1 citations · 1 across the 8 of their papers we have counts for
9 papers
Evaluating the Semantic-to-Geometric Gap in Adversarial Defenses Against Vision-Language Model-Based Plagiarism
Christopher Burger, Christina Trotter, Joseph Carlisle +1
The rapidly advancing capabilities of vision-language models (VLMs) present a systemic challenge to academic integrity. VLMs now allow students to bypass meaningful engagement by c…
Distribution-Free Process Monitoring with Conformal Prediction
Christopher Burger
Traditional Statistical Process Control (SPC) is essential for quality management but is limited by its reliance on often violated statistical assumptions, leading to unreliable mo…
Can AI Recognize Its Own Reflection? Self-Detection Performance of LLMs in Computing Education
Christopher Burger, Karmece Talley, Christina Trotter
The rapid advancement of Large Language Models (LLMs) presents a significant challenge to academic integrity within computing education. As educators seek reliable detection method…
Improving Stability Estimates in Adversarial Explainable AI through Alternate Search Methods
Christopher Burger, Charles Walter
Advances in the effectiveness of machine learning models have come at the cost of enormous complexity resulting in a poor understanding of how they function. Local surrogate method…
Towards Robust and Accurate Stability Estimation of Local Surrogate Models in Text-based Explainable AI
Christopher Burger, Charles Walter, Thai Le +1
Recent work has investigated the concept of adversarial attacks on explainable AI (XAI) in the NLP domain with a focus on examining the vulnerability of local surrogate methods suc…
Quantifying True Robustness: Synonymity-Weighted Similarity for Trustworthy XAI Evaluation
Christopher Burger
Adversarial attacks challenge the reliability of Explainable AI (XAI) by altering explanations while the model's output remains unchanged. The success of these attacks on text-base…
Beyond Individual Facts: Investigating Categorical Knowledge Locality of Taxonomy and Meronomy Concepts in GPT Models
Christopher Burger, Yifan Hu, Thai Le
The location of knowledge within Generative Pre-trained Transformer (GPT)-like models has seen extensive recent investigation. However, much of the work is focused towards determin…
The Effect of Similarity Measures on Accurate Stability Estimates for Local Surrogate Models in Text-based Explainable AI
Christopher Burger, Charles Walter, Thai Le
Recent work has investigated the vulnerability of local surrogate methods to adversarial perturbations on a machine learning (ML) model's inputs, where the explanation is manipulat…
Are Your Explanations Reliable? Investigating the Stability of LIME in Explaining Text Classifiers by Marrying XAI and Adversarial Attack
Christopher Burger, Lingwei Chen, Thai Le
LIME has emerged as one of the most commonly referenced tools in explainable AI (XAI) frameworks that is integrated into critical machine learning applications--e.g., healthcare an…