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The advent of large language models (LLMs) has brought about a revolution in the development of tailored machine learning models and sparked debates on redefining data requirements.
An overview of statistical learning theory
Vladimir N Vapnik · 1999
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Semi-supervised learning literature survey
Xiaojin Zhu · 2005
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A tutorial on conformal prediction
Glenn Shafer and Vladimir Vovk · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Iterative learning for reliable crowdsourcing systems
David R Karger, Sewoong Oh, and Devavrat Shah · 2011
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Framing image description as a ranking task: Data, models and evaluation metrics
Micah Hodosh, Peter Young, and Julia Hockenmaier · 2013
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Efficient crowdsourcing for multi-class labeling
David R Karger, Sewoong Oh, and Devavrat Shah · 2013
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Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013
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Dwelling on the Negative: Incentivizing Effort in Peer Prediction
Jens Witkowski, Yoram Bachrach, Peter Key, and David C. Parkes · 2013
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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An online learning approach to improving the quality of crowd-sourcing
Yang Liu and Mingyan Liu · 2015
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Double or nothing: Multiplicative incentive mechanisms for crowdsourcing
Nihar Bhadresh Shah and Dengyong Zhou · 2015
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Strategic classification
Moritz Hardt, Nimrod Megiddo, Christos Papadimitriou, and Mary Wootters · 2016
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Learning to incentivize: Eliciting effort via output agreement
Yang Liu and Yiling Chen · 2016
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A survey of transfer learning
Karl Weiss, Taghi M Khoshgoftaar, and DingDing Wang · 2016
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Machine-learning aided peer prediction
Yang Liu and Yiling Chen · 2017
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A brief introduction to weakly supervised learning
Zhi-Hua Zhou · 2018
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Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru · 2019
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When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton · 2019
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How model accuracy and explanation fidelity influence user trust
Andrea Papenmeier, Gwenn Englebienne, and Christin Seifert · 2019
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Dialogpt: Large-scale generative pre-training for conversational response generation
Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, and Bill Dolan · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Learning strategy-aware linear classifiers
Yiling Chen, Yang Liu, and Chara Podimata · 2020
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Peer loss functions: Learning from noisy labels without knowing noise rates
Yang Liu and Hongyi Guo · 2020
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Replication markets: Results, lessons, challenges and opportunities in ai replication
Yang Liu, Michael Gordon, Juntao Wang, Michael Bishop, Yiling Chen, Thomas Pfeiffer, Charles Twardy, and Domenico Viganola · 2020
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Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi · 2022
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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
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Red teaming language models with language models
Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, and Geoffrey Irving · 2022
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To smooth or not? when label smoothing meets noisy labels
Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, and Yang Liu · 2022
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Learning with noisy labels revisited: A study using real-world human annotations
Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, and Yang Liu · 2022
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Performative prediction
Juan Perdomo, Tijana Zrnic, Celestine Mendler-Dünner, and Moritz Hardt · 2020
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How do fair decisions fare in long-term qualification?
Xueru Zhang, Ruibo Tu, Yang Liu, Mingyan Liu, Hedvig Kjellstrom, Kun Zhang, and Cheng Zhang · 2020
Cited alongside, same era.
Learning with instance-dependent label noise: A sample sieve approach
Hao Cheng, Zhaowei Zhu, Xingyu Li, Yifei Gong, Xing Sun, and Yang Liu · 2021
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Unfairness despite awareness: Group-fair classification with strategic agents
Andrew Estornell, Sanmay Das, Yang Liu, and Yevgeniy Vorobeychik · 2021
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Datasheets for datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé Iii, and Kate Crawford · 2021
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Model transferability with responsive decision subjects
Yang Liu, Yatong Chen, Zeyu Tang, and Kun Zhang · 2021
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Understanding instance-level label noise: Disparate impacts and treatments
Yang Liu · 2021
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Detecting corrupted labels without training a model to predict
Zhaowei Zhu, Zihao Dong, and Yang Liu · 2022
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The rich get richer: Disparate impact of semi-supervised learning
Zhaowei Zhu, Tianyi Luo, and Yang Liu · 2022
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Beyond images: Label noise transition matrix estimation for tasks with lower-quality features
Zhaowei Zhu, Jialu Wang, and Yang Liu · 2022
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A cookbook of self-supervised learning, 2023
Randall Balestriero, Mark Ibrahim, Vlad Sobal, Ari Morcos, Shashank Shekhar, Tom Goldstein, Florian Bordes, Adrien Bardes, Gregoire Mialon, Yuandong Tian, Avi Schwarzschild, Andrew Gordon Wilson, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Pirsiavash, Yann LeCun, and Micah Goldblum · 2023
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Chatgpt outperforms crowd-workers for text-annotation tasks
Fabrizio Gilardi, Meysam Alizadeh, and Maël Kubli · 2023
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Jie Gui, Tuo Chen, Qiong Cao, Zhenan Sun, Hao Luo, and Dacheng Tao · 2023
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Minghao Liu, Jiaheng Wei, Yang Liu, and James Davis · 2023
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Surrogate scoring rules
Yang Liu, Juntao Wang, and Yiling Chen · 2023
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Gpt-4 system card
OpenAI · 2023
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GPTs vs. human crowd in real-world text labeling: Who outperforms who?
Toloka · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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To aggregate or not? learning with separate noisy labels
Jiaheng Wei, Zhaowei Zhu, Tianyi Luo, Ehsan Amid, Abhishek Kumar, and Yang Liu · 2023
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A prompt log analysis of text-to-image generation systems
Yutong Xie, Zhaoying Pan, Jinge Ma, Luo Jie, and Qiaozhu Mei · 2023
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Long-term fairness with unknown dynamics
Tongxin Yin, Reilly Raab, Mingyan Liu, and Yang Liu · 2023
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Weak proxies are sufficient and preferable for fairness with missing sensitive attributes
Zhaowei Zhu, Yuanshun Yao, Jiankai Sun, Hang Li, and Yang Liu · 2023
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