Keegan E. Hines

According to our database, Keegan E. Hines authored at least 18 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Defending Against Indirect Prompt Injection Attacks With Spotlighting.

Keegan Hines, Gary Lopez, Matthew Hall, Federico Zarfati, Yonatan Zunger, Emre Kiciman

CoRR, 2024

2023

Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models.

Jingwei Yi, Yueqi Xie, Bin Zhu, Keegan Hines, Emre Kiciman, Guangzhong Sun, Xing Xie, Fangzhao Wu

CoRR, 2023

Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective.

Avi Schwarzschild, Max Cembalest, Karthik Rao, Keegan Hines, John P. Dickerson

Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 2023

2022

Achieving Downstream Fairness with Geometric Repair.

Kweku Kwegyir-Aggrey, Jessica Dai, John P. Dickerson, Keegan Hines

CoRR, 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

Proceedings of the AIES '22: AAAI/ACM Conference on AI, Ethics, and Society, Oxford, United Kingdom, May 19, 2022

Amortized Generation of Sequential Algorithmic Recourses for Black-Box Models.

Sahil Verma, Keegan Hines, John P. Dickerson

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Counterfactual Explanations for Machine Learning: Challenges Revisited.

Sahil Verma, John P. Dickerson, Keegan Hines

CoRR, 2021

Amortized Generation of Sequential Counterfactual Explanations for Black-box Models.

Sahil Verma, Keegan Hines, John P. Dickerson

CoRR, 2021

2020

Counterfactual Explanations for Machine Learning: A Review.

Sahil Verma, John P. Dickerson, Keegan Hines

CoRR, 2020

Quantifying Challenges in the Application of Graph Representation Learning.

Antonia Gogoglou, C. Bayan Bruss, Brian Nguyen, Reza Sarshogh, Keegan E. Hines

Proceedings of the 19th IEEE International Conference on Machine Learning and Applications, 2020

2019

On the Interpretability and Evaluation of Graph Representation Learning.

Antonia Gogoglou, C. Bayan Bruss, 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

Graph Embeddings at Scale.

C. Bayan Bruss, Anish Khazane, Jonathan Rider, Richard Serpe, Saurabh Nagrecha, Keegan E. Hines

CoRR, 2019

A Multitask Network for Localization and Recognition of Text in Images.

Mohammad Reza Sarshogh, Keegan E. Hines

CoRR, 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

Proceedings of the 31st IEEE International Conference on Tools with Artificial Intelligence, 2019

DeepTrax: Embedding Graphs of Financial Transactions.

Anish Khazane, Jonathan Rider, Max Serpe, Antonia Gogoglou, Keegan E. Hines, C. Bayan Bruss, Richard Serpe

Proceedings of the 18th IEEE International Conference On Machine Learning And Applications, 2019

A Multi-task Network for Localization and Recognition of Text in Images.

Mohammad Reza Sarshogh, Keegan E. Hines

Proceedings of the 2019 International Conference on Document Analysis and Recognition, 2019

2018

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