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11/05/2019
Practical Compositional Fairness: Understanding Fairness in Multi-Task ML Systems
Most literature in fairness has focused on improving fairness with respe...
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10/25/2019
Toward a better trade-off between performance and fairness with kernel-based distribution matching
As recent literature has demonstrated how classifiers often carry uninte...
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06/24/2019
Transfer of Machine Learning Fairness across Domains
If our models are used in new or unexpected cases, do we know if they wi...
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05/23/2019
Quantifying Long Range Dependence in Language and User Behavior to improve RNNs
Characterizing temporal dependence patterns is a critical step in unders...
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03/02/2019
Fairness in Recommendation Ranking through Pairwise Comparisons
Recommender systems are one of the most pervasive applications of machin...
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02/26/2019
AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks
Recurrent neural networks have gained widespread use in modeling sequent...
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02/22/2019
Towards Neural Mixture Recommender for Long Range Dependent User Sequences
Understanding temporal dynamics has proved to be highly valuable for acc...
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01/25/2019
Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs
Training recurrent neural networks (RNNs) on long sequence tasks is plag...
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01/14/2019
Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements
As more researchers have become aware of and passionate about algorithmi...
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09/27/2018
Counterfactual Fairness in Text Classification through Robustness
In this paper, we study counterfactual fairness in text classification, ...
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12/04/2017
