Fetching the paper…
Reading the bibliography…
These lecture notes give a statistical perspective on the foundations of reinforcement learning and interactive decision making.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
W. R. Thompson · 1933
Earlier work this paper cites.
Some aspects of the sequential design of experiments
H. Robbins · 1952
Earlier work this paper cites.
The theory of dynamic programming
R. Bellman · 1954
Earlier work this paper cites.
On general minimax theorems
M. Sion · 1958
Earlier work this paper cites.
The equivalence of two extremum problems
J. Kiefer and J. Wolfowitz · 1960
Earlier work this paper cites.
Asymptotically efficient adaptive allocation rules
T. L. Lai and H. Robbins · 1985
Earlier work this paper cites.
Complexity of strings in the class of markov sources
J. Rissanen · 1986
Earlier work this paper cites.
Geometrizing rates of convergence
D. L. Donoho and R. C. Liu · 1987
Earlier work this paper cites.
Universal sequential coding of single messages
Y. M. Shtar’kov · 1987
Earlier work this paper cites.
Universal portfolios
T. M. Cover · 1991
Earlier work this paper cites.
Sample mean based index policies by o (log n) regret for the multi-armed bandit problem
R. Agrawal · 1995
Earlier work this paper cites.
A game of prediction with expert advice
V. Vovk · 1995
Earlier work this paper cites.
Assouad, Fano, and Le Cam
B. Yu · 1997
Earlier work this paper cites.
Associative reinforcement learning using linear probabilistic concepts
N. Abe and P. M. Long · 1999
Earlier work this paper cites.
Minimax regret under log loss for general classes of experts
N. Cesa-Bianchi and G. Lugosi · 1999
Earlier work this paper cites.
Worst case prediction over sequences under log loss
M. Opper and D. Haussler · 1999
Earlier work this paper cites.
Using confidence bounds for exploitation-exploration trade-offs
P. Auer · 2002
Earlier work this paper cites.
Finite-time analysis of the multiarmed bandit problem
P. Auer, N. Cesa-Bianchi, and P. Fischer · 2002
Earlier work this paper cites.
Efficient algorithms for universal portfolios
A. Kalai and S. Vempala · 2002
Earlier work this paper cites.
Introduction to statistical learning theory
O. Bousquet, S. Boucheron, and G. Lugosi · 2004
Earlier work this paper cites.
Nearly tight bounds for the continuum-armed bandit problem
R. Kleinberg · 2004
Earlier work this paper cites.
Theory of classification: A survey of some recent advances
S. Boucheron, O. Bousquet, and G. Lugosi · 2005
Earlier work this paper cites.
Online convex optimization in the bandit setting: gradient descent without a gradient
A. D. Flaxman, A. T. Kalai, and H. B. McMahan · 2005
Earlier work this paper cites.
Prediction, Learning, and Games
N. Cesa-Bianchi and G. Lugosi · 2006
Earlier work this paper cites.
Improved rates for the stochastic continuum-armed bandit problem
P. Auer, R. Ortner, and C. Szepesvári · 2007
Earlier work this paper cites.
Stochastic linear optimization under bandit feedback
V. Dani, T. P. Hayes, and S. M. Kakade · 2008
Earlier work this paper cites.
The epoch-greedy algorithm for multi-armed bandits with side information
J. Langford and T. Zhang · 2008
Earlier work this paper cites.
Introduction to Nonparametric Estimation
A. B. Tsybakov · 2008
Earlier work this paper cites.
Neural network learning: Theoretical foundations
M. Anthony and P. L. Bartlett · 2009
Earlier work this paper cites.
Minimax policies for adversarial and stochastic bandits
J.-Y. Audibert and S. Bubeck · 2009
Cited alongside, same era.
A unifying framework for computational reinforcement learning theory
L. Li · 2009
Cited alongside, same era.
Factorizing personalized markov chains for next-basket recommendation
S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme · 2010
Cited alongside, same era.
Improved algorithms for linear stochastic bandits
Y. Abbasi-Yadkori, D. Pál, and C. Szepesvári · 2011
Cited alongside, same era.
Bandits, query learning, and the haystack dimension
K. Amin, M. Kearns, and U. Syed · 2011
Cited alongside, same era.
Contextual bandits with linear payoff functions
W. Chu, L. Li, L. Reyzin, and R. E. Schapire · 2011
Cited alongside, same era.
Bandits and experts in metric spaces
R. Kleinberg, A. Slivkins, and E. Upfal · 2019
Later among the works it cites.
An information-theoretic approach to minimax regret in partial monitoring
T. Lattimore and C. Szepesvári · 2019
Later among the works it cites.
Sample-optimal parametric Q-learning using linearly additive features
L. Yang and M. Wang · 2019
Later among the works it cites.
