no code implementations • 12 Oct 2022 • Shubham Sharma, Alan H. Gee, Jette Henderson, Joydeep Ghosh
The ability to quickly examine combinations of the most promising gradient directions as well as to incorporate additional user-defined constraints allows us to generate multiple counterfactual explanations that are sparse, realistic, and robust to input manipulations.
no code implementations • 13 Oct 2020 • Shubham Sharma, Alan H. Gee, David Paydarfar, Joydeep Ghosh
Fairness in machine learning is crucial when individuals are subject to automated decisions made by models in high-stake domains.
1 code implementation • 18 Apr 2019 • Alan H. Gee, Diego Garcia-Olano, Joydeep Ghosh, David Paydarfar
We improve upon existing models by optimizing for increased prototype diversity and robustness, visualize how these prototypes in the latent space are used by the model to distinguish classes, and show that prototypes are capable of learning features on two dimensional time-series data to produce explainable insights during classification tasks.