6 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.…
Preventing Model Collapse via Contraction-Conditioned Neural Filters
Zongjian Han, Yiran Liang, Ruiwen Wang +4
This paper presents a neural network filter method based on contraction operators to address model collapse in recursive training of generative models. Unlike \cite{xu2024probabili…
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 with Randomized Regression Trees
Soham Bakshi, Yiling Huang, Snigdha Panigrahi +1
Regression trees are a popular machine learning algorithm that fit piecewise constant models by recursively partitioning the predictor space. This paper focuses on statistical infe…
Selective Inference for Sparse Graphs via Neighborhood Selection
Yiling Huang, Snigdha Panigrahi, Walter Dempsey
Neighborhood selection is a widely used method used for estimating the support set of sparse precision matrices, which helps determine the conditional dependence structure in undir…
Selective inference using randomized group lasso estimators for general models
Yiling Huang, Sarah Pirenne, Snigdha Panigrahi +1
Selective inference methods are developed for group lasso estimators for use with a wide class of distributions and loss functions. The method includes the use of exponential famil…