Ramtin Keramati
Cited by
Cited by
Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy
R Keramati, C Dann, A Tamkin, E Brunskill
The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-2020), 2020
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding
H Namkoong, R Keramati, S Yadlowsky, E Brunskill
Thirty-fourth Conference on Neural Information Processing Systems, NeurIPS 2020, 2020
Distributionally-Aware Exploration for CVaR Bandits
A Tamkin, R Keramati, C Dann, E Brunskill
Neural Information Processing Systems 2019 Workshop on Safety and Robustness …, 2019
Significant contribution of small icebergs to the freshwater budget in Greenland fjords
S Rezvanbehbahani, LA Stearns, R Keramati, S Shankar, ...
Communications earth & environment 1 (1), 31, 2020
Strategic object oriented reinforcement learning
R Keramati, J Whang, P Cho, E Brunskill
International Conference on Machine Learning (ICML) 2018 Workshop, 2018
Dynamics of the nanoneedle probe in trolling mode AFM
A Abdi, HN Pishkenari, R Keramati, M Minary-Jolandan
Nanotechnology 26 (20), 205702, 2015
Learning abstract models for strategic exploration and fast reward transfer
EZ Liu, R Keramati, S Seshadri, K Guu, P Pasupat, E Brunskill, P Liang
arXiv preprint arXiv:2007.05896, 2020
Molecular dynamics modeling of a nanomaterials-water surface interaction
H Nejat Pishkenari, R Keramati, A Abdi, M Minary-Jolandan
Journal of Applied Physics 119 (16), 2016
Identification of subgroups with similar benefits in off-policy policy evaluation
R Keramati, O Gottesman, LA Celi, F Doshi-Velez, E Brunskill
arXiv preprint arXiv:2111.14272, 2021
Value driven representation for human-in-the-loop reinforcement learning
R Keramati, E Brunskill
Proceedings of the 27th ACM Conference on User Modeling, Adaptation and …, 2019
Model-based offline reinforcement learning with local misspecification
K Dong, Y Flet-Berliac, A Nie, E Brunskill
Proceedings of the AAAI Conference on Artificial Intelligence 37 (6), 7423-7431, 2023
Robust Learning and Evaluation in Sequential Decision Making
R Keramati
Stanford University, 2021
Hierarchy-Driven Exploration for Reinforcement Learning
EZ Liu, R Keramati, S Seshadri, K Guu, P Pasupat, E Brunskill, P Liang
The system can't perform the operation now. Try again later.
Articles 1–13