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Zach Wood-Doughty
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2020 – today
- 2024
- [c10]Jackson Michaels, Juncheng B. Li, Laura Yao, Lijun Yu
, Zach Wood-Doughty, Florian Metze:
Audio-Journey: Open Domain Latent Diffusion Based Text-To-Audio Generation. ICASSP 2024: 6960-6964 - [c9]Marko Veljanovski, Zach Wood-Doughty:
DoubleLingo: Causal Estimation with Large Language Models. NAACL (Short Papers) 2024: 799-807 - 2023
- [j3]Ilya Shpitser, Zach Wood-Doughty, Eric J. Tchetgen Tchetgen:
The Proximal ID Algorithm. J. Mach. Learn. Res. 24: 188:1-188:46 (2023) - [i8]Jingqian Wu, Rongtao Xu, Zach Wood-Doughty, Changwei Wang:
Segment Anything Model is a Good Teacher for Local Feature Learning. CoRR abs/2309.16992 (2023) - 2022
- [j2]Amir Feder, Katherine A. Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond. Trans. Assoc. Comput. Linguistics 10: 1138-1158 (2022) - [c8]Zach Wood-Doughty, Isabel Cachola, Mark Dredze:
Model Distillation for Faithful Explanations of Medical Code Predictions. BioNLP@ACL 2022: 412-425 - 2021
- [j1]Aaron Mueller, Zach Wood-Doughty, Silvio Amir, Mark Dredze, Alicia Lynn Nobles:
Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement. Proc. ACM Hum. Comput. Interact. 5(CSCW1): 107:1-107:28 (2021) - [c7]Zach Wood-Doughty, Paiheng Xu
Using Noisy Self-Reports to Predict Twitter User Demographics. SocialNLP@NAACL 2021: 123-137 - [c6]Zach Wood-Doughty, Isabel Cachola, Mark Dredze:
Proxy Model Explanations for Time Series RNNs. ICMLA 2021: 698-703 - [i7]Zach Wood-Doughty, Ilya Shpitser, Mark Dredze:
Generating Synthetic Text Data to Evaluate Causal Inference Methods. CoRR abs/2102.05638 (2021) - [i6]Zach Wood-Doughty, Isabel Cachola, Mark Dredze:
Faithful and Plausible Explanations of Medical Code Predictions. CoRR abs/2104.07894 (2021) - [i5]Ilya Shpitser, Zach Wood-Doughty, Eric J. Tchetgen Tchetgen:
The Proximal ID Algorithm. CoRR abs/2108.06818 (2021) - [i4]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:
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond. CoRR abs/2109.00725 (2021) - 2020
- [i3]Zach Wood-Doughty, Paiheng Xu
Using Noisy Self-Reports to Predict Twitter User Demographics. CoRR abs/2005.00635 (2020) - [i2]Aaron Mueller, Zach Wood-Doughty, Silvio Amir, Mark Dredze, Alicia L. Nobles:
Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement. CoRR abs/2010.06472 (2020)
2010 – 2019
- 2018
- [c5]Zach Wood-Doughty, Praateek Mahajan, Mark Dredze:
Johns Hopkins or johnny-hopkins: Classifying Individuals versus Organizations on Twitter. PEOPLES@NAACL-HTL 2018: 56-61 - [c4]Zach Wood-Doughty, Nicholas Andrews, Rebecca Marvin, Mark Dredze:
Predicting Twitter User Demographics from Names Alone. PEOPLES@NAACL-HTL 2018: 105-111 - [c3]Zach Wood-Doughty, Nicholas Andrews, Mark Dredze:
Convolutions Are All You Need (For Classifying Character Sequences). NUT@EMNLP 2018: 208-213 - [c2]Zach Wood-Doughty, Ilya Shpitser, Mark Dredze:
Challenges of Using Text Classifiers for Causal Inference. EMNLP 2018: 4586-4598 - [i1]Zach Wood-Doughty, Ilya Shpitser, Mark Dredze:
Challenges of Using Text Classifiers for Causal Inference. CoRR abs/1810.00956 (2018) - 2017
- [c1]Zach Wood-Doughty, Michael Smith, David A. Broniatowski, Mark Dredze:
How Does Twitter User Behavior Vary Across Demographic Groups? NLP+CSS@ACL 2017: 83-89
Coauthor Index
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last updated on 2024-10-07 22:13 CEST by the dblp team
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