Fetching the paper…
Reading the bibliography…
We provide new insights on eluder dimension, a complexity measure that has been extensively used to bound the regret of algorithms for online bandits and reinforcement learning with function approximation.
Geometrical realization of set systems and probabilistic communication complexity
N. Alon, P. Frankl, and V. Rödl · 1985
Earlier work this paper cites.
Classification theory: and the number of non-isomorphic models
S. Shelah · 1990
Earlier work this paper cites.
A shorter model theory
W. Hodges et al · 1997
Earlier work this paper cites.
Limitations of learning via embeddings in euclidean half spaces
S. Ben-David, N. Eiron, and H. U. Simon · 2002
Earlier work this paper cites.
A linear lower bound on the unbounded error probabilistic communication complexity
J. Forster · 2002
Earlier work this paper cites.
An algorithmic theory of learning: Robust concepts and random projection
R. I. Arriaga and S. S. Vempala · 2006
Earlier work this paper cites.
Agnostic online learning
S. Ben-David, D. Pál, and S. Shalev-Shwartz · 2009
Earlier work this paper cites.
Gaussian process optimization in the bandit setting: No regret and experimental design
N. Srinivas, A. Krause, S. M. Kakade, and M. Seeger · 2009
Earlier work this paper cites.
Parametric bandits: The generalized linear case
S. Filippi, O. Cappe, A. Garivier, and C. Szepesvári · 2010
Earlier work this paper cites.
D. J. Foster, A. Rakhlin, D. Simchi-Levi, and Y. Xu · 2010
Earlier work this paper cites.
Eluder dimension and the sample complexity of optimistic exploration
D. Russo and B. Van Roy · 2013
Earlier work this paper cites.
Efficient exploration and value function generalization in deterministic systems
Z. Wen and B. Van Roy · 2013
Earlier work this paper cites.
Model-based reinforcement learning and the eluder dimension
I. Osband and B. Van Roy · 2014
Earlier work this paper cites.
Understanding machine learning: From theory to algorithms
S. Shalev-Shwartz and S. Ben-David · 2014
Earlier work this paper cites.
Minimax analysis of active learning
S. Hanneke and L. Yang · 2015
Cited alongside, same era.
Sign rank versus VC dimension
N. Alon, S. Moran, and A. Yehudayoff · 2016
Cited alongside, same era.
An information-theoretic analysis of thompson sampling
D. Russo and B. Van Roy · 2016
Cited alongside, same era.
Contextual decision processes with low Bellman rank are PAC-learnable
N. Jiang, A. Krishnamurthy, A. Agarwal, J. Langford, and R. E. Schapire · 2017
Cited alongside, same era.
Provably optimal algorithms for generalized linear contextual bandits
L. Li, Y. Lu, and D. Zhou · 2017
Cited alongside, same era.
A survey of quantitative bounds for hypergraph ramsey problems. arxiv e-prints (july 2017)
D. Mubayi and A. Suk · 2017
Cited alongside, same era.
Approximate is good enough: Probabilistic variants of dimensional and margin complexity
P. Kamath, O. Montasser, and N. Srebro · 2020
Later among the works it cites.
Randomized exploration in generalized linear bandits
B. Kveton, M. Zaheer, C. Szepesvari, L. Li, M. Ghavamzadeh, and C. Boutilier · 2020
Later among the works it cites.
On the sample complexity of reinforcement learning with policy space generalization
W. Mou, Z. Wen, and X. Chen · 2020
Later among the works it cites.
Reinforcement learning with general value function approximation: Provably efficient approach via bounded eluder dimension
R. Wang, R. R. Salakhutdinov, and L. Yang · 2020
Later among the works it cites.
Adversarial laws of large numbers and optimal regret in online classification
N. Alon, O. Ben-Eliezer, Y. Dagan, S. Moran, M. Naor, and E. Yogev · 2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
High-dimensional probability: An introduction with applications in data science , volume 47
R. Vershynin · 2018
Cited alongside, same era.
Private pac learning implies finite littlestone dimension
N. Alon, R. Livni, M. Malliaris, and S. Moran · 2019
Cited alongside, same era.
Model-based rl in contextual decision processes: Pac bounds and exponential improvements over model-free approaches
W. Sun, N. Jiang, A. Krishnamurthy, A. Agarwal, and J. Langford · 2019
Cited alongside, same era.
Optimism in reinforcement learning with generalized linear function approximation
Y. Wang, R. Wang, S. S. Du, and A. Krishnamurthy · 2019
Cited alongside, same era.
Model-based reinforcement learning with value-targeted regression
A. Ayoub, Z. Jia, C. Szepesvari, M. Wang, and L. Yang · 2020
Cited alongside, same era.
An equivalence between private classification and online prediction
M. Bun, R. Livni, and S. Moran · 2020
Cited alongside, same era.
Active online learning with hidden shifting domains
Y. Chen, H. Luo, T. Ma, and C. Zhang · 2021
Closest in time.
K. Dong, J. Yang, and T. Ma · 2021
Closest in time.
Bilinear classes: A structural framework for provable generalization in rl
S. Du, S. Kakade, J. Lee, S. Lovett, G. Mahajan, W. Sun, and R. Wang · 2021
Closest in time.
Risk-sensitive reinforcement learning with function approximation: A debiasing approach
Y. Fei, Z. Yang, and Z. Wang · 2021
Closest in time.
Provably correct optimization and exploration with non-linear policies
F. Feng, W. Yin, A. Agarwal, and L. Yang · 2021
Closest in time.
The statistical complexity of interactive decision making
D. J. Foster, S. M. Kakade, J. Qian, and A. Rakhlin · 2021
Closest in time.
Randomized exploration in reinforcement learning with general value function approximation
H. Ishfaq, Q. Cui, V. Nguyen, A. Ayoub, Z. Yang, Z. Wang, D. Precup, and L. Yang · 2021
Closest in time.
Personal communication
G. Mahajan and S. Lovett · 2021
Closest in time.
Representation learning beyond linear prediction functions
Z. Xu and A. Tewari · 2021
Closest in time.