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Multitask learning is widely used in practice to train a low-resource target task by augmenting it with multiple related source tasks.
“Design and analysis of computer experiments”
Jerome Sacks, William Welch, Toby Mitchell and Henry Wynn · 1989
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“Design and analysis of computer experiments”
Jerome Sacks, William Welch, Toby Mitchell and Henry Wynn · 1989
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“Multitask Learning”, 1997
Rich Caruana · 1997
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“Multitask Learning”, 1997
Rich Caruana · 1997
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“Learning to learn: Introduction and overview”
Sebastian Thrun and Lorien Pratt · 1998
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“Learning to learn: Introduction and overview”
Sebastian Thrun and Lorien Pratt · 1998
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“Rademacher and Gaussian complexities: Risk bounds and structural results”
Peter Bartlett and Shahar Mendelson · 2002
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“Rademacher and Gaussian complexities: Risk bounds and structural results”
Peter Bartlett and Shahar Mendelson · 2002
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“Exploiting task relatedness for multiple task learning”
Shai Ben-David and Reba Schuller · 2003
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“Evolutionary optimization of computationally expensive problems via surrogate modeling”
Yew Ong, Prasanth Nair and Andrew Keane · 2003
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“Exploiting task relatedness for multiple task learning”
Shai Ben-David and Reba Schuller · 2003
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“Evolutionary optimization of computationally expensive problems via surrogate modeling”
Yew Ong, Prasanth Nair and Andrew Keane · 2003
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“Regularized multi-task learning”
Theodoros Evgeniou and Massimiliano Pontil · 2004
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“Regularized multi-task learning”
Theodoros Evgeniou and Massimiliano Pontil · 2004
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“A framework for learning predictive structures from multiple tasks and unlabeled data”
Rie Ando and Tong Zhang · 2005
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“To Transfer or Not To Transfer” NIPS 2005 Workshop; Inductive Transfer: 10 Years Later , 2005
Michael. Rosenstein, Zvika Marx, Leslie Kaelbling and Thomas. Dietterich · 2005
Earlier work this paper cites.
“A framework for learning predictive structures from multiple tasks and unlabeled data”
Rie Ando and Tong Zhang · 2005
Earlier work this paper cites.
“To Transfer or Not To Transfer” NIPS 2005 Workshop; Inductive Transfer: 10 Years Later , 2005
Michael. Rosenstein, Zvika Marx, Leslie Kaelbling and Thomas. Dietterich · 2005
Earlier work this paper cites.
“A spectral regularization framework for multi-task structure learning”
Andreas Argyriou, Massimiliano Pontil, Yiming Ying and Charles Micchelli · 2007
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“A spectral regularization framework for multi-task structure learning”
Andreas Argyriou, Massimiliano Pontil, Yiming Ying and Charles Micchelli · 2007
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“Convex multi-task feature learning”
Andreas Argyriou, Theodoros Evgeniou and Massimiliano Pontil · 2008
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“Convex multi-task feature learning”
Andreas Argyriou, Theodoros Evgeniou and Massimiliano Pontil · 2008
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“A theory of learning from different domains”
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira and Jennifer Vaughan · 2010
Earlier work this paper cites.
