Artikler med mandater om offentlig tilgang - Emma BrunskillLes mer
Ikke tilgjengelig noe sted: 1
Towards operationalizing outlier detection in community health programs
T McCarthy, B DeRenzi, J Blumenstock, E Brunskill
Proceedings of the Sixth International Conference on Information and …, 2013
Mandater: US National Institutes of Health
Tilgjengelige et eller annet sted: 61
Data-efficient off-policy policy evaluation for reinforcement learning
P Thomas, E Brunskill
International Conference on Machine Learning, 2139-2148, 2016
Mandater: US National Science Foundation, US Institute of Education Sciences
Unifying PAC and regret: Uniform PAC bounds for episodic reinforcement learning
C Dann, T Lattimore, E Brunskill
Advances in Neural Information Processing Systems 30, 2017
Mandater: US National Science Foundation
Tighter problem-dependent regret bounds in reinforcement learning without domain knowledge using value function bounds
A Zanette, E Brunskill
International Conference on Machine Learning, 7304-7312, 2019
Mandater: US National Science Foundation, US Department of Defense
Faster teaching via pomdp planning
AN Rafferty, E Brunskill, TL Griffiths, P Shafto
Cognitive science 40 (6), 1290-1332, 2016
Mandater: US National Science Foundation
Provably good batch off-policy reinforcement learning without great exploration
Y Liu, A Swaminathan, A Agarwal, E Brunskill
Advances in neural information processing systems 33, 1264-1274, 2020
Mandater: US National Science Foundation, US Department of Defense
Preventing undesirable behavior of intelligent machines
PS Thomas, B Castro da Silva, AG Barto, S Giguere, Y Brun, E Brunskill
Science 366 (6468), 999-1004, 2019
Mandater: US National Science Foundation, US Department of Education
Scaling up behavioral science interventions in online education
RF Kizilcec, J Reich, M Yeomans, C Dann, E Brunskill, G Lopez, S Turkay, ...
Proceedings of the National Academy of Sciences 117 (26), 14900-14905, 2020
Mandater: US National Science Foundation
Policy certificates: Towards accountable reinforcement learning
C Dann, L Li, W Wei, E Brunskill
International Conference on Machine Learning, 1507-1516, 2019
Mandater: US National Science Foundation
Frequentist regret bounds for randomized least-squares value iteration
A Zanette, D Brandfonbrener, E Brunskill, M Pirotta, A Lazaric
International Conference on Artificial Intelligence and Statistics, 1954-1964, 2020
Mandater: US Department of Defense
Provable benefits of actor-critic methods for offline reinforcement learning
A Zanette, MJ Wainwright, E Brunskill
Advances in neural information processing systems 34, 13626-13640, 2021
Mandater: US National Science Foundation, US Department of Defense
Learning when-to-treat policies
X Nie, E Brunskill, S Wager
Journal of the American Statistical Association 116 (533), 392-409, 2021
Mandater: US National Science Foundation
Where’s the reward? a review of reinforcement learning for instructional sequencing
S Doroudi, V Aleven, E Brunskill
International Journal of Artificial Intelligence in Education 29, 568-620, 2019
Mandater: US Institute of Education Sciences, US Department of Education
Being optimistic to be conservative: Quickly learning a CVaR policy
R Keramati, C Dann, A Tamkin, E Brunskill
Proceedings of the AAAI conference on artificial intelligence 34 (04), 4436-4443, 2020
Mandater: US National Science Foundation, US Department of Defense
Representation balancing mdps for off-policy policy evaluation
Y Liu, O Gottesman, A Raghu, M Komorowski, AA Faisal, F Doshi-Velez, ...
Advances in neural information processing systems 31, 2018
Mandater: US National Science Foundation
Off-policy policy evaluation for sequential decisions under unobserved confounding
H Namkoong, R Keramati, S Yadlowsky, E Brunskill
Advances in Neural Information Processing Systems 33, 18819-18831, 2020
Mandater: US National Science Foundation, US Department of Defense
A pac rl algorithm for episodic pomdps
ZD Guo, S Doroudi, E Brunskill
Artificial Intelligence and Statistics, 510-518, 2016
Mandater: US National Science Foundation
Interface design optimization as a multi-armed bandit problem
JD Lomas, J Forlizzi, N Poonwala, N Patel, S Shodhan, K Patel, ...
Proceedings of the 2016 CHI conference on human factors in computing systems …, 2016
Mandater: US Institute of Education Sciences
Provably efficient reward-agnostic navigation with linear value iteration
A Zanette, A Lazaric, MJ Kochenderfer, E Brunskill
Advances in Neural Information Processing Systems 33, 11756-11766, 2020
Mandater: US National Science Foundation, US Department of Defense
The queue method: Handling delay, heuristics, prior data, and evaluation in bandits
T Mandel, YE Liu, E Brunskill, Z Popović
Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015
Mandater: Hewlett Foundation, Bill & Melinda Gates Foundation
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