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Two lines of work are taking the central stage in AI research.
On the identifiability of finite mixtures
Sidney J Yakowitz and John D Spragins · 1968
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Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
Jürgen Schmidhuber · 1987
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Statistical learning theory
Vladimir Vapnik · 1998
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Probability and measure theory
Robert B Ash and Catherine A Doléans-Dade · 2000
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A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, and Pascal Vincent · 2000
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Domain generalization by marginal transfer learning
Gilles Blanchard, Aniket Anand Deshmukh, Ürun Dogan, Gyemin Lee, and Clayton Scott · 2011
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Domain generalization via invariant feature representation
Krikamol Muandet, David Balduzzi, and Bernhard Schölkopf · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Machine bias
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Revisiting batch normalization for practical domain adaptation
Yanghao Li, Naiyan Wang, Jianping Shi, Jiaying Liu, and Xiaodi Hou · 2016
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Causal inference by using invariant prediction: identification and confidence intervals
Jonas Peters, Peter Bühlmann, and Nicolai Meinshausen · 2016
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Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
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Emnist: Extending mnist to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andre Van Schaik · 2017
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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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Domain generalization via conditional invariant representations
Ya Li, Mingming Gong, Xinmei Tian, Tongliang Liu, and Dacheng Tao · 2018
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Invariant models for causal transfer learning
Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, and Jonas Peters · 2018
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Invariant risk minimization
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2019
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Invariant risk minimization games
Kartik Ahuja, Karthikeyan Shanmugam, Kush Varshney, and Amit Dhurandhar · 2020
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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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Invariant rationalization
Shiyu Chang, Yang Zhang, Mo Yu, and Tommi S Jaakkola · 2020
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In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2020
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Enforcing predictive invariance across structured biomedical domains, 2020
Counterfactual invariance to spurious correlations in text classification
Victor Veitch, Alexander D’Amour, Steve Yadlowsky, and Jacob Eisenstein · 2021
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On calibration and out-of-domain generalization
Yoav Wald, Amir Feder, Daniel Greenfeld, and Uri Shalit · 2021
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Predictive processing and relevance realization: exploring convergent solutions to the frame problem
Brett P Andersen, Mark Miller, and John Vervaeke · 2022
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Iterative feature matching: Toward provable domain generalization with logarithmic environments
Yining Chen, Elan Rosenfeld, Mark Sellke, Tengyu Ma, and Andrej Risteski · 2022
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Probable domain generalization via quantile risk minimization
Cian Eastwood, Alexander Robey, Shashank Singh, Julius Von Kügelgen, Hamed Hassani, George J Pappas, and Bernhard Schölkopf · 2022
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Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2020
Cited alongside, same era.
Variational autoencoders and nonlinear ica: A unifying framework
Ilyes Khemakhem, Diederik Kingma, Ricardo Monti, and Aapo Hyvarinen · 2020
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Out-of-distribution generalization with maximal invariant predictor
Masanori Koyama and Shoichiro Yamaguchi · 2020
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Out-of-distribution generalization via risk extrapolation (rex)
David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, and Aaron Courville · 2020
Cited alongside, same era.
Domain generalization using causal matching
Divyat Mahajan, Shruti Tople, and Amit Sharma · 2020
Cited alongside, same era.
Counterfactual Theories of Causation
Peter Menzies and Helen Beebee · 2020
Cited alongside, same era.
Learning robust models using the principle of independent causal mechanisms
Jens Müller, Robert Schmier, Lynton Ardizzone, Carsten Rother, and Ullrich Köthe · 2020
Cited alongside, same era.
Pavel Izmailov, Polina Kirichenko, Nate Gruver, and Andrew G Wilson · 2022
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Last layer re-training is sufficient for robustness to spurious correlations
Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson · 2022
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Disentanglement via mechanism sparsity regularization: A new principle for nonlinear ica
Sébastien Lachapelle, Pau Rodriguez, Yash Sharma, Katie E Everett, Rémi Le Priol, Alexandre Lacoste, and Simon Lacoste-Julien · 2022
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Are all vision models created equal? a study of the open-loop to closed-loop causality gap
Mathias Lechner, Ramin Hasani, Alexander Amini, Tsun-Hsuan Wang, Thomas A Henzinger, and Daniela Rus · 2022
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Causally motivated shortcut removal using auxiliary labels
Maggie Makar, Ben Packer, Dan Moldovan, Davis Blalock, Yoni Halpern, and Alexander D’Amour · 2022
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Fishr: Invariant gradient variances for out-of-distribution generalization
Alexandre Rame, Corentin Dancette, and Matthieu Cord · 2022
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Provable domain generalization via invariant-feature subspace recovery
Haoxiang Wang, Haozhe Si, Bo Li, and Han Zhao · 2022
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Wild-time: A benchmark of in-the-wild distribution shift over time
Huaxiu Yao, Caroline Choi, Bochuan Cao, Yoonho Lee, Pang Wei W Koh, and Chelsea Finn · 2022
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A closer look at in-context learning under distribution shifts
Kartik Ahuja and David Lopez-Paz · 2023
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Contextual Vision Transformers for Robust Representation Learning
Yujia Bao and Theofanis Karaletsos · 2023
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WOODS: Benchmarks for out-of-distribution generalization in time series
Jean-Christophe Gagnon-Audet, Kartik Ahuja, Mohammad Javad Darvishi Bayazi, Pooneh Mousavi, Guillaume Dumas, and Irina Rish · 2023
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GPT-4 Technical Report
OpenAI · 2023
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Workshop on spurious correlations, invariance and stability
Yoav Wald, Claudia Shi, Aahlad Puli, Amir Feder, Limor Gultchin, Mark Goldstein, Maggie Makar, Victor Veitch, and Uri Shalit · 2023
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Doremi: Optimizing data mixtures speeds up language model pretraining
Sang Michael Xie, Hieu Pham, Xuanyi Dong, Nan Du, Hanxiao Liu, Yifeng Lu, Percy Liang, Quoc V Le, Tengyu Ma, and Adams Wei Yu · 2023
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What is missing in irm training and evaluation? challenges and solutions
Yihua Zhang, Pranay Sharma, Parikshit Ram, Mingyi Hong, Kush Varshney, and Sijia Liu · 2023
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