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ORCIDKeegan E. Hines
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2020 – today
- 2024
[i15]Keegan Hines, Gary Lopez, Matthew Hall, Federico Zarfati, Yonatan Zunger, Emre Kiciman:
Defending Against Indirect Prompt Injection Attacks With Spotlighting. CoRR abs/2403.14720 (2024)- 2023
- [c7]Avi Schwarzschild
, Max Cembalest, Karthik Rao, Keegan Hines, John P. Dickerson:
Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective. AIES 2023: 662-678 - [i14]Avi Schwarzschild, Max Cembalest, Karthik Rao, Keegan Hines, John P. Dickerson:
Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective. CoRR abs/2303.13299 (2023) - [i13]Jingwei Yi, Yueqi Xie, Bin Zhu, Keegan Hines, Emre Kiciman, Guangzhong Sun, Xing Xie, Fangzhao Wu:
Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models. CoRR abs/2312.14197 (2023) - 2022
- [c6]Sahil Verma, Keegan Hines, John P. Dickerson:
Amortized Generation of Sequential Algorithmic Recourses for Black-Box Models. AAAI 2022: 8512-8519 - [c5]I. Elizabeth Kumar, Keegan E. Hines, John P. Dickerson:
Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation. AIES 2022: 357-368 - [i12]Kweku Kwegyir-Aggrey, Jessica Dai, John P. Dickerson, Keegan Hines:
Achieving Downstream Fairness with Geometric Repair. CoRR abs/2203.07490 (2022) - [i11]I. Elizabeth Kumar, Keegan E. Hines, John P. Dickerson:
Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation. CoRR abs/2210.02516 (2022) - 2021
- [i10]Sahil Verma, Keegan Hines, John P. Dickerson:
Amortized Generation of Sequential Counterfactual Explanations for Black-box Models. CoRR abs/2106.03962 (2021) - [i9]Sahil Verma, John P. Dickerson, Keegan Hines:
Counterfactual Explanations for Machine Learning: Challenges Revisited. CoRR abs/2106.07756 (2021) - 2020
- [c4]Antonia Gogoglou, C. Bayan Bruss, Brian Nguyen, Reza Sarshogh, Keegan E. Hines:
Quantifying Challenges in the Application of Graph Representation Learning. ICMLA 2020: 1519-1526 - [i8]Antonia Gogoglou, C. Bayan Bruss, Brian Nguyen, Reza Sarshogh, Keegan E. Hines:
Quantifying Challenges in the Application of Graph Representation Learning. CoRR abs/2006.10252 (2020) - [i7]Sahil Verma, John P. Dickerson, Keegan Hines:
Counterfactual Explanations for Machine Learning: A Review. CoRR abs/2010.10596 (2020)
2010 – 2019
- 2019
- [c3]Mohammad Reza Sarshogh, Keegan E. Hines:
A Multi-task Network for Localization and Recognition of Text in Images. ICDAR 2019: 494-501 - [c2]Anish Khazane, Jonathan Rider, Max Serpe, Antonia Gogoglou, Keegan E. Hines, C. Bayan Bruss, Richard Serpe:
DeepTrax: Embedding Graphs of Financial Transactions. ICMLA 2019: 126-133 - [c1]Anh Truong, Austin Walters, Jeremy Goodsitt, Keegan E. Hines, C. Bayan Bruss, Reza Farivar:
Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools. ICTAI 2019: 1471-1479 - [i6]Mohammad Reza Sarshogh, Keegan E. Hines:
A Multitask Network for Localization and Recognition of Text in Images. CoRR abs/1906.09266 (2019) - [i5]C. Bayan Bruss, Anish Khazane, Jonathan Rider, Richard Serpe, Saurabh Nagrecha, Keegan E. Hines:
Graph Embeddings at Scale. CoRR abs/1907.01705 (2019) - [i4]C. Bayan Bruss, Anish Khazane, Jonathan Rider, Richard Serpe, Antonia Gogoglou, Keegan E. Hines:
DeepTrax: Embedding Graphs of Financial Transactions. CoRR abs/1907.07225 (2019) - [i3]Anh Truong, Austin Walters, Jeremy Goodsitt, Keegan E. Hines, C. Bayan Bruss, Reza Farivar:
Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools. CoRR abs/1908.05557 (2019) - [i2]Antonia Gogoglou, C. Bayan Bruss, Keegan E. Hines:
On the Interpretability and Evaluation of Graph Representation Learning. CoRR abs/1910.03081 (2019) - 2018
- [i1]Bartley D. Richardson, Benjamin J. Radford, Shawn E. Davis, Keegan E. Hines, David Pekarek:
Anomaly Detection in Cyber Network Data Using a Cyber Language Approach. CoRR abs/1808.10742 (2018)
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last updated on 2024-10-02 20:39 CEST by the dblp team
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