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Michael (misha) Laskin
Michael (misha) Laskin
Senior Research Scientist, DeepMind
Verified email at google.com - Homepage
Title
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Cited by
Year
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
M Laskin, A Srinivas, P Abbeel
Proceedings of the 37th International Conference on Machine Learning, Vienna …, 2020
434*2020
Reinforcement learning with augmented data
M Laskin, K Lee, A Stooke, L Pinto, P Abbeel, A Srinivas
Advances in neural information processing systems 33, 19884-19895, 2020
2692020
Decision transformer: Reinforcement learning via sequence modeling
L Chen, K Lu, A Rajeswaran, K Lee, A Grover, M Laskin, P Abbeel, ...
Advances in neural information processing systems 34, 15084-15097, 2021
1632021
Fractional quantum Hall effect in a curved space: gravitational anomaly and electromagnetic response
T Can, M Laskin, P Wiegmann
Physical review letters 113 (4), 046803, 2014
1332014
Decoupling representation learning from reinforcement learning
A Stooke, K Lee, P Abbeel, M Laskin
International Conference on Machine Learning, 9870-9879, 2021
1172021
Geometry of quantum Hall states: Gravitational anomaly and transport coefficients
T Can, M Laskin, PB Wiegmann
Annals of Physics 362, 752-794, 2015
832015
Sunrise: A simple unified framework for ensemble learning in deep reinforcement learning
K Lee, M Laskin, A Srinivas, P Abbeel
International Conference on Machine Learning, 6131-6141, 2021
742021
Emergent conformal symmetry and geometric transport properties of quantum hall states on singular surfaces
T Can, YH Chiu, M Laskin, P Wiegmann
Physical review letters 117 (26), 266803, 2016
472016
Collective field theory for quantum Hall states
M Laskin, T Can, P Wiegmann
Physical Review B 92 (23), 235141, 2015
432015
A framework for efficient robotic manipulation
A Zhan, P Zhao, L Pinto, P Abbeel, M Laskin
arXiv preprint arXiv:2012.07975, 2020
372020
Sparse graphical memory for robust planning
S Emmons, A Jain, M Laskin, T Kurutach, P Abbeel, D Pathak
Advances in Neural Information Processing Systems 33, 5251-5262, 2020
262020
URLB: Unsupervised reinforcement learning benchmark
M Laskin, D Yarats, H Liu, K Lee, A Zhan, K Lu, C Cang, L Pinto, P Abbeel
arXiv preprint arXiv:2110.15191, 2021
172021
Parallel training of deep networks with local updates
M Laskin, L Metz, S Nabarro, M Saroufim, B Noune, C Luschi, ...
arXiv preprint arXiv:2012.03837, 2020
82020
Population of the giant pairing vibration
M Laskin, RF Casten, AO Macchiavelli, RM Clark, D Bucurescu
Physical Review C 93 (3), 034321, 2016
82016
Reinforcement learning with latent flow
W Shang, X Wang, A Srinivas, A Rajeswaran, Y Gao, P Abbeel, M Laskin
Advances in Neural Information Processing Systems 34, 22171-22183, 2021
72021
Behavioral priors and dynamics models: Improving performance and domain transfer in offline rl
C Cang, A Rajeswaran, P Abbeel, M Laskin
arXiv preprint arXiv:2106.09119, 2021
72021
Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning
D Yarats, D Brandfonbrener, H Liu, M Laskin, P Abbeel, A Lazaric, L Pinto
arXiv preprint arXiv:2201.13425, 2022
62022
Skill preferences: Learning to extract and execute robotic skills from human feedback
X Wang, K Lee, K Hakhamaneshi, P Abbeel, M Laskin
Conference on Robot Learning, 1259-1268, 2022
62022
CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery
M Laskin, H Liu, XB Peng, D Yarats, A Rajeswaran, P Abbeel
arXiv preprint arXiv:2202.00161, 2022
42022
Hierarchical few-shot imitation with skill transition models
K Hakhamaneshi, R Zhao, A Zhan, P Abbeel, M Laskin
arXiv preprint arXiv:2107.08981, 2021
42021
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