21 citations · 38 across the 7 of their papers we have counts for
7 papers
I Bet You Did Not Mean That: Testing Semantic Importance via Betting
Jacopo Teneggi, Jeremias Sulam
Recent works have extended notions of feature importance to semantic concepts that are inherently interpretable to the users interacting with a black-box predictive model. Yet, pre…
Fourier Diffusion Models: A Method to Control MTF and NPS in Score-Based Stochastic Image Generation
Matthew Tivnan, Jacopo Teneggi, Tzu-Cheng Lee +6
Score-based stochastic denoising models have recently been demonstrated as powerful machine learning tools for conditional and unconditional image generation. The existing methods…
How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control
Jacopo Teneggi, Matthew Tivnan, J. Webster Stayman +1
Score-based generative modeling, informally referred to as diffusion models, continue to grow in popularity across several important domains and tasks. While they provide high-qual…
Weakly Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT
Jacopo Teneggi, Paul H. Yi, Jeremias Sulam
Modern machine learning pipelines, in particular those based on deep learning (DL) models, require large amounts of labeled data. For classification problems, the most common learn…
SHAP-XRT: The Shapley Value Meets Conditional Independence Testing
Jacopo Teneggi, Beepul Bharti, Yaniv Romano +1
The complex nature of artificial neural networks raises concerns on their reliability, trustworthiness, and fairness in real-world scenarios. The Shapley value -- a solution concep…
Fast Hierarchical Games for Image Explanations
Jacopo Teneggi, Alexandre Luster, Jeremias Sulam
As modern complex neural networks keep breaking records and solving harder problems, their predictions also become less and less intelligible. The current lack of interpretability…
Fitting Splines to Axonal Arbors Quantifies Relationship between Branch Order and Geometry
Thomas L. Athey, Jacopo Teneggi, Joshua T. Vogelstein +3
Neuromorphology is crucial to identifying neuronal subtypes and understanding learning. It is also implicated in neurological disease. However, standard morphological analysis focu…