“A theory of learning from different domains”
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira and Jennifer Vaughan · 2010
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“Spectral norm of products of random and deterministic matrices”
Roman Vershynin · 2011
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“Spectral norm of products of random and deterministic matrices”
Roman Vershynin · 2011
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“Learning task grouping and overlap in multi-task learning”
Abhishek Kumar and Hal Daume · 2012
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“Learning task grouping and overlap in multi-task learning”
Abhishek Kumar and Hal Daume · 2012
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“Multitask learning meets tensor factorization: task imputation via convex optimization”
Kishan Wimalawarne, Masashi Sugiyama and Ryota Tomioka · 2014
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“Multitask learning meets tensor factorization: task imputation via convex optimization”
Kishan Wimalawarne, Masashi Sugiyama and Ryota Tomioka · 2014
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“Classification with noisy labels by importance reweighting”
Tongliang Liu and Dacheng Tao · 2015
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“Classification with noisy labels by importance reweighting”
Tongliang Liu and Dacheng Tao · 2015
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“Algorithm-dependent generalization bounds for multi-task learning”
Tongliang Liu, Dacheng Tao, Mingli Song and Stephen Maybank · 2016
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“Data programming: Creating large training sets, quickly”
Alexander Ratner, Christopher De, Sen Wu, Daniel Selsam and Christopher Ré · 2016
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“Algorithm-dependent generalization bounds for multi-task learning”
Tongliang Liu, Dacheng Tao, Mingli Song and Stephen Maybank · 2016
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“Data programming: Creating large training sets, quickly”
Alexander Ratner, Christopher De, Sen Wu, Daniel Selsam and Christopher Ré · 2016
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“Spectrally-normalized margin bounds for neural networks”
Peter Bartlett, Dylan Foster and Matus Telgarsky · 2017
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“Understanding black-box predictions via influence functions”
Pang Koh and Percy Liang · 2017
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“Deep multi-task representation learning: A tensor factorisation approach”
Yongxin Yang and Timothy Hospedales · 2017
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“Spectrally-normalized margin bounds for neural networks”
Peter Bartlett, Dylan Foster and Matus Telgarsky · 2017
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“Understanding black-box predictions via influence functions”
Pang Koh and Percy Liang · 2017
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“Deep multi-task representation learning: A tensor factorisation approach”
Yongxin Yang and Timothy Hospedales · 2017
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“Calibrated multi-task learning”
Feiping Nie, Zhanxuan Hu and Xuelong Li · 2018
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“Representer point selection for explaining deep neural networks”
Chih-Kuan Yeh, Joon Kim, Ian-Hsu Yen and Pradeep Ravikumar · 2018
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“Taskonomy: Disentangling task transfer learning”
Amir Zamir, Alexander Sax, William Shen, Leonidas Guibas, Jitendra Malik and Silvio Savarese · 2018
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“Calibrated multi-task learning”
Feiping Nie, Zhanxuan Hu and Xuelong Li · 2018
Cited alongside, same era.
“Task-feature collaborative learning with application to personalized attribute prediction”
Zhiyong Yang, Qianqian Xu, Xiaochun Cao and Qingming Huang · 2020
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“Gradient surgery for multi-task learning”
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman and Chelsea Finn · 2020
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“Auxiliary task update decomposition: The good, the bad and the neutral”
Lucio Dery, Yann Dauphin and David Grangier · 2021
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“Retiring adult: New datasets for fair machine learning”
Frances Ding, Moritz Hardt, John Miller and Ludwig Schmidt · 2021
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“Efficiently identifying task groupings for multi-task learning”
Chris Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil and Chelsea Finn · 2021
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“Improved Regularization and Robustness for Fine-Tuning in Neural Networks”
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“Representer point selection for explaining deep neural networks”
Chih-Kuan Yeh, Joon Kim, Ian-Hsu Yen and Pradeep Ravikumar · 2018
Cited alongside, same era.
“Taskonomy: Disentangling task transfer learning”
Amir Zamir, Alexander Sax, William Shen, Leonidas Guibas, Jitendra Malik and Silvio Savarese · 2018
Cited alongside, same era.
“AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning”
Han Guo, Ramakanth Pasunuru and Mohit Bansal · 2019
Cited alongside, same era.
“On the value of target data in transfer learning”
Steve Hanneke and Samory Kpotufe · 2019
Cited alongside, same era.
“Training complex models with multi-task weak supervision”
Alexander Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey and Christopher Ré · 2019
Cited alongside, same era.
“High-dimensional statistics: A non-asymptotic viewpoint”
Martin Wainwright · 2019
Cited alongside, same era.
Dongyue Li and Hongyang Zhang · 2021
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“A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning”
Nikunj Saunshi, Arushi Gupta and Wei Hu · 2021
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Fan Yang, Hongyang Zhang, Sen Wu, Weijie Su and Christopher Ré · 2021
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“WRENCH: A Comprehensive Benchmark for Weak Supervision”
Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang and Alexander Ratner · 2021
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“A survey on multi-task learning”
Yu Zhang and Qiang Yang · 2021
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“Auxiliary task update decomposition: The good, the bad and the neutral”
Lucio Dery, Yann Dauphin and David Grangier · 2021
Later among the works it cites.
