Creative

Research

See Google Scholar for most recent work

(αβ) indicates author list in alphabetical order, (*) indicates equal contribution

Data and Model Explainability

Unifying Corroborative and Contributive Attributions in Large Language Models
T. Worledge*, JH. Shen*, N. Meister, C. Winston, C. Guestrin
2nd IEEE Conference on Secure and Trustworthy Machine Learning. 2024.
Contributed talk at NeurIPS ATTRIB Workshop. 2023.

Dissenting Explanations: Leveraging Disagreement to Reduce Model Overreliance
O. Reingold, JH. Shen, A. Talati (αβ)
38th Annual Conference on Artificial Intelligence (AAAI). 2024.

Algorithmic Fairness

Bidding Strategies for Proportional Representation in Advertisement Campaigns
IL. Navon, C. Peale, O. Reingold, JH. Shen (αβ)
4th annual Symposium on Foundations of Responsible Computing (FORC). 2023.

Leximax Approximations and Representative Cohort Selection
M Henzinger, C Peale, O Reingold, JH. Shen (αβ)
3rd annual Symposium on Foundations of Responsible Computing (FORC). 2022.

Darling or Babygirl? Investigating Stylistic Bias in Sentiment Analysis
JH. Shen*, L. Fratamico*, I. Rahwan, A. M. Rush.
5th Workshop on Fairness, Accountability, and Transparency in Machine Learning. 2018.
Contributed talk

Differential Privacy

Unlocking Accuracy and Fairness in Differentially Private Image Classification
L. Berrada*, S. De*, JH Shen*, J. Hayes, R. Stanforth, D. Stutz, P. Kohli, S.L. Smith, B. Balle
Preprint 2023.
[ blog post ]

Differentially Private Set Union
S. Gopi, P. Gulhane, J. Kulkarni, JH. Shen, M. Shokouhi, S. Yekhanin (αβ)
Journal of Privacy and Confidentiality (JPC) 2021.
Previous version at ICML 2020.
Contributed Talk at TPDP 2020.
[ talk ] [ code ]

Fast and Memory Efficient Differentially Private-SGD via JL Projections
Z. Bu, S. Gopi, J. Kulkarni, YT. Lee, JH. Shen, U. Tantipongpipat (αβ)
Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021). 2021.

Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
H. Nori, R. Caruana, Z. Bu, JH. Shen, J. Kulkarni.
Thirty-fifth Conference on Neural Information Processing Systems (ICML 2021). 2021.

Learning from Human Prefernces

Human-centric dialog training via offline reinforcement learning
N. Jaques*, JH. Shen*, A. Ghandeharioun, C. Ferguson, A. Lapedriza, N. Jones, S.S. Gu, R. Picard.
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020). 2020.
Previous version: Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog
Contributed talk at NeurIPS workshop on Conversational AI 2019.
[ data ] [ code ]

Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems
A. Ghandeharioun*, JH. Shen*, N. Jaques*, C. Ferguson, N. Jones, A. Lapedriza, R. Picard.
33rd Conference on Neural Information Processing Systems (NeurIPS 2019). 2019.
[ data ] [ code ]

Hierarchical reinforcement learning for open-domain dialog
A. Saleh, N. Jaques, A. Ghandeharioun, JH. Shen, R. Picard.
The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020). 2020.
[ data ] [ code ]

The Popstar, the Poet, and the Grinch: Relating Artificial Intelligence to the Computational Thinking Framework with Block-based Coding
J Van Brummelen, JH Shen, EW Patton.
Proceedings of International Conference on Computational Thinking Education. 2019.

Unintentional affective priming during labeling may bias labels
JH. Shen, A. Lapedriza, R. Picard.
8th International Conference on Affective Computing and Intelligent Interaction (ACII). 2019.

Comparing Models of Associative Meaning: An Empirical Investigation of Reference in Simple Language Games
JH. Shen*, M. Hofer*, B. Felbo, R. Levy.
Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018). 2018.
Oral presentation
[ code + data ]

TuringBox: An Experimental Platform for the Evaluation of AI Systems
Z. Epstein, B.H Payne, JH. Shen, CJ. Hong, B. Felbo, A. Dubey, M. Groh, N. Obradovich, M. Cebrian & I. Rahwan.
5th Workshop on Fairness, Accountability, and Transparency in Machine Learning. 2018.
Contributed talk

Closing the AI Knowledge Gap
Z. Epstein, B.H Payne, JH. Shen, A. Dubey, B. Felbo, M Groh, N. Obradovich, M. Cebrian & I. Rahwan
Preprint. 2018.

Mental Health and NLP

Detecting Anxiety through Reddit
JH. Shen. F. Rudzicz.
Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology–-From Linguistic Signal to Clinical Reality. 2017.
[ code ] [data: (available by request)]