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Gellért Weisz
Gellért Weisz
DeepMind, UCL, gellert@google.com
Verified email at google.com
Title
Cited by
Cited by
Year
Learning with good feature representations in bandits and in rl with a generative model
T Lattimore, C Szepesvari, G Weisz
International conference on machine learning, 5662-5670, 2020
1752020
Politex: Regret bounds for policy iteration using expert prediction
Y Abbasi-Yadkori, P Bartlett, K Bhatia, N Lazic, C Szepesvari, G Weisz
International Conference on Machine Learning, 3692-3702, 2019
1322019
Sample efficient deep reinforcement learning for dialogue systems with large action spaces
G Weisz, P Budzianowski, PH Su, M Gašić
IEEE/ACM Transactions on Audio, Speech, and Language Processing 26 (11 …, 2018
912018
Exponential lower bounds for planning in mdps with linearly-realizable optimal action-value functions
G Weisz, P Amortila, C Szepesvári
Algorithmic Learning Theory, 1237-1264, 2021
892021
LeapsAndBounds: A method for approximately optimal algorithm configuration
G Weisz, A Gyorgy, C Szepesvári
International Conference on Machine Learning, 5257-5265, 2018
402018
Exploration-enhanced politex
Y Abbasi-Yadkori, N Lazic, C Szepesvari, G Weisz
arXiv preprint arXiv:1908.10479, 2019
312019
CapsAndRuns: An improved method for approximately optimal algorithm configuration
G Weisz, A Gyorgy, C Szepesvári
International Conference on Machine Learning, 6707-6715, 2019
242019
On query-efficient planning in mdps under linear realizability of the optimal state-value function
G Weisz, P Amortila, B Janzer, Y Abbasi-Yadkori, N Jiang, C Szepesvári
Conference on Learning Theory, 4355-4385, 2021
202021
Tensorplan and the few actions lower bound for planning in mdps under linear realizability of optimal value functions
G Weisz, C Szepesvári, A György
International Conference on Algorithmic Learning Theory, 1097-1137, 2022
142022
Confident Approximate Policy Iteration for Efficient Local Planning in -realizable MDPs
G Weisz, A György, T Kozuno, C Szepesvári
Advances in Neural Information Processing Systems 35, 25547-25559, 2022
92022
Inter-device data transfer based on barcodes
J Chien, R Ian Orton, G Weisz, V Varma
US Patent 9,600,701, 2017
72017
ImpatientCapsAndRuns: Approximately optimal algorithm configuration from an infinite pool
G Weisz, A György, WI Lin, D Graham, K Leyton-Brown, C Szepesvari, ...
Advances in Neural Information Processing Systems 33, 17478-17488, 2020
52020
Optimistic natural policy gradient: a simple efficient policy optimization framework for online rl
Q Liu, G Weisz, A György, C Jin, C Szepesvári
Advances in Neural Information Processing Systems 36, 2024
42024
Exponential Hardness of Reinforcement Learning with Linear Function Approximation
S Liu, G Mahajan, D Kane, S Lovett, G Weisz, C Szepesvári
The Thirty Sixth Annual Conference on Learning Theory, 1588-1617, 2023
4*2023
Online RL in Linearly -Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore
G Weisz, A György, C Szepesvári
Advances in Neural Information Processing Systems 36, 2024
12024
The Complexity of Reinforcement Learning with Linear Function Approximation
G Weisz
UCL (Univesity College London), 2024
2024
P: Regret Bounds for Policy Iteration Using Expert Prediction
Y Abbasi-Yadkori, PL Bartle, K Bhatia, N Lazić, C Szepesvári, G Weisz
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Articles 1–17