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Neural Network based controllers hold enormous potential to learn complex, high-dimensional functions.
A theory of the learnable
L. G. Valiant · 1984
Earlier work this paper cites.
Empirical model-building and response surfaces
G. E. Box and N. R. Draper · 1987
Earlier work this paper cites.
Keeping the neural networks simple by minimizing the description length of the weights
G. E. Hinton and D. van Camp · 1993
Earlier work this paper cites.
A pac analysis of a bayesian estimator
J. Shawe-Taylor and R. C. Williamson · 1997
Earlier work this paper cites.
Some pac-bayesian theorems
D. A. McAllester · 1999
Earlier work this paper cites.
Pac-bayesian model averaging
D. A. McAllester · 1999
Earlier work this paper cites.
A note on the pac bayesian theorem
A. Maurer · 2004
Earlier work this paper cites.
From ɛ-entropy to kl-entropy: Analysis of minimum information complexity density estimation
T. Zhang et al · 2006
Earlier work this paper cites.
Information-theoretic upper and lower bounds for statistical estimation
T. Zhang · 2006
Earlier work this paper cites.
Learning bounds for domain adaptation
J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Wortman · 2008
Earlier work this paper cites.
Chromatic pac-bayes bounds for non-iid data
L. Ralaivola, M. Szafranski, and G. Stempfel · 2009
Earlier work this paper cites.
Prédiction de suites individuelles et cadre statistique classique: étude de quelques liens autour de la régression parcimonieuse et des techniques d’agrégation
S. Gerchinovitz · 2011
Earlier work this paper cites.
Pilco: A model-based and data-efficient approach to policy search
M. Deisenroth and C. E. Rasmussen · 2011
Earlier work this paper cites.
The safe bayesian
P. Grünwald · 2012
Earlier work this paper cites.
Subgaussian random variables: An expository note
O. Rivasplata · 2012
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
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A pac-bayesian approach for domain adaptation with specialization to linear classifiers
P. Germain, A. Habrard, F. Laviolette, and E. Morvant · 2013
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A pac-bayesian bound for lifelong learning
A. Pentina and C. Lampert · 2014
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Doubly stochastic variational bayes for non-conjugate inference
M. Titsias and M. Lázaro-Gredilla · 2014
Cited alongside, same era.
Improving pilco with bayesian neural network dynamics models
Y. Gal, R. McAllister, and C. E. Rasmussen · 2016
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Openai gym
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
Later among the works it cites.
Concentration of tempered posteriors and of their variational approximations
Alquier, Pierre and Ridgway, James · 2017
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G. K. Dziugaite and D. M. Roy · 2017
Later among the works it cites.
Exploring generalization in deep learning
B. Neyshabur, S. Bhojanapalli, D. McAllester, and N. Srebro · 2017
Later among the works it cites.
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Cited alongside, same era.
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C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra · 2015
Cited alongside, same era.
Variational dropout and the local reparameterization trick
D. P. Kingma, T. Salimans, and M. Welling · 2015
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Wavenet: A generative model for raw audio
A. Van Den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. W. Senior, and K. Kavukcuoglu · 2016
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M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, et al · 2016
Cited alongside, same era.
Fast rates for general unbounded loss functions: from erm to generalized bayes
P. D. Grünwald and N. A. Mehta · 2016
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Cited alongside, same era.
J. C. G. Higuera, D. Meger, and G. Dudek · 2017
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Yolov3: An incremental improvement
J. Redmon and A. Farhadi · 2018
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Agile autonomous driving using end-to-end deep imitation learning
Y. Pan, C.-A. Cheng, K. Saigol, K. Lee, X. Yan, E. Theodorou, and B. Boots · 2018
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Simpler pac-bayesian bounds for hostile data
P. Alquier and B. Guedj · 2018
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A quasi-bayesian perspective to online clustering
L. Li, B. Guedj, S. Loustau, et al · 2018
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Synthesizing neural network controllers with probabilistic model-based reinforcement learning
J. C. G. Higuera, D. Meger, and G. Dudek · 2018
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Uncertainty aware learning from demonstrations in multiple contexts using bayesian neural networks
S. Thakur, H. van Hoof, J. C. G. Higuera, D. Precup, and D. Meger · 2019
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