3.2k citations · 4.3k across the 100 of their papers we have counts for
14 papers · 1 filter
Prediction-Constrained Topic Models for Antidepressant Recommendation
Michael C. Hughes, Gabriel Hope, Leah Weiner +4
Supervisory signals can help topic models discover low-dimensional data representations that are more interpretable for clinical tasks. We propose a framework for training supervis…
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients
Andrew Slavin Ross, Finale Doshi-Velez
Deep neural networks have proven remarkably effective at solving many classification problems, but have been criticized recently for two major weaknesses: the reasons behind their…
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
Mike Wu, Michael C. Hughes, Sonali Parbhoo +3
The lack of interpretability remains a key barrier to the adoption of deep models in many applications. In this work, we explicitly regularize deep models so human users might step…
Accountability of AI Under the Law: The Role of Explanation
Finale Doshi-Velez, Mason Kortz, Ryan Budish +9
The ubiquity of systems using artificial intelligence or "AI" has brought increasing attention to how those systems should be regulated. The choice of how to regulate AI systems wi…
Weighted Tensor Decomposition for Learning Latent Variables with Partial Data
Omer Gottesman, Weiwei Pan, Finale Doshi-Velez
Tensor decomposition methods are popular tools for learning latent variables given only lower-order moments of the data. However, the standard assumption is that we have sufficient…
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez +1
Bayesian neural networks with latent variables are scalable and flexible probabilistic models: They account for uncertainty in the estimation of the network weights and, by making…