Search Results for author:
Found 6 papers, 1 papers with code
TextGenSHAP: Scalable Post-hoc Explanations in Text Generation with Long Documents
Given their nature as black-boxes using complex reasoning processes on their inputs, it is inevitable that the demand for scalable and faithful explanations for LLMs' generated content will continue to grow.
Measuring, Interpreting, and Improving Fairness of Algorithms using Causal Inference and Randomized Experiments
Algorithm fairness has become a central problem for the broad adoption of artificial intelligence.
Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders
Causal analysis for time series data, in particular estimating individualized treatment effect (ITE), is a key task in many real-world applications, such as finance, retail, healthcare, etc.
Estimating Treatment Effects in Continuous Time with Hidden Confounders
Estimating treatment effects plays a crucial role in causal inference, having many real-world applications like policy analysis and decision making.
Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection
There is currently a large gap in performance between the statistically rigorous methods like linear regression or additive splines and the powerful deep methods using neural networks.
Interpretable Artificial Intelligence through the Lens of Feature Interaction
Interpretation of deep learning models is a very challenging problem because of their large number of parameters, complex connections between nodes, and unintelligible feature representations.
