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	Date-Added = {2018-05-18 14:57:39 +0000},
	Date-Modified = {2018-05-18 14:57:39 +0000},
	Organization = {IEEE},
	Title = {{The Earth Mover's Distance is the Mallows Distance: Some Insights from Statistics}},
	Year = {2001}}

@article{lspi,
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	Date-Added = {2018-05-18 14:57:39 +0000},
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@article{majumdar2017should,
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@article{mannor11meanvariance,
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@phdthesis{mccallum95reinforcement,
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	Date-Added = {2018-05-18 14:57:39 +0000},
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	Date-Added = {2018-05-18 14:57:39 +0000},
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	Date-Added = {2018-05-18 14:57:39 +0000},
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	Date-Added = {2018-05-18 14:57:39 +0000},
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	Date-Added = {2018-05-18 14:57:39 +0000},
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	Date-Added = {2018-05-18 14:57:39 +0000},
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	Date-Added = {2018-05-18 14:57:39 +0000},
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	Date-Added = {2018-05-18 14:57:39 +0000},
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	Date-Added = {2018-05-18 14:57:39 +0000},
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	Date-Added = {2018-05-18 14:57:39 +0000},
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	Date-Added = {2018-05-18 14:57:39 +0000},
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	Date-Added = {2018-05-18 14:57:39 +0000},
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	Date-Added = {2018-05-18 14:57:39 +0000},
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@misc{additionalmaterial,
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	Date-Modified = {2018-05-18 14:55:54 +0000},
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@article{chowdhery2022palm,
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	Date-Added = {2018-05-18 14:55:54 +0000},
	Date-Modified = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
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@inproceedings{osband2020bsuite,
    title={Behaviour Suite for Reinforcement Learning},
    author={Osband, Ian and
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            Hessel, Matteo and
            Aslanides, John and
            Sezener, Eren and
            Saraiva, Andre and
            McKinney, Katrina and
            Lattimore, Tor and
            {Sz}epesv{\'a}ri, Csaba and
            Singh, Satinder and
            Van Roy, Benjamin and
            Sutton, Richard and
            Silver, David and
            van Hasselt, Hado},
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    year={2020},
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	Date-Added = {2018-05-18 14:55:54 +0000},
	Date-Modified = {2018-05-18 14:55:54 +0000},
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	Booktitle = {Proc. of ICML},
	Date-Added = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
	Date-Modified = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
	Date-Modified = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
	Date-Modified = {2018-05-18 14:55:54 +0000},
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	Year = {2012}}

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	Date-Added = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
	Date-Modified = {2018-05-18 14:55:54 +0000},
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	Author = {Lillicrap, Timothy P. and Hunt, Jonathan J. and Pritzel, Alexander and Heess, Nicolas and Erez, Tom and Tassa, Yuval and Silver, David and Wierstra, Daan},
	Date-Added = {2018-05-18 14:55:54 +0000},
	Date-Modified = {2018-05-18 14:55:54 +0000},
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	Year = {2015}}

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	Date-Added = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
	Date-Modified = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
	Date-Modified = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
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@book{vapnik1998statistical,
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	Date-Added = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
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	Date-Added = {2018-05-18 14:55:54 +0000},
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	Author = {Oriol Vinyals and Timo Ewalds and Sergey Bartunov and Petko Georgiev and Alexander Sasha Vezhnevets and Michelle Yeo and Alireza Makhzani and Heinrich K{\"{u}}ttler and John Agapiou and Julian Schrittwieser and John Quan and Stephen Gaffney and Stig Petersen and Karen Simonyan and Tom Schaul and Hado van Hasselt and David Silver and Timothy P. Lillicrap and Kevin Calderone and Paul Keet and Anthony Brunasso and David Lawrence and Anders Ekermo and Jacob Repp and Rodney Tsing},
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	Timestamp = {Mon, 03 Jul 2017 13:29:02 +0200},
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	Journal = {CoRR},
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	Title = {ViZDoom: {A} Doom-based {AI} Research Platform for Visual Reinforcement Learning},
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	Journal = {CoRR},
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	Title = {DeepMind Lab},
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@article{espeholt2018rawbeast,
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	Author = {Lasse Espeholt and Hubert Soyer and R{\'{e}}mi Munos and Karen Simonyan and Volodymyr Mnih and Tom Ward and Yotam Doron and Vlad Firoiu and Tim Harley and Iain Dunning and Shane Legg and Koray Kavukcuoglu},
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	Journal = {CoRR},
	Timestamp = {Thu, 01 Mar 2018 15:00:45 +0100},
	Title = {{IMPALA:} Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures},
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	Year = {2018},
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@article{martin2017countbased,
	Archiveprefix = {arXiv},
	Author = {Jarryd Martin and Suraj Narayanan Sasikumar and Tom Everitt and Marcus Hutter},
	Bibsource = {dblp computer science bibliography, https://dblp.org},
	Biburl = {https://dblp.org/rec/bib/journals/corr/MartinSEH17},
	Date-Added = {2018-05-12 20:15:26 +0000},
	Date-Modified = {2018-05-12 20:15:54 +0000},
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	Journal = {CoRR},
	Timestamp = {Mon, 06 Nov 2017 12:14:00 +0100},
	Title = {Count-Based Exploration in Feature Space for Reinforcement Learning},
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@article {silver2018alphazeroscience,
	author = {Silver, David and Hubert, Thomas and Schrittwieser, Julian and Antonoglou, Ioannis and Lai, Matthew and Guez, Arthur and Lanctot, Marc and Sifre, Laurent and Kumaran, Dharshan and Graepel, Thore and Lillicrap, Timothy and Simonyan, Karen and Hassabis, Demis},
	title = {A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play},
	volume = {362},
	number = {6419},
	pages = {1140--1144},
	year = {2018},
	doi = {10.1126/science.aar6404},
	publisher = {American Association for the Advancement of Science},
	abstract = {Computers can beat humans at increasingly complex games, including chess and Go. However, these programs are typically constructed for a particular game, exploiting its properties, such as the symmetries of the board on which it is played. Silver et al. developed a program called AlphaZero, which taught itself to play Go, chess, and shogi (a Japanese version of chess) (see the Editorial, and the Perspective by Campbell). AlphaZero managed to beat state-of-the-art programs specializing in these three games. The ability of AlphaZero to adapt to various game rules is a notable step toward achieving a general game-playing system.Science, this issue p. 1140; see also pp. 1087 and 1118The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go.},
	issn = {0036-8075},
	URL = {https://science.sciencemag.org/content/362/6419/1140},
	eprint = {https://science.sciencemag.org/content/362/6419/1140.full.pdf},
	journal = {Science}
}

