1 citations · 5 across the 13 of their papers we have counts for
5 papers · 1 filter
Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat
Shantanu Ghosh, Ke Yu, Forough Arabshahi +1
ML model design either starts with an interpretable model or a Blackbox and explains it post hoc. Blackbox models are flexible but difficult to explain, while interpretable models…
Exploring the Lottery Ticket Hypothesis with Explainability Methods: Insights into Sparse Network Performance
Shantanu Ghosh, Kayhan Batmanghelich
Discovering a high-performing sparse network within a massive neural network is advantageous for deploying them on devices with limited storage, such as mobile phones. Additionally…
Distilling BlackBox to Interpretable models for Efficient Transfer Learning
Shantanu Ghosh, Ke Yu, Kayhan Batmanghelich
Building generalizable AI models is one of the primary challenges in the healthcare domain. While radiologists rely on generalizable descriptive rules of abnormality, Neural Networ…
DR-VIDAL -- Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data
Shantanu Ghosh, Zheng Feng, Jiang Bian +2
Determining causal effects of interventions onto outcomes from real-world, observational (non-randomized) data, e.g., treatment repurposing using electronic health records, is chal…
Tackling Shortcut Learning in Deep Neural Networks: An Iterative Approach with Interpretable Models
Shantanu Ghosh, Ke Yu, Forough Arabshahi +1
We use concept-based interpretable models to mitigate shortcut learning. Existing methods lack interpretability. Beginning with a Blackbox, we iteratively carve out a mixture of in…