Anomaly Detection in Cyber Network Data Using a Cyber Language Approach. - Bartley D. Richardson; Benjamin J. Radford; Shawn E. Davis; Keegan E. Hines; David Pekarek (CoRR, 2018)

A Multi-task Network for Localization and Recognition of Text in Images. - Mohammad Reza Sarshogh; Keegan E. Hines (ICDAR, 2019)

DeepTrax: Embedding Graphs of Financial Transactions. - Anish Khazane; Jonathan Rider; Max Serpe; Antonia Gogoglou; Keegan E. Hines; C. Bayan Bruss; Richard Serpe (ICMLA, 2019)

Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools. - Anh Truong; Austin Walters; Jeremy Goodsitt; Keegan E. Hines; C. Bayan Bruss; Reza Farivar (ICTAI, 2019)

A Multitask Network for Localization and Recognition of Text in Images. - Mohammad Reza Sarshogh; Keegan E. Hines (CoRR, 2019)

Graph Embeddings at Scale. - C. Bayan Bruss; Anish Khazane; Jonathan Rider; Richard Serpe; Saurabh Nagrecha; Keegan E. Hines (CoRR, 2019)

DeepTrax: Embedding Graphs of Financial Transactions. - C. Bayan Bruss; Anish Khazane; Jonathan Rider; Richard Serpe; Antonia Gogoglou; Keegan E. Hines (CoRR, 2019)

On the Interpretability and Evaluation of Graph Representation Learning. - Antonia Gogoglou; C. Bayan Bruss; Keegan E. Hines (CoRR, 2019)

Quantifying Challenges in the Application of Graph Representation Learning. - Antonia Gogoglou; C. Bayan Bruss; Brian Nguyen; Reza Sarshogh; Keegan E. Hines (ICMLA, 2020)

Counterfactual Explanations for Machine Learning: A Review. - Sahil Verma; John P. Dickerson; Keegan Hines (CoRR, 2020)

Amortized Generation of Sequential Counterfactual Explanations for Black-box Models. - Sahil Verma; Keegan Hines; John P. Dickerson (CoRR, 2021)

Counterfactual Explanations for Machine Learning: Challenges Revisited. - Sahil Verma; John P. Dickerson; Keegan Hines (CoRR, 2021)

Amortized Generation of Sequential Algorithmic Recourses for Black-Box Models. - Sahil Verma; Keegan Hines; John P. Dickerson (AAAI, 2022)

Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation. - I. Elizabeth Kumar; Keegan E. Hines; John P. Dickerson (AIES, 2022)

Achieving Downstream Fairness with Geometric Repair. - Kweku Kwegyir-Aggrey; Jessica Dai; John P. Dickerson; Keegan Hines (CoRR, 2022)

Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective. - Avi Schwarzschild; Max Cembalest; Karthik Rao; Keegan Hines; John P. Dickerson (AIES, 2023)

Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models. - Jingwei Yi; Yueqi Xie; Bin Zhu 008; Keegan Hines; Emre Kiciman; Guangzhong Sun; Xing Xie 001; Fangzhao Wu (CoRR, 2023)

Defending Against Indirect Prompt Injection Attacks With Spotlighting. - Keegan Hines; Gary Lopez; Matthew Hall; Federico Zarfati; Yonatan Zunger; Emre Kiciman (CAMLIS, 2024)

Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review. - Sahil Verma; Varich Boonsanong; Minh Hoang; Keegan Hines; John Dickerson 001; Chirag Shah 001 (ACM Comput. Surv., 2024)

Lessons From Red Teaming 100 Generative AI Products. - Blake Bullwinkel; Amanda J. Minnich; Shiven Chawla; Gary Lopez; Martin Pouliot; Whitney Maxwell; Joris de Gruyter; Katherine Pratt; Saphir Qi; Nina Chikanov; Roman Lutz; Raja Sekhar Rao Dheekonda; et al. (CoRR, 2025)

OMNIGUARD: An Efficient Approach for AI Safety Moderation Across Modalities. - Sahil Verma; Keegan Hines; Jeff Bilmes; Charlotte Siska; Luke Zettlemoyer; Hila Gonen; Chandan Singh (CoRR, 2025)

A Representation Engineering Perspective on the Effectiveness of Multi-Turn Jailbreaks. - Blake Bullwinkel; Mark Russinovich; Ahmed Salem 001; Santiago Zanella-Béguelin; Daniel Jones; Giorgio Severi; Eugenia Kim; Keegan Hines; Amanda J. Minnich; Yonatan Zunger; Ram Shankar Siva Kumar (CoRR, 2025)