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Meta-Learning aims to speed up the learning process on new tasks by acquiring useful inductive biases from datasets of related learning tasks.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
William R Thompson · 1933
Earlier work this paper cites.
Use of Different Monte Carlo Sampling Techniques
Herman Kahn · 1955
Earlier work this paper cites.
I-divergence geometry of probability distributions and minimization problems
Imre Csiszár · 1975
Earlier work this paper cites.
Large deviations for Markov processes and the asymptotic evaluation of certain Markov process expectations for large times
M.D. Donsker and S.R.S. Varadhan · 1975
Earlier work this paper cites.
Evolutionary principles in self-referential learning
Juergen Schmidhuber · 1987
Earlier work this paper cites.
Learning a synaptic learning rule
Y. Bengio, S. Bengio, and J. Cloutier · 1991
Earlier work this paper cites.
Learning to Learn
Sebastian Thrun and Lorien Pratt · 1998
Earlier work this paper cites.
Some PAC-Bayesian theorems
David A McAllester · 1999
Earlier work this paper cites.
A model of inductive bias learning
Jonathan Baxter · 2000
Earlier work this paper cites.
Learning To Learn Using Gradient Descent
Sepp Hochreiter, A. Steven Younger, and Peter R. Conwell · 2001
Earlier work this paper cites.
Using confidence bounds for exploitation-exploration trade-offs
Peter Auer · 2002
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PAC-Bayesian generalisation error bounds for Gaussian process classification
Matthias Seeger · 2002
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A note on the PAC Bayesian theorem
Andreas Maurer · 2004
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Kernels for Multi-task Learning
Charles Micchelli and Massimiliano Pontil · 2004
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Algorithmic stability and meta-learning
Andreas Maurer and Tommi Jaakkola · 2005
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Learning the Kernel with Hyperkernels
Cheng S. Ong, Alexander J. Smola, and Robert C. Williamson · 2005
Earlier work this paper cites.
Learning Gaussian processes from multiple tasks
Kai Yu, Volker Tresp, and Anton Schwaighofer · 2005
Earlier work this paper cites.
Gaussian Processes in machine learning
Carl Edward Rasmussen and Christopher K. I. Williams · 2006
Earlier work this paper cites.
PAC-Bayesian supervised classification: the thermodynamics of statistical learning
Olivier Catoni · 2007
Earlier work this paper cites.
Multiclass Multiple Kernel Learning
Alexander Zien and Cheng Soon Ong · 2007
Earlier work this paper cites.
PAC-Bayesian bounds for randomized empirical risk minimizers
Pierre Alquier · 2008
Earlier work this paper cites.
Multi-task Gaussian Process prediction
Edwin V. Bonilla, Kian M. Chai, and Christopher Williams · 2008
Earlier work this paper cites.
Gaussian process optimization in the bandit setting: No regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham M. Kakade, and Matthias Seeger · 2009
Earlier work this paper cites.
Large margin multi-task metric learning
Shibin Parameswaran and Kilian Q Weinberger · 2010
Earlier work this paper cites.
Inferring latent task structure for multitask learning by multiple kernel learning
Christian Widmer, Nora C. Toussaint, Yasemin Altun, and Gunnar Rätsch · 2010
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Multiple kernel learning algorithms
Mehmet Gönen and Ethem Alpaydın · 2011
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Contextual Gaussian Process bandit optimization
Andreas Krause and Cheng S. Ong · 2011
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PAC-Bayes Bounds with Data Dependent Priors
Emilio Parrado-Hernandez, Amiran Ambroladze, John Shawe-Taylor, and Shiliang Sun · 2012
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One-shot learning with a hierarchical nonparametric Bayesian model
Ruslan Salakhutdinov, Joshua Tenenbaum, and Antonio Torralba · 2012
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Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012
Ikaro Silva, George Moody, Daniel J. Scott, Leo A. Celi, and Roger G. Mark · 2012
Earlier work this paper cites.
Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
James Bergstra, Daniel Yamins, and David Cox · 2013
Earlier work this paper cites.
Concentration inequalities: a nonasymptotic theory of independence
Stephane Boucheron, Gabor Lugosi, and Pascal Massart · 2013
Earlier work this paper cites.
