Zach Wood-Doughty

According to our database, Zach Wood-Doughty authored at least 19 papers between 2017 and 2024.

Collaborative distances:

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

2024

Can You Trust LLM Judgments? Reliability of LLM-as-a-Judge.

Kayla Schroeder, Zach Wood-Doughty

CoRR, 2024

Controlling for Unobserved Confounding with Large Language Model Classification of Patient Smoking Status.

Samuel Lee, Zach Wood-Doughty

CoRR, 2024

Reliability of Topic Modeling.

Kayla Schroeder, Zach Wood-Doughty

CoRR, 2024

DoubleLingo: Causal Estimation with Large Language Models.

Marko Veljanovski, Zach Wood-Doughty

Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Short Papers, 2024

Audio-Journey: Open Domain Latent Diffusion Based Text-To-Audio Generation.

Jackson Michaels, Juncheng B. Li, Laura Yao, Lijun Yu, Zach Wood-Doughty, Florian Metze

Proceedings of the IEEE International Conference on Acoustics, 2024

2023

The Proximal ID Algorithm.

Ilya Shpitser, Zach Wood-Doughty, Eric J. Tchetgen Tchetgen

J. Mach. Learn. Res., 2023

Segment Anything Model is a Good Teacher for Local Feature Learning.

Jingqian Wu, Rongtao Xu, Zach Wood-Doughty, Changwei Wang

CoRR, 2023

2022

Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond.

Amir Feder, Katherine A. Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E. Roberts, Brandon M. Stewart, Victor Veitch, Diyi Yang

Trans. Assoc. Comput. Linguistics, 2022

Model Distillation for Faithful Explanations of Medical Code Predictions.

Zach Wood-Doughty, Isabel Cachola, Mark Dredze

Proceedings of the 21st Workshop on Biomedical Language Processing, 2022

2021

Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement.

Aaron Mueller, Zach Wood-Doughty, Silvio Amir, Mark Dredze, Alicia Lynn Nobles

Proc. ACM Hum. Comput. Interact., 2021

Faithful and Plausible Explanations of Medical Code Predictions.

Zach Wood-Doughty, Isabel Cachola, Mark Dredze

CoRR, 2021

Generating Synthetic Text Data to Evaluate Causal Inference Methods.

Zach Wood-Doughty, Ilya Shpitser, Mark Dredze

CoRR, 2021

Proxy Model Explanations for Time Series RNNs.

Zach Wood-Doughty, Isabel Cachola, Mark Dredze

Proceedings of the 20th IEEE International Conference on Machine Learning and Applications, 2021

Using Noisy Self-Reports to Predict Twitter User Demographics.

Zach Wood-Doughty, Paiheng Xu, Xiao Liu, Mark Dredze

Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media, 2021

2018

Challenges of Using Text Classifiers for Causal Inference.

Zach Wood-Doughty, Ilya Shpitser, Mark Dredze

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31, 2018

Convolutions Are All You Need (For Classifying Character Sequences).

Zach Wood-Doughty, Nicholas Andrews, Mark Dredze

Proceedings of the 4th Workshop on Noisy User-generated Text, 2018

Johns Hopkins or johnny-hopkins: Classifying Individuals versus Organizations on Twitter.

Zach Wood-Doughty, Praateek Mahajan, Mark Dredze

Proceedings of the Second Workshop on Computational Modeling of People's Opinions, 2018

Predicting Twitter User Demographics from Names Alone.

Zach Wood-Doughty, Nicholas Andrews, Rebecca Marvin, Mark Dredze

Proceedings of the Second Workshop on Computational Modeling of People's Opinions, 2018

2017

How Does Twitter User Behavior Vary Across Demographic Groups?

Zach Wood-Doughty, Michael Smith, David A. Broniatowski, Mark Dredze

Proceedings of the Second Workshop on NLP and Computational Social Science, 2017