Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers

no code implementations • 28 Nov 2022 • Wanqian Yang, Polina Kirichenko, Micah Goldblum, Andrew Gordon Wilson

Deep neural networks are susceptible to shortcut learning, using simple features to achieve low training loss without discovering essential semantic structure.

Incorporating Interpretable Output Constraints in Bayesian Neural Networks

1 code implementation • NeurIPS 2020 • Wanqian Yang, Lars Lorch, Moritz A. Graule, Himabindu Lakkaraju, Finale Doshi-Velez

Domains where supervised models are deployed often come with task-specific constraints, such as prior expert knowledge on the ground-truth function, or desiderata like safety and fairness.

Fairness Uncertainty Quantification

Output-Constrained Bayesian Neural Networks

1 code implementation • 15 May 2019 • Wanqian Yang, Lars Lorch, Moritz A. Graule, Srivatsan Srinivasan, Anirudh Suresh, Jiayu Yao, Melanie F. Pradier, Finale Doshi-Velez

Bayesian neural network (BNN) priors are defined in parameter space, making it hard to encode prior knowledge expressed in function space.