Tighter PAC-Bayes bounds through distribution-dependent priors
Guy Lever, François Laviolette, and John Shawe-Taylor · 2013
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Auto-Encoding Variational Bayes
Diederik P. Kingma and Max Welling · 2014
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A PAC-Bayesian bound for lifelong learning
Anastasia Pentina and Christoph Lampert · 2014
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Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
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Human-level concept learning through probabilistic program induction
Brenden M. Lake, Ruslan Salakhutdinov, and Joshua B. Tenenbaum · 2015
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Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
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Taking the human out of the loop: A review of Bayesian optimization
Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, and Nando De Freitas · 2015
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On the properties of variational approximations of Gibbs posteriors
Pierre Alquier, James Ridgway, and Nicolas Chopin · 2016
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Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds
David Reeb, Andreas Doerr, Sebastian Gerwinn, and Barbara Rakitsch · 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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Multi-task learning as multi-objective optimization
Ozan Sener and Vladlen Koltun · 2018
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Bayesian model-agnostic meta-learning
Jaesik Yoon, Taesup Kim, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, and Sungjin Ahn · 2018
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Deep Mean Functions for Meta-Learning in Gaussian Processes
Vincent Fortuin and Gunnar Rätsch · 2019
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A primer on PAC-Bayesian learning
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Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gómez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas · 2016
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PAC-Bayesian theory meets Bayesian inference
Pascal Germain, Francis Bach, Alexandre Lacoste, and Simon Lacoste-Julien · 2016
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Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
Qiang Liu and Dilin Wang · 2016
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PAC-Bayesian analysis of distribution dependent priors: Tighter risk bounds and stability analysis
Luca Oneto, Davide Anguita, and Sandro Ridella · 2016
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Physically Based Rendering: From Theory to Implementation , chapter 13.7
Matt Pharr, Wenzel Jakob, and Greg Humphreys · 2016
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Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 2016
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Benjamin Guedj · 2019
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PAC-Bayes under potentially heavy tails
Matthew Holland · 2019
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Attentive neural processes
Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, and Yee Whye Teh · 2019
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ProMP: Proximal Meta-Policy Search
Jonas Rothfuss, Dennis Lee, Ignasi Clavera, Tamim Asfour, and Pieter Abbeel · 2019
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Unifying variational inference and pac-bayes for supervised learning that scales
Sanjay Thakur, Herke Van Hoof, Gunshi Gupta, and David Meger · 2019
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Unraveling meta-learning: Understanding feature representations for few-shot tasks
Micah Goldblum, Steven Reich, Liam Fowl, Renkun Ni, Valeriia Cherepanova, and Tom Goldstein · 2020
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SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models
Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, and Ricky T. Q. Chen · 2020
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Intel lab data
Samuel Madden · 2020
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Improved PAC-Bayesian bounds for linear regression
Vera Shalaeva, Alireza Fakhrizadeh Esfahani, Pascal Germain, and Mihaly Petreczky · 2020
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Metafun: Meta-learning with iterative functional updates
Jin Xu, Jean-Francois Ton, Hyunjik Kim, Adam Kosiorek, and Yee Whye Teh · 2020
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Meta-learning without memorization
Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, and Chelsea Finn · 2020
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User-friendly introduction to PAC-Bayes bounds
Pierre Alquier · 2021
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Multi-task and meta-learning with sparse linear bandits
Leonardo Cella and Massimiliano Pontil · 2021
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Generalization bounds for meta-learning: An information-theoretic analysis
Qi Chen, Changjian Shui, and Mario Marchand · 2021
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Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning
Nan Ding, Xi Chen, Tomer Levinboim, Sebastian Goodman, and Radu Soricut · 2021
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On the role of data in PAC-Bayes bounds
Gintare Karolina Dziugaite, Kyle Hsu, Waseem Gharbieh, Gabriel Arpino, and Daniel Roy · 2021
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Generalization bounds for meta-learning via PAC-Bayes and uniform stability
Alec Farid and Anirudha Majumdar · 2021
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Information-theoretic generalization bounds for meta-learning and applications
Sharu Theresa Jose and Osvaldo Simeone · 2021
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Statistical generalization performance guarantee for meta-learning with data-dependent prior
Tianyu Liu, Jie Lu, Zheng Yan, and Guangquan Zhang · 2021
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Tighter risk certificates for neural networks
María Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor, and Csaba Szepesvári · 2021
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Conditional mutual information-based generalization bound for meta learning
Arezou Rezazadeh, Sharu Theresa Jose, Giuseppe Durisi, and Osvaldo Simeone · 2021
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Meta representation learning with contextual linear bandits
Leonardo Cella, Karim Lounici, and Massimiliano Pontil · 2022
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Bayesian neural network priors revisited
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Fast-rate PAC-Bayesian generalization bounds for meta-learning
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Information-Theoretic Analysis of Epistemic Uncertainty in Bayesian Meta-learning
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Meta-learning hypothesis spaces for sequential decision-making
Parnian Kassraie, Jonas Rothfuss, and Andreas Krause · 2022
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Meta-Learning Priors for Safe Bayesian Optimization
Jonas Rothfuss, Christopher Koenig, Alisa Rupenyan, and Andreas Krause · 2022
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Lifelong bandit optimization: no prior and no regret
Felix Schur, Parnian Kassraie, Jonas Rothfuss, and Andreas Krause · 2023
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