A practical test set bound for rank learning. - Nathan Drenkow; Philippe Burlina; I-Jeng Wang; Daniel DeMenthon; Craig Carmen (MLSP, 2013)

Selection of universal features for image classification. - Pedro A. Rodriguez; Nathan Drenkow; Daniel DeMenthon; Zachary H. Koterba; Kathleen Kauffman; Duane Cornish; Bart L. Paulhamus; R. Jacob Vogelstein (WACV, 2014)

The First International Competition in Machine Reconnaissance Blind Chess. - Ryan W. Gardner; Corey Lowman; Casey Richardson; Ashley J. Llorens; Jared Markowitz; Nathan Drenkow; Andrew Newman; Gregory Clark; Gino Perrotta; Robert Perrotta; Timothy Highley; Vlad Shcherbina; et al. (NeurIPS (Competition and Demos), 2019)

Jacks of All Trades, Masters of None: Addressing Distributional Shift and Obtrusiveness via Transparent Patch Attacks. - Neil Fendley; Max Lennon; I-Jeng Wang; Philippe Burlina; Nathan Drenkow (ECCV Workshops (1), 2020)

Leveraging Tools from Autonomous Navigation for Rapid, Robust Neuron Connectivity. - Nathan Drenkow; Justin Joyce; Jordan Matelsky; Jennifer Heiko; Reem Larabi; Brock A. Wester; Dean Kleissas; William R. Gray Roncal (MICCAI (5), 2020)

Random Projections for Adversarial Attack Detection. - Nathan Drenkow; Neil Fendley; Philippe Burlina (CoRR, 2020)

Objectness-Guided Open Set Visual Search and Closed Set Detection. - Nathan Drenkow; Philippe Burlina; Neil Fendley; Kachi Odoemene; Jared Markowitz (CoRR, 2020)

Addressing Visual Search in Open and Closed Set Settings. - Nathan Drenkow; Philippe Burlina; Neil Fendley; Onyekachi Odoemene; Jared Markowitz (CVPR Workshops, 2021)

Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers. - Zhaoshuo Li; Xingtong Liu; Nathan Drenkow; Andy S. Ding; Francis X. Creighton; Russell H. Taylor; Mathias Unberath (ICCV, 2021)

Patch Attack Invariance: How Sensitive are Patch Attacks to 3D Pose? - Max Lennon; Nathan Drenkow; Philippe Burlina (ICCVW, 2021)

On the Sins of Image Synthesis Loss for Self-supervised Depth Estimation. - Zhaoshuo Li; Nathan Drenkow; Hao Ding; Andy S. Ding; Alexander Lu; Francis X. Creighton; Russell H. Taylor; Mathias Unberath (CoRR, 2021)

Robustness in Deep Learning for Computer Vision: Mind the gap? - Nathan Drenkow; Numair Sani; Ilya Shpitser; Mathias Unberath (CoRR, 2021)

Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis. - Nathan Drenkow; Neil Fendley; Philippe Burlina (WACV, 2022)

Context-Adaptive Deep Neural Networks via Bridge-Mode Connectivity. - Nathan Drenkow; Alvin Tan; Chace Ashcraft; Kiran Karra (CoRR, 2022)

Data AUDIT: Identifying Attribute Utility- and Detectability-Induced Bias in Task Models. - Mitchell Pavlak; Nathan Drenkow; Nicholas Petrick; Mohammad Mehdi Farhangi; Mathias Unberath (MICCAI (3), 2023)

A sparse null code emerges in deep neural networks. - Brian S. Robinson; Nathan Drenkow; Colin Conwell; Michael F. Bonner (UniReps, 2023)

Do Adaptive Active Attacks Pose Greater Risk Than Static Attacks? - Nathan Drenkow; Max Lennon; I-Jeng Wang; Philippe Burlina (WACV, 2023)

Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence. - Erik C. Johnson; Brian S. Robinson; Gautam K. Vallabha; Justin Joyce; Jordan K. Matelsky; Raphael Norman-Tenazas; Isaac Western; Marisel Villafañe-Delgado; Martha Cervantes; Michael S. Robinette; Arun V. Reddy; Lindsey Kitchell; et al. (CoRR, 2023)

RobustCLEVR: A Benchmark and Framework for Evaluating Robustness in Object-centric Learning. - Nathan Drenkow; Mathias Unberath (WACV, 2024)

From Generalization to Precision: Exploring SAM for Tool Segmentation in Surgical Environments. - Kanyifeechukwu Jane Oguine; Roger D. Soberanis-Mukul; Nathan Drenkow; Mathias Unberath (CoRR, 2024)

Causality-Driven Audits of Model Robustness. - Nathan Drenkow; Chris Ribaudo; Mathias Unberath (CoRR, 2024)

Towards Virtual Clinical Trials of Radiology AI with Conditional Generative Modeling. - Benjamin D. Killeen; Bohua Wan; Aditya V. Kulkarni; Nathan Drenkow; Michael Oberst; Paul H. Yi; Mathias Unberath (CoRR, 2025)

A Causal Framework for Aligning Image Quality Metrics and Deep Neural Network Robustness. - Nathan Drenkow; Mathias Unberath (CoRR, 2025)

Detecting Dataset Bias in Medical AI: A Generalized and Modality-Agnostic Auditing Framework. - Nathan Drenkow; Mitchell Pavlak; Keith Harrigian; Ayah Zirikly; Adarsh Subbaswamy; Mathias Unberath (CoRR, 2025)

Investigating the Treacherous Turn in Deep Reinforcement Learning. - Chace Ashcraft; Kiran Karra; Josh Carney; Nathan Drenkow (CoRR, 2025)

Backdoors in DRL: Four Environments Focusing on In-distribution Triggers. - Chace Ashcraft; Ted Staley; Josh Carney; Cameron Hickert; Kiran Karra; Nathan Drenkow (CoRR, 2025)

A Systematic Review of Poisoning Attacks Against Large Language Models. - Neil Fendley; Edward W. Staley; Joshua Carney; William Redman; Marie Chau; Nathan Drenkow (CoRR, 2025)