3 citations · 3 across the 7 of their papers we have counts for
7 papers
Interpretable AI with Local Distillation
Erin Craig, Yiling Huang, Snigdha Panigrahi
Modern AI models such as tabular foundation models and gradient-boosted ensembles can outpredict classical methods, but provide little basis for reasoning about their predictions.…
Flexible Inference for Winners with Conditional Validity
Soham Bakshi, Lingjun Gao, Zijun Gao +1
Researchers often select top-performing options or winners, based on a data-driven criterion, such as treatments, models, or model features and then report effect estimates for the…
Post-selection inference for penalized M-estimators via score thinning
Ronan Perry, Snigdha Panigrahi, Daniela Witten
We consider inference for M-estimators after model selection using a sparsity-inducing penalty. While existing methods for this task require bespoke inference procedures, we propos…
Hierarchical Clustering With Confidence
Di Wu, Jacob Bien, Snigdha Panigrahi
Agglomerative hierarchical clustering is one of the most widely used approaches for exploring how observations in a dataset relate to each other. However, its greedy nature makes i…
Classification Trees with Valid Inference via the Exponential Mechanism
Soham Bakshi, Snigdha Panigrahi
Decision trees are widely used for non-linear modeling, as they capture interactions between predictors while producing inherently interpretable models. Despite their popularity, p…
Reluctant Interaction Inference after Additive Modeling
Yiling Huang, Snigdha Panigrahi, Guo Yu +1
Additive models enjoy the flexibility of nonlinear models while still being readily understandable to humans. By contrast, other nonlinear models, which involve interactions betwee…
Inference on the proportion of variance explained in principal component analysis
Ronan Perry, Snigdha Panigrahi, Jacob Bien +1
Principal component analysis (PCA) is a longstanding and well-studied approach for dimension reduction. It rests upon the assumption that the underlying signal in the data has low…