Fetching the paper…
Reading the bibliography…
Active Learning (AL) allows models to learn interactively from user feedback.
Image counterfactual sensitivity analysis for detecting unintended bias
Emily Denton, Ben Hutchinson, Margaret Mitchell, Timnit Gebru, and Andrew Zaldivar. 2020 · 1906
Earlier work this paper cites.
Your wish is my command: Programming by example
Henry Lieberman. 2001 · 2001
Earlier work this paper cites.
Active learning literature survey
Burr Settles. 2009 · 2009
Earlier work this paper cites.
Example-based learning: Integrating cognitive and social-cognitive research perspectives
Tamara Gog and Nikol Rummel. 2010 · 2010
Earlier work this paper cites.
Explaining the efficacy of counterfactually augmented data
Divyansh Kaushik, Amrith Setlur, Eduard Hovy, and Zachary C. Lipton. 2021 · 2010
Earlier work this paper cites.
Cold-start active learning through self-supervised language modeling
Michelle Yuan, Hsuan-Tien Lin, and Jordan Boyd-Graber. 2020 · 2010
Earlier work this paper cites.
Automating string processing in spreadsheets using input-output examples
Sumit Gulwani. 2011 · 2011
Earlier work this paper cites.
Zhao Wang and Aron Culotta. 2020 · 2012
Earlier work this paper cites.
A survey on instance selection for active learning
Yifan Fu, Xingquan Zhu, and Bin Li. 2013 · 2013
Earlier work this paper cites.
Necessary conditions of learning
Ference Marton. 2014 · 2014
Earlier work this paper cites.
Yelp dataset challenge: Review rating prediction
Nabiha Asghar. 2016 · 2016
Earlier work this paper cites.
Learning through the variation theory: A case study
Wai Lun Eddie Cheng. 2016 · 2016
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Active learning via membership query synthesis for semi-supervised sentence classification
Raphael Schumann and Ines Rehbein. 2019 · 2019
Cited alongside, same era.
Learning the difference that makes a difference with counterfactually-augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary Lipton. 2020 · 2020
Cited alongside, same era.
A survey on active learning and human-in-the-loop deep learning for medical image analysis
Samuel Budd, Emma C Robinson, and Bernhard Kainz. 2021 · 2021
Cited alongside, same era.
Teaching models to express their uncertainty in words
Stephanie Lin, Jacob Hilton, and Owain Evans. 2022 · 2022
Later among the works it cites.
WANLI: Worker and AI collaboration for natural language inference dataset creation
Alisa Liu, Swabha Swayamdipta, Noah A. Smith, and Yejin Choi. 2022 · 2022
Later among the works it cites.
Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
Later among the works it cites.
Disco: Distilling counterfactuals with large language models
Zeming Chen, Qiyue Gao, Antoine Bosselut, Ashish Sabharwal, and Kyle Richardson. 2023 · 2023
Later among the works it cites.
Patat: Human-ai collaborative qualitative coding with explainable interactive rule synthesis
Simret Araya Gebreegziabher, Zheng Zhang, Xiaohang Tang, Yihao Meng, Elena L. Glassman, and Toby Jia-Jun Li. 2023 · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Context-aware Adversarial Training for Name Regularity Bias in Named Entity Recognition
Abbas Ghaddar, Philippe Langlais, Ahmad Rashid, and Mehdi Rezagholizadeh. 2021 · 2021
Cited alongside, same era.
Polyjuice: Generating counterfactuals for explaining, evaluating, and improving models
Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, and Daniel S Weld. 2021 · 2021
Cited alongside, same era.
Jack FitzGerald, Christopher Hench, Charith Peris, Scott Mackie, Kay Rottmann, Ana Sanchez, Aaron Nash, Liam Urbach, Vishesh Kakarala, Richa Singh, Swetha Ranganath, Laurie Crist, Misha Britan, Wouter Leeuwis, Gokhan Tur, and Prem Natarajan. 2022 · 2022
Cited alongside, same era.
Neuro-symbolic artificial intelligence: The state of the art
Pascal Hitzler and Md Kamruzzaman Sarker. 2022 · 2022
Cited alongside, same era.
An investigation of the (in)effectiveness of counterfactually augmented data
Nitish Joshi and He He. 2022 · 2022
Cited alongside, same era.
Large language models as counterfactual generator: Strengths and weaknesses
Yongqi Li, Mayi Xu, Xin Miao, Shen Zhou, and Tieyun Qian. 2023a
Cited in the paper.
Zhuoyan Li, Hangxiao Zhu, Zhuoran Lu, and Ming Yin. 2023b
Cited in the paper.
Rethinking counterfactual data augmentation under confounding
Abbavaram Gowtham Reddy, Saketh Bachu, Saloni Dash, Charchit Sharma, Amit Sharma, and Vineeth N Balasubramanian. 2023 · 2023
Later among the works it cites.
Beware of model collapse! fast and stable test-time adaptation for robust question answering
Yi Su, Yixin Ji, Juntao Li, Hai Ye, and Min Zhang. 2023 · 2023
Later among the works it cites.
On the robustness of chatgpt: An adversarial and out-of-distribution perspective
Jindong Wang, Xixu Hu, Wenxin Hou, Hao Chen, Runkai Zheng, Yidong Wang, Linyi Yang, Haojun Huang, Wei Ye, Xiubo Geng, and 1 others. 2023 · 2023
Later among the works it cites.
Scattershot: Interactive in-context example curation for text transformation
Sherry Wu, Hua Shen, Daniel S Weld, Jeffrey Heer, and Marco Tulio Ribeiro. 2023 · 2023
Later among the works it cites.
Emotions Dataset
Nidula Elgiriyewithana. 2024 · 2024
Closest in time.