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Informational parsimony provides a useful inductive bias for learning representations that achieve better generalization by being robust to noise and spurious correlations.
The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek · 2000
Earlier work this paper cites.
Feature selection, l 1 vs. l 2 regularization, and rotational invariance
Andrew Y Ng · 2004
Earlier work this paper cites.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
Earlier work this paper cites.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol, and Léon Bottou · 2010
Earlier work this paper cites.
Bisimulation metrics for continuous markov decision processes
Norm Ferns, Prakash Panangaden, and Doina Precup · 2011
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Variational dropout and the local reparameterization trick
Durk P Kingma, Tim Salimans, and Max Welling · 2015
Earlier work this paper cites.
Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky · 2015
Earlier work this paper cites.
Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy · 2016
Earlier work this paper cites.
Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian J. Goodfellow, and Sergey Levine · 2016
Earlier work this paper cites.
End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
Earlier work this paper cites.
Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell · 2017
Earlier work this paper cites.
Information dropout: Learning optimal representations through noisy computation
Alessandro Achille and Stefano Soatto · 2018
Earlier work this paper cites.
Discovering and removing exogenous state variables and rewards for reinforcement learning
Thomas G. Dietterich, George Trimponias, and Zhitang Chen · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Time-contrastive networks: Self-supervised learning from video
Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, Sergey Levine, and Google Brain · 2018
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Learning representations by maximizing mutual information across views
Philip Bachman, R Devon Hjelm, and William Buchwalter · 2019
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Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning
Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, and Karol Hausman · 2019
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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2019
Learning task informed abstractions
Xiang Fu, Ge Yang, Pulkit Agrawal, and Tommi Jaakkola · 2021
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Variational diffusion models
Diederik Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
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Offline reinforcement learning with implicit q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2021
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Learning representations for pixel-based control: What matters and why?
Manan Tomar, Utkarsh A Mishra, Amy Zhang, and Matthew E Taylor · 2021
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Masked siamese networks for label-efficient learning
Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Mike Rabbat, and Nicolas Ballas · 2022
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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DARTS: differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
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Learning to adapt in dynamic, real-world environments through meta-reinforcement learning
Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S. Fearing, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Ilya Kostrikov, Denis Yarats, and Rob Fergus · 2020
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Deep reinforcement and infomax learning
Bogdan Mazoure, Remi Tachet des Combes, Thang Long Doan, Philip Bachman, and R Devon Hjelm · 2020
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Viewmaker networks: Learning views for unsupervised representation learning
Alex Tamkin, Mike Wu, and Noah Goodman · 2020
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Masked autoencoders are scalable vision learners
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Agent-controller representations: Principled offline rl with rich exogenous information
Riashat Islam, Manan Tomar, Alex Lamb, Yonathan Efroni, Hongyu Zang, Aniket Didolkar, Dipendra Misra, Xin Li, Harm van Seijen, Remi Tachet des Combes, et al · 2022
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Guaranteed discovery of controllable latent states with multi-step inverse models
Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Didolkar, Dipendra Misra, Dylan Foster, Lekan Molu, Rajan Chari, Akshay Krishnamurthy, and John Langford · 2022
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Challenges and opportunities in offline reinforcement learning from visual observations
Cong Lu, Philip J Ball, Tim GJ Rudner, Jack Parker-Holder, Michael A Osborne, and Yee Whye Teh · 2022
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Vip: Towards universal visual reward and representation via value-implicit pre-training
Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, and Amy Zhang · 2022
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R3m: A universal visual representation for robot manipulation
Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, and Abhinav Gupta · 2022
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Adversarial unlearning: Reducing confidence along adversarial directions
Amrith Setlur, Benjamin Eysenbach, Virginia Smith, and Sergey Levine · 2022
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Adversarial masking for self-supervised learning
Yuge Shi, N Siddharth, Philip Torr, and Adam R Kosiorek · 2022
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Denoised mdps: Learning world models better than the world itself
Tongzhou Wang, Simon S Du, Antonio Torralba, Phillip Isola, Amy Zhang, and Yuandong Tian · 2022
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