FLAMBE: Structural complexity and representation learning of low rank MDPs
A. Agarwal, S. Kakade, A. Krishnamurthy, and W. Sun · 2020
Later among the works it cites.
Model-based reinforcement learning with value-targeted regression
A. Ayoub, Z. Jia, C. Szepesvari, M. Wang, and L. Yang · 2020
Later among the works it cites.
Tight bounds on minimax regret under logarithmic loss via self-concordance
B. Bilodeau, D. J. Foster, and D. Roy · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Analysis of thompson sampling for the multi-armed bandit problem
S. Agrawal and N. Goyal · 2012
Cited alongside, same era.
Statistical learning and sequential prediction, 2012
A. Rakhlin and K. Sridharan · 2012
Cited alongside, same era.
Stochastic convex optimization with bandit feedback
A. Agarwal, D. P. Foster, D. Hsu, S. M. Kakade, and A. Rakhlin · 2013
Cited alongside, same era.
Thompson sampling for contextual bandits with linear payoffs
S. Agrawal and N. Goyal · 2013
Cited alongside, same era.
Eluder dimension and the sample complexity of optimistic exploration
D. Russo and B. Van Roy · 2013
Cited alongside, same era.
Learning to optimize via posterior sampling
D. Russo and B. Van Roy · 2014
Cited alongside, same era.
Later among the works it cites.
On the sample complexity of the linear quadratic regulator
S. Dean, H. Mania, N. Matni, B. Recht, and S. Tu · 2020
Later among the works it cites.
Beyond UCB: Optimal and efficient contextual bandits with regression oracles
D. J. Foster and A. Rakhlin · 2020
Later among the works it cites.
Adapting to misspecification in contextual bandits
D. J. Foster, C. Gentile, M. Mohri, and J. Zimmert · 2020
Later among the works it cites.
Time-uniform chernoff bounds via nonnegative supermartingales
S. R. Howard, A. Ramdas, J. McAuliffe, and J. Sekhon · 2020
Later among the works it cites.
Crush optimism with pessimism: Structured bandits beyond asymptotic optimality
K.-S. Jun and C. Zhang · 2020
Later among the works it cites.
Improved regret for zeroth-order adversarial bandit convex optimisation
T. Lattimore · 2020
Later among the works it cites.
Bandit algorithms
T. Lattimore and C. Szepesvári · 2020
Later among the works it cites.
Kinematic state abstraction and provably efficient rich-observation reinforcement learning
D. Misra, M. Henaff, A. Krishnamurthy, and J. Langford · 2020
Later among the works it cites.
Sample complexity of reinforcement learning using linearly combined model ensembles
A. Modi, N. Jiang, A. Tewari, and S. Singh · 2020
Later among the works it cites.
Information theoretic methods in statistics and computer science
Y. Polyanskiy · 2020
Later among the works it cites.
A provably efficient model-free posterior sampling method for episodic reinforcement learning
C. Dann, M. Mohri, T. Zhang, and J. Zimmert · 2021
Later among the works it cites.
Bilinear classes: A structural framework for provable generalization in RL
S. S. Du, S. M. Kakade, J. D. Lee, S. Lovett, G. Mahajan, W. Sun, and R. Wang · 2021
Later among the works it cites.
The statistical complexity of interactive decision making
D. J. Foster, S. M. Kakade, J. Qian, and A. Rakhlin · 2021
Later among the works it cites.
Bellman eluder dimension: New rich classes of RL problems, and sample-efficient algorithms
C. Jin, Q. Liu, and S. Miryoosefi · 2021
Later among the works it cites.
Eluder dimension and generalized rank
G. Li, P. Kamath, D. J. Foster, and N. Srebro · 2021
Later among the works it cites.
Bypassing the monster: A faster and simpler optimal algorithm for contextual bandits under realizability
D. Simchi-Levi and Y. Xu · 2021
Later among the works it cites.
An exponential lower bound for linearly-realizable MDPs with constant suboptimality gap
Y. Wang, R. Wang, and S. M. Kakade · 2021
Later among the works it cites.
Exponential lower bounds for planning in MDPs with linearly-realizable optimal action-value functions
G. Weisz, P. Amortila, and C. Szepesvári · 2021
Later among the works it cites.
Model-based RL with optimistic posterior sampling: Structural conditions and sample complexity
A. Agarwal and T. Zhang · 2022
Later among the works it cites.
Feel-good thompson sampling for contextual bandits and reinforcement learning
T. Zhang · 2022
Later among the works it cites.
A posterior sampling framework for interactive decision making
H. Zhong, W. Xiong, S. Zheng, L. Wang, Z. Wang, Z. Yang, and T. Zhang · 2022
Later among the works it cites.
Tight guarantees for interactive decision making with the decision-estimation coefficient
D. J. Foster, N. Golowich, and Y. Han · 2023
Closest in time.