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Matthew Tivnan

4 accepted papers

2026

Auditing Sybil: Explaining Deep Lung Cancer Risk Prediction Through Generative Interventional Attributions

ICML 2026poster

Lung cancer remains the leading cause of cancer mortality, driving the development of automated screening tools to alleviate radiologist workload. Standing at the frontier of this effort is Sybil, a deep learning model capable of predicting future risk solely from computed tomography (CT) with high …

Cited by 0SourceScholar
2025

Rethinking Visual Counterfactual Explanations Through Region Constraint

ICLR 2025poster

Visual counterfactual explanations (VCEs) have recently gained immense popularity as a tool for clarifying the decision-making process of image classifiers. This trend is largely motivated by what these explanations promise to deliver -- indicate semantically meaningful factors that change the class…

2025

System-Embedded Diffusion Bridge Models

NeurIPS 2025poster

Solving inverse problems—recovering signals from incomplete or noisy measurements—is fundamental in science and engineering. Score-based generative models (SGMs) have recently emerged as a powerful framework for this task. Two main paradigms have formed: unsupervised approaches that adapt pretrained…

Cited by 0SourceScholar
2023

How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control

ICML 2023poster

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-quality and diverse samples from empirical distributions, important questions remain on the reliability and trustworthiness…