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Estimating the test performance of a model, possibly under distribution shift, without having access to the ground-truth labels is a challenging, yet very important problem for the safe deployment of machine learning algorithms in the wild.
Co-validation: Using model disagreement on unlabeled data to validate classification algorithms
Omid Madani, David Pennock, and Gary Flake · 2004
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Dataset shift in machine learning
Joaquin Quinonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D Lawrence · 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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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
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Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
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Train faster, generalize better: Stability of stochastic gradient descent
Moritz Hardt, Ben Recht, and Yoram Singer · 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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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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Estimating accuracy from unlabeled data: A bayesian approach
Emmanouil Antonios Platanios, Avinava Dubey, and Tom Mitchell · 2016
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Diversity assessment in many-objective optimization
Handing Wang, Yaochu Jin, and Xin Yao · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
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Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and Rayadurgam Srikant · 2017
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A pac-bayesian analysis of randomized learning with application to stochastic gradient descent
Ben London · 2017
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Exploring generalization in deep learning
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro · 2017
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Estimating accuracy from unlabeled data: A probabilistic logic approach
Emmanouil Platanios, Hoifung Poon, Tom M Mitchell, and Eric J Horvitz · 2017
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Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2018
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Deep anomaly detection with outlier exposure
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 2018
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Measuring catastrophic forgetting in neural networks
Ronald Kemker, Marc McClure, Angelina Abitino, Tyler Hayes, and Christopher Kanan · 2018
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Pac-bayes bounds for stable algorithms with instance-dependent priors
Omar Rivasplata, Emilio Parrado-Hernández, John S Shawe-Taylor, Shiliang Sun, and Csaba Szepesvári · 2018
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Provable guarantees for gradient-based meta-learning
Maria-Florina Balcan, Mikhail Khodak, and Ameet Talwalkar · 2019
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Learning-to-learn stochastic gradient descent with biased regularization
Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, and Massimiliano Pontil · 2019
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Towards better generalization of adaptive gradient methods
Yingxue Zhou, Belhal Karimi, Jinxing Yu, Zhiqiang Xu, and Ping Li · 2020
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When maml can adapt fast and how to assist when it cannot
Sebastien Arnold, Shariq Iqbal, and Fei Sha · 2021
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Detecting errors and estimating accuracy on unlabeled data with self-training ensembles
Jiefeng Chen, Frederick Liu, Besim Avci, Xi Wu, Yingyu Liang, and Somesh Jha · 2021
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Are labels always necessary for classifier accuracy evaluation?
Weijian Deng and Liang Zheng · 2021
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What does rotation prediction tell us about classifier accuracy under varying testing environments?
Weijian Deng, Stephen Gould, and Liang Zheng · 2021
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Confident anchor-induced multi-source free domain adaptation
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Vasilii Feofanov, Emilie Devijver, and Massih-Reza Amini · 2019
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
Cited alongside, same era.
Predicting the generalization gap in deep networks with margin distributions
Yiding Jiang, Dilip Krishnan, Hossein Mobahi, and Samy Bengio · 2019
Cited alongside, same era.
On generalization error bounds of noisy gradient methods for non-convex learning
Jian Li, Xuanyuan Luo, and Mingda Qiao · 2019
Cited alongside, same era.
When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton · 2019
Cited alongside, same era.
Information-theoretic generalization bounds for sgld via data-dependent estimates
Jeffrey Negrea, Mahdi Haghifam, Gintare Karolina Dziugaite, Ashish Khisti, and Daniel M Roy · 2019
Cited alongside, same era.
Towards task and architecture-independent generalization gap predictors
Scott Yak, Javier Gonzalvo, and Hanna Mazzawi · 2019
Cited alongside, same era.
Jiahua Dong, Zhen Fang, Anjin Liu, Gan Sun, and Tongliang Liu · 2021
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Predicting with confidence on unseen distributions
Devin Guillory, Vaishaal Shankar, Sayna Ebrahimi, Trevor Darrell, and Ludwig Schmidt · 2021
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On the importance of gradients for detecting distributional shifts in the wild
Rui Huang, Andrew Geng, and Yixuan Li · 2021
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Assessing generalization of sgd via disagreement
Yiding Jiang, Vaishnavh Nagarajan, Christina Baek, and J Zico Kolter · 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, Irena Gao, et al · 2021
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Domain generalization via gradient surgery
Lucas Mansilla, Rodrigo Echeveste, Diego H Milone, and Enzo Ferrante · 2021
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Do vision transformers see like convolutional neural networks?
Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, and Alexey Dosovitskiy · 2021
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Gradient matching for domain generalization
Yuge Shi, Jeffrey Seely, Philip HS Torr, N Siddharth, Awni Hannun, Nicolas Usunier, and Gabriel Synnaeve · 2021
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Generalized out-of-distribution detection: A survey
Jingkang Yang, Kaiyang Zhou, Yixuan Li, and Ziwei Liu · 2021
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Leveraging unlabeled data to predict out-of-distribution performance
Saurabh Garg, Sivaraman Balakrishnan, Zachary C Lipton, Behnam Neyshabur, and Hanie Sedghi · 2022
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Penalizing gradient norm for efficiently improving generalization in deep learning
Yang Zhao, Hao Zhang, and Xiuyuan Hu · 2022
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Massih-Reza Amini, Vasilii Feofanov, Loic Pauletto, Lies Hadjadj, Emilie Devijver, and Yury Maximov · 2023
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Confidence and dispersity speak: Characterizing prediction matrix for unsupervised accuracy estimation
Weijian Deng, Yumin Suh, Stephen Gould, and Liang Zheng · 2023
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Characterizing out-of-distribution error via optimal transport
Yuzhe Lu, Yilong Qin, Runtian Zhai, Andrew Shen, Ketong Chen, Zhenlin Wang, Soheil Kolouri, Simon Stepputtis, Joseph Campbell, and Katia Sycara · 2023
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On the importance of feature separability in predicting out-of-distribution error
Renchunzi Xie, Hongxin Wei, Yuzhou Cao, Lei Feng, and Bo An · 2023
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