster
Thu 8:00 Generalized and Scalable Optimal Sparse Decision Trees
Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, Margo Seltzer
Poster
Thu 9:00 Bounding the fairness and accuracy of classifiers from population statistics
Sivan Sabato, Elad Yom-Tov