Fetching the paper…
Reading the bibliography…
We study the neural-linear bandit model for solving sequential decision-making problems with high dimensional side information.
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.
Universal approximation bounds for superpositions of a sigmoidal function
Andrew R Barron · 1993
Earlier work this paper cites.
Semidefinite programming
Lieven Vandenberghe and Stephen Boyd · 1996
Earlier work this paper cites.
Using confidence bounds for exploitation-exploration trade-offs
Peter Auer · 2002
Earlier work this paper cites.
Kendall’s advanced theory of statistics, volume 2B: Bayesian inference , volume 2
Anthony O’Hagan and Jonathan J Forster · 2004
Earlier work this paper cites.
The epoch-greedy algorithm for multi-armed bandits with side information
John Langford and Tong Zhang · 2008
Earlier work this paper cites.
Distributed inference for latent dirichlet allocation
David Newman, Padhraic Smyth, Max Welling, and Arthur U Asuncion · 2008
Earlier work this paper cites.
Improved algorithms for linear stochastic bandits
Yasin Abbasi-Yadkori, David Pal, and Csaba Szepesvari · 2011
Earlier work this paper cites.
Contextual bandits with linear payoff functions
Wei Chu, Lihong Li, Lev Reyzin, and Robert Schapire · 2011
Earlier work this paper cites.
Thompson sampling for contextual bandits with linear payoffs
Shipra Agrawal and Navin Goyal · 2013
Earlier work this paper cites.
Convolutional neural networks for sentence classification
Yoon Kim · 2014
Cited alongside, same era.
The reusable holdout: Preserving validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Roth · 2015
Cited alongside, same era.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Cited alongside, same era.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Cited alongside, same era.
Ye Zhang and Byron Wallace · 2015
Cited alongside, same era.
Unifying count-based exploration and intrinsic motivation
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2017
Later among the works it cites.
Shallow updates for deep reinforcement learning
Nir Levine, Tom Zahavy, Daniel J Mankowitz, Aviv Tamar, and Shie Mannor · 2017
Later among the works it cites.
Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell · 2017
Later among the works it cites.
Efficient exploration through bayesian deep q-networks
Kamyar Azizzadenesheli, Emma Brunskill, and Animashree Anandkumar · 2018
Later among the works it cites.
Bandit algorithms
Tor Lattimore and Csaba Szepesvári · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Remi Munos · 2016
Cited alongside, same era.
CVXPY: A Python-embedded modeling language for convex optimization
Steven Diamond and Stephen Boyd · 2016
Cited alongside, same era.
Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Cited alongside, same era.
Mastering the game of Go with deep neural networks and tree search
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
Cited alongside, same era.
Linear thompson sampling revisited
Marc Abeille, Alessandro Lazaric, et al · 2017
Cited alongside, same era.
Learn what not to learn: Action elimination with deep reinforcement learning
Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J Mankowitz, and Shie Mannor
Cited in the paper.
Is a picture worth a thousand words? a deep multi-modal fusion architecture for product classification in e-commerce
Tom Zahavy, Alessandro Magnani, Abhinandan Krishnan, and Shie Mannor
Cited in the paper.
Brendan O’Donoghue, Ian Osband, Remi Munos, and Volodymyr Mnih · 2018
Later among the works it cites.
Randomized prior functions for deep reinforcement learning
Ian Osband, John Aslanides, and Cassirer Albin · 2018
Later among the works it cites.
Deep bayesian bandits showdown
Carlos Riquelme, George Tucker, and Jasper Snoek · 2018
Later among the works it cites.
A tutorial on thompson sampling
Daniel J Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen, et al · 2018
Later among the works it cites.