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Empirical risk minimization (ERM) of neural networks is prone to over-reliance on spurious correlations and poor generalization on minority groups.
Improving generalization with active learning
David Cohn, Les Atlas, and Richard Ladner · 1994
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Committee-based sampling for training probabilistic classifiers
Ido Dagan and Sean P. Engelson · 1995
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Statistical Learning Theory
Vladimir Vapnik · 1998
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Active hidden markov models for information extraction
Tobias Scheffer, Christian Decomain, and Stefan Wrobel · 2001
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Reducing labeling effort for structured prediction tasks
Aron Culotta and Andrew McCallum · 2005
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Agnostic active learning
Maria-Florina Balcan, Alina Beygelzimer, and John Langford · 2006
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Matplotlib: A 2D graphics environment
John D. Hunter · 2007
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Sparse online learning via truncated gradient
John Langford, Lihong Li, and Tong Zhang · 2009
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Domain adaptation: Learning bounds and algorithms
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2009
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Caltech-UCSD birds 200
Peter Welinder, Steve Branson, Takeshi Mita, Catherine Wah, Florian Schroff, Serge Belongie, and Pietro Perona · 2010
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The Caltech-UCSD birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Theory of Disagreement-Based Active Learning
Steve Hanneke · 2014
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In search of the real inductive bias: On the role of implicit regularization in deep learning
Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Tagging performance correlates with author age
Dirk Hovy and Anders Søgaard · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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ImageNet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler · 2015
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Demographic dialectal variation in social media: A case study of African-American English
Su Lin Blodgett, Lisa Green, and Brendan O’Connor · 2016
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2016
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Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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TorchVision: PyTorch’s computer vision library
TorchVision maintainers and contributors · 2016
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A closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, and Simon Lacoste-Julien · 2017
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Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Gender and dialect bias in YouTube’s automatic captions
Rachael Tatman · 2017
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Recognition in terra incognita
Sara Beery, Grant van Horn, and Pietro Perona · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith · 2018
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Fairness without demographics in repeated loss minimization
Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang · 2018
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The elephant in the room
Amir Rosenfeld, Richard Zemel, and John K. Tsotsos · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman · 2018
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Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study
Model patching: Closing the subgroup performance gap with data augmentation
Karan Goel, Albert Gu, Yixuan Li, and Christopher Ré · 2021
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BiaSwap: removing dataset bias with bias-tailored swapping augmentation
Eungyeup Kim, Jihyeon Lee, and Jaegul Choo · 2021
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Uniform convergence of interpolators: Gaussian width, norm bounds and benign overfitting
Frederic Koehler, Lijia Zhou, Danica J. Sutherland, and Nathan Srebro · 2021
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WILDS: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang · 2021
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Just train twice: Improving group robustness without training group information
Evan Zheran Liu, Behzad Haghgoo, Annie S. Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, and Chelsea Finn · 2021
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John R. Zech, Marcus A. Badgeley, Manway Liu, Anthony B. Costa, Joseph J. Titano, and Eric Karl Oermann · 2018
Cited alongside, same era.
Invariant risk minimization
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Nuanced metrics for measuring unintended bias with real data for text classification
Daniel Borkan, Lucas Dixon, Jeffrey Sorenson, Nithium Thain, and Lucy Vasserman · 2019
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen · 2019
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Gradient starvation: A learning proclivity in neural networks
Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron Courville, Doina Precup, and Guillaume Lajoie · 2021
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Noise or signal: The role of image backgrounds in object recognition
Kai Yuanqing Xiao, Logan Engstrom, Andrew Ilyas, and Aleksander Mądry · 2021
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Increasing robustness to spurious correlations using forgettable examples
Yadollah Yaghoobzadeh, Soroush Mehri, Remi Tachet, T.J. Hazen, and Alessandro Sordoni · 2021
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Agreement-on-the-line: Predicting the performance of neural networks under distribution shift
Christina Baek, Yiding Jiang, Aditi Raghunathan, and Zico Kolter · 2022
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Simple data balancing achieves competitive worst-group-accuracy
Badr Youbi Idrissi, Martín Arjovsky, Mohammad Pezeshki, and David Lopez-Paz · 2022
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On feature learning in the presence of spurious correlations
Pavel Izmailov, Polina Kirichenko, Nate Gruver, and Andrew Gordon Wilson · 2022
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Assessing generalization of SGD via disagreement
Yiding Jiang, Vaishnavh Nagarajan, Christina Baek, and J. Zico Kolter · 2022
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Learning debiased classifier with biased committee
Nayeong Kim, Sehyun Hwang, Sungsoo Ahn, Jaesik Park, and Suha Kwak · 2022
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Dropout disagreement: A recipe for group robustness with fewer annotations
Tyler LaBonte, Vidya Muthukumar, and Abhishek Kumar · 2022
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Spread spurious attribute: Improving worst-group accuracy with spurious attribute estimation
Junhyun Nam, Jaehyung Kim, Jaeho Lee, and Jinwoo Shin · 2022
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Salient ImageNet: how to discover spurious features in deep learning?
Sahil Singla and Soheil Feizi · 2022
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BARACK: partially supervised group robustness with guarantees
Nimit S. Sohoni, Maziar Sanjabi, Nicolas Ballas, Aditya Grover, Shaoliang Nie, Hamed Firooz, and Christopher Ré · 2022
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MaskTune: mitigating spurious correlations by forcing to explore
Saeid Asgari Taghanaki, Aliasghar Khani, Fereshte Khani, Ali Gholami, Linh Trana, Ali Mahdavi-Amiri, and Ghassan Hamarneh · 2022
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Distributionally robust post-hoc classifiers under prior shifts
Jiaheng Wei, Harikrishna Narasimhan, Ehsan Amid, Wen-Sheng Chu, Yang Liu, and Abhishek Kumar · 2022
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Controlling directions orthogonal to a classifier
Yilun Xu, Hao He, Tianxiao Shen, and Tommi Jaakkola · 2022
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Understanding rare spurious correlations in neural networks
Yao-Yuan Yang, Chi-Ning Chou, and Kamalika Chaudhuri · 2022
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Last layer re-training is sufficient for robustness to spurious correlations
Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson · 2023
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Milkshake: Quick and extendable experimentation with classification models
Tyler LaBonte · 2023
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Saving a split for last-layer retraining can improve group robustness without group annotations
Tyler LaBonte, Vidya Muthukumar, and Abhishek Kumar · 2023
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Diversify and disambiguate: Learning from underspecified data
Yoonho Lee, Huaxiu Yao, and Chelsea Finn · 2023
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Agree to disagree: Diversity through disagreement for better transferability
Matteo Pagliardini, Martin Jaggi, François Fleuret, and Sai Praneeth Karimireddy · 2023
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Simple and fast group robustness by automatic feature reweighting
Shikai Qiu, Andres Potapczynski, Pavel Izmailov, and Andrew Gordon Wilson · 2023
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