@article{wang2018glue,
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@article{gleave2022uncertainty,
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	 booktitle = {Proceedings of the 27th International Joint Conference on Artificial Intelligence},
	 series = {IJCAI'18},
	 year = {2018},
	 isbn = {978-0-9992411-2-7},
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	 pages = {5573--5577},
	 numpages = {5},
	 url = {http://dl.acm.org/citation.cfm?id=3304652.3304802},
	 acmid = {3304802},
	 publisher = {AAAI Press},
} 


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@inproceedings{osband2022neural,
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@misc{hendrycks2019benchmarking,
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      year={2019},
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      primaryClass={cs.LG}
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@misc{zhang2022memo,
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      author={Marvin Zhang and Sergey Levine and Chelsea Finn},
      year={2022},
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      archivePrefix={arXiv},
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@inproceedings{dwaracherla2019posterior,
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@article{wilson2021evaluating,
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@Inbook{Goan2020,
author="Goan, Ethan
and Fookes, Clinton",
editor="Mengersen, Kerrie L.
and Pudlo, Pierre
and Robert, Christian P.",
title="Bayesian Neural Networks: An Introduction and Survey",
bookTitle="Case Studies in Applied {B}ayesian Data Science: CIRM Jean-Morlet Chair, Fall 2018",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="45--87",
abstract="Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning tasks such as detection, regression and classification across the domains of computer vision, speech recognition and natural language processing. Despite their success, they are often implemented in a frequentist scheme, meaning they are unable to reason about uncertainty in their predictions. This article introduces Bayesian Neural Networks (BNNs) and the seminal research regarding their implementation. Different approximate inference methods are compared, and used to highlight where future research can improve on current methods.",
isbn="978-3-030-42553-1",
doi="10.1007/978-3-030-42553-1_3",
url="https://doi.org/10.1007/978-3-030-42553-1_3"
}

@article{WangYeung2020,
author = {Wang, Hao and Yeung, Dit-Yan},
title = {A Survey on {B}ayesian Deep Learning},
year = {2020},
issue_date = {September 2021},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {53},
number = {5},
issn = {0360-0300},
url = {https://doi.org/10.1145/3409383},
doi = {10.1145/3409383},
abstract = {A comprehensive artificial intelligence system needs to not only perceive the environment with different “senses” (e.g., seeing and hearing) but also infer the world’s conditional (or even causal) relations and corresponding uncertainty. The past decade has seen major advances in many perception tasks, such as visual object recognition and speech recognition, using deep learning models. For higher-level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible. In recent years, Bayesian deep learning has emerged as a unified probabilistic framework to tightly integrate deep learning and Bayesian models.1 In this general framework, the perception of text or images using deep learning can boost the performance of higher-level inference and, in turn, the feedback from the inference process is able to enhance the perception of text or images. This survey provides a comprehensive introduction to Bayesian deep learning and reviews its recent applications on recommender systems, topic models, control, and so on. We also discuss the relationship and differences between Bayesian deep learning and other related topics, such as Bayesian treatment of neural networks.},
journal = {ACM Comput. Surv.},
month = {sep},
articleno = {108},
numpages = {37},
keywords = {generative models, Deep learning, Bayesian networks, probabilistic graphical models}
}

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@article{askell2021general,
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@article{hoffmann2022training,
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@article{ziegler2019fine,
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@article{cobbe2021training,
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@misc{plex2022,
  doi = {10.48550/ARXIV.2207.07411},
  url = {https://arxiv.org/abs/2207.07411},
  author = {Tran, Dustin and Liu, Jeremiah and Dusenberry, Michael W. and Phan, Du and Collier, Mark and Ren, Jie and Han, Kehang and Wang, Zi and Mariet, Zelda and Hu, Huiyi and Band, Neil and Rudner, Tim G. J. and Singhal, Karan and Nado, Zachary and van Amersfoort, Joost and Kirsch, Andreas and Jenatton, Rodolphe and Thain, Nithum and Yuan, Honglin and Buchanan, Kelly and Murphy, Kevin and Sculley, D. and Gal, Yarin and Ghahramani, Zoubin and Snoek, Jasper and Lakshminarayanan, Balaji},
  keywords = {Machine Learning (cs.LG), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Plex: Towards Reliability using Pretrained Large Model Extensions},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}