Fetching the paper…
Reading the bibliography…
A fundamental challenge for any intelligent system is prediction: given some inputs, can you predict corresponding outcomes? Most work on supervised learning has focused on producing accurate marginal predictions for each input.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
Thompson, W. R · 1933
Earlier work this paper cites.
A tutorial on Thompson sampling
Russo, D. J., Van Roy, B., Kazerouni, A., Osband, I., and Wen, Z · 1935
Earlier work this paper cites.
Combinatorial optimization: algorithms and complexity
Papadimitriou, C. H. and Steiglitz, K · 1998
Earlier work this paper cites.
Elements of information theory
Cover, T. M · 1999
Earlier work this paper cites.
The elements of statistical learning , volume 1
Friedman, J., Hastie, T., Tibshirani, R., et al · 2001
Earlier work this paper cites.
Finite-time analysis of the multiarmed bandit problem
Auer, P., Cesa-Bianchi, N., and Fischer, P · 2002
Earlier work this paper cites.
Mean absolute deviations of sample means and minimally concentrated binomials
Mattner, L · 2003
Earlier work this paper cites.
Pattern recognition
Bishop, C. M · 2006
Earlier work this paper cites.
An empirical evaluation of thompson sampling
Chapelle, O. and Li, L · 2011
Earlier work this paper cites.
Online learning and online convex optimization
Shalev-Shwartz, S. et al · 2011
Earlier work this paper cites.
Bayesian learning via stochastic gradient Langevin dynamics
Welling, M. and Teh, Y. W · 2011
Cited alongside, same era.
Weight uncertainty in neural network
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
Cited alongside, same era.
Adam: A Method for Stochastic Optimization
Kingma, D. and Ba, J · 2015
Cited alongside, same era.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
Cited alongside, same era.
Bootstrapped Thompson sampling and deep exploration
Osband, I. and Van Roy, B · 2015
Cited alongside, same era.
Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
Cited alongside, same era.
Riquelme, C., Tucker, G., and Snoek, J · 2018
Later among the works it cites.
Learning to optimize via information-directed sampling
Russo, D. and Van Roy, B · 2018
Later among the works it cites.
Thompson sampling with approximate inference
Phan, M., Abbasi-Yadkori, Y., and Domke, J · 2019
Later among the works it cites.
Scalable thompson sampling via optimal transport
Zhang, R., Wen, Z., Chen, C., and Carin, L · 2019
Later among the works it cites.
Bandit algorithms
Lattimore, T. and Szepesvári, C · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep learning
Goodfellow, I., Bengio, Y., and Courville, A · 2016
Cited alongside, same era.
An information-theoretic analysis of Thompson sampling
Russo, D. and Van Roy, B · 2016
Cited alongside, same era.
Ensemble sampling
Lu, X. and Van Roy, B · 2017
Cited alongside, same era.
Graphical models meet bandits: A variational thompson sampling approach
Yu, T., Kveton, B., Wen, Z., Zhang, R., and Mengshoel, O. J · 2020
Later among the works it cites.
Reinforcement learning, bit by bit
Lu, X., Van Roy, B., Dwaracherla, V., Ibrahimi, M., Osband, I., and Wen, Z · 2021
Closest in time.
Evaluating predictive distributions: Does bayesian deep learning work?
Osband, I., Wen, Z., Asghari, S. M., Dwaracherla, V., Hao, B., Ibrahimi, M., Lawson, D., Lu, X., O’Donoghue, B., and Van Roy, B · 2021
Closest in time.
Beyond marginal uncertainty: How accurately can Bayesian regression models estimate posterior predictive correlations?
Wang, C., Sun, S., and Grosse, R · 2021
Closest in time.