“Retiring adult: New datasets for fair machine learning”
Frances Ding, Moritz Hardt, John Miller and Ludwig Schmidt · 2021
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“Efficiently identifying task groupings for multi-task learning”
Chris Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil and Chelsea Finn · 2021
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“Improved Regularization and Robustness for Fine-Tuning in Neural Networks”
Dongyue Li and Hongyang Zhang · 2021
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“A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning”
Nikunj Saunshi, Arushi Gupta and Wei Hu · 2021
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Fan Yang, Hongyang Zhang, Sen Wu, Weijie Su and Christopher Ré · 2021
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“WRENCH: A Comprehensive Benchmark for Weak Supervision”
Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang and Alexander Ratner · 2021
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“A survey on multi-task learning”
Yu Zhang and Qiang Yang · 2021
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“ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning”
Vamsi Aribandi, Yi Tay, Tal Schuster, Jinfeng Rao, Huaixiu Zheng, Sanket Mehta, Honglei Zhuang, Vinh Tran, Dara Bahri and Jianmo Ni · 2022
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“Weighted Training for Cross-Task Learning”
Shuxiao Chen, Koby Crammer, Hangfeng He, Dan Roth and Weijie Su · 2022
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“Datamodels: Predicting predictions from training data”
Andrew Ilyas, Sung Park, Logan Engstrom, Guillaume Leclerc and Aleksander Madry · 2022
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“Transferability in Deep Learning: A Survey”
Junguang Jiang, Yang Shu, Jianmin Wang and Mingsheng Long · 2022
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“Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees”
Haotian Ju, Dongyue Li and Hongyang Zhang · 2022
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“Auto-lambda: Disentangling dynamic task relationships”
Shikun Liu, Stephen James, Andrew Davison and Edward Johns · 2022
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“Thompson Sampling for Robust Transfer in Multi-Task Bandits”
Zhi Wang, Chicheng Zhang and Kamalika Chaudhuri · 2022
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“Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlations”
Michael Zhang, Nimit Sohoni, Hongyang Zhang, Chelsea Finn and Christopher Ré · 2022
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“ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning”
Vamsi Aribandi, Yi Tay, Tal Schuster, Jinfeng Rao, Huaixiu Zheng, Sanket Mehta, Honglei Zhuang, Vinh Tran, Dara Bahri and Jianmo Ni · 2022
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“Weighted Training for Cross-Task Learning”
Shuxiao Chen, Koby Crammer, Hangfeng He, Dan Roth and Weijie Su · 2022
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“Datamodels: Predicting predictions from training data”
Andrew Ilyas, Sung Park, Logan Engstrom, Guillaume Leclerc and Aleksander Madry · 2022
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“Transferability in Deep Learning: A Survey”
Junguang Jiang, Yang Shu, Jianmin Wang and Mingsheng Long · 2022
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“Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees”
Haotian Ju, Dongyue Li and Hongyang Zhang · 2022
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“Auto-lambda: Disentangling dynamic task relationships”
Shikun Liu, Stephen James, Andrew Davison and Edward Johns · 2022
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“Thompson Sampling for Robust Transfer in Multi-Task Bandits”
Zhi Wang, Chicheng Zhang and Kamalika Chaudhuri · 2022
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“Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlations”
Michael Zhang, Nimit Sohoni, Hongyang Zhang, Chelsea Finn and Christopher Ré · 2022
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“Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion”
Haotian Ju, Dongyue Li, Aneesh Sharma and Hongyang Zhang · 2023
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“Boosting Multitask Learning on Graphs through Higher-Order Task Affinities”
Dongyue Li, Haotian Ju, Aneesh Sharma and Hongyang. Zhang · 2023
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“Understanding Influence Functions and Datamodels via Harmonic Analysis”
Nikunj Saunshi, Arushi Gupta, Mark Braverman and Sanjeev Arora · 2023
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“Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion”
Haotian Ju, Dongyue Li, Aneesh Sharma and Hongyang Zhang · 2023
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“Boosting Multitask Learning on Graphs through Higher-Order Task Affinities”
Dongyue Li, Haotian Ju, Aneesh Sharma and Hongyang. Zhang · 2023
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“Understanding Influence Functions and Datamodels via Harmonic Analysis”
Nikunj Saunshi, Arushi Gupta, Mark Braverman and Sanjeev Arora · 2023
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