| On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 4927 | 2021 |
| Global and regional hearing impairment prevalence: an analysis of 42 studies in 29 countries G Stevens, S Flaxman, E Brunskill, M Mascarenhas, CD Mathers, ... The European Journal of Public Health 23 (1), 146-152, 2013 | 892 | 2013 |
| Data-efficient off-policy policy evaluation for reinforcement learning P Thomas, E Brunskill International conference on machine learning, 2139-2148, 2016 | 785 | 2016 |
| Towards the systematic reporting of the energy and carbon footprints of machine learning P Henderson, J Hu, J Romoff, E Brunskill, D Jurafsky, J Pineau Journal of Machine Learning Research 21 (248), 1-43, 2020 | 626 | 2020 |
| Building peer-to-peer systems with Chord, a distributed lookup service F Dabek, E Brunskill, MF Kaashoek, D Karger, R Morris, I Stoica, ... Proceedings Eighth Workshop on Hot Topics in Operating Systems, 81-86, 2001 | 390 | 2001 |
| Designing mobile interfaces for novice and low-literacy users I Medhi, S Patnaik, E Brunskill, SNN Gautama, W Thies, K Toyama ACM Transactions on Computer-Human Interaction (TOCHI) 18 (1), 1-28, 2011 | 345 | 2011 |
| 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 | 341 | 2017 |
| 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 | 321 | 2019 |
| Sample complexity of episodic fixed-horizon reinforcement learning C Dann, E Brunskill Advances in Neural Information Processing Systems 28, 2015 | 284 | 2015 |
| New potentials for data-driven intelligent tutoring system development and optimization KR Koedinger, E Brunskill, RSJ Baker, EA McLaughlin, J Stamper AI Magazine 34 (3), 27-41, 2013 | 273 | 2013 |
| Faster teaching via pomdp planning AN Rafferty, E Brunskill, TL Griffiths, P Shafto Cognitive science 40 (6), 1290-1332, 2016 | 269 | 2016 |
| Learning near optimal policies with low inherent bellman error A Zanette, A Lazaric, M Kochenderfer, E Brunskill International Conference on Machine Learning, 10978-10989, 2020 | 260 | 2020 |
| Offline policy evaluation across representations with applications to educational games. T Mandel, YE Liu, S Levine, E Brunskill, Z Popovic AAMAS 1077, 2014 | 254 | 2014 |
| 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 | 237 | 2020 |
| Quizbot: A dialogue-based adaptive learning system for factual knowledge S Ruan, L Jiang, J Xu, BJK Tham, Z Qiu, Y Zhu, EL Murnane, E Brunskill, ... Proceedings of the 2019 CHI conference on human factors in computing systems …, 2019 | 231 | 2019 |
| 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 | 217 | 2020 |
| Efficient exploration through bayesian deep q-networks K Azizzadenesheli, E Brunskill, A Anandkumar 2018 Information Theory and Applications Workshop (ITA), 1-9, 2018 | 215 | 2018 |
| 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 | 210 | 2019 |
| On the opportunities and risks of foundation models. arXiv R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258 10, 2021 | 198 | 2021 |
| The Impact on Individualizing Student Models on Necessary Practice Opportunities. JI Lee, E Brunskill International educational data mining society, 2012 | 180 | 2012 |
Philip ThomasUniversity of Massachusetts Amherstایمیل تأیید شده در cs.umass.edu
