michael (misha) laskinI'm a postdoc at UC Berkeley in the BAIR lab, where I work on deep unsupervised and reinforcement learning with Pieter Abbeel. I received my PhD in theoretical physics from the University of Chicago, where I worked on statisical quantum physics with Paul Wiegmann. During my PhD I received the Bloomenthal Fellowship, awarded to the best graduate student in theoretical physics. In between my PhD and postdoc I started a company - Claire AI - which made AI products for the retail sector and went through Y Combinator (W17). My co-founder and I made the Forbes 30 Under 30 list in 2017 for our work in retail and e-commerce. Before that, I received a BS from Yale University where I studied physics and literature. Email / Twitter / GitHub / Google Scholar / LinkedIn |
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AI Research |
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A Simple Unified Framework for Ensemble Learning in Deep Reinforcement LearningKimin Lee, Michael Laskin, Aravind Srinivas, Pieter Abbeel In Submission, 2020 paper / code Developed a unified framework for utilizing ensembles to greatly stabilize training of both state-based and pixel-based RL algorithms. |
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Reinforcement Learning with Augmented DataMichael Laskin*, Kimin Lee*, Adam Stooke, Lerrel Pinto, Pieter Abbeel, Aravind Srinivas In Submission, 2020 paper / code / twitter / press First extensive study of image augmentation in the RL setting. Showed that simple RL algorithms with augmented data achieve SOTA results on many common RL benchmarks. |
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CURL: Contrastive Unsupervised Representations for Reinforcement LearningMichael Laskin*, Aravind Srinivas*, Pieter Abbeel ICML, 2020 paper / code / twitter Showed for the first time that RL from pixels can be as efficient as RL from state by leveraging unsupervised contrastive representations. |
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Sparse Graphical Memory for Robust PlanningMichael Laskin*, Scott Emmons*, Ajay Jain*, Thanard Kurutach, Pieter Abbeel, Deepak Pathak In Submission, 2020 paper / video / code Introduced novel state aggregation criterium - two-way consistency (TWC) - and utilized it to make any semi-parametric graphical method more robust. Proved theoretically that TWC enables near-optimal search. |
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Discrete Representation Learning for Goal-Conditioned Visual Reinforcement LearningMichael Laskin, Thanard Kurutach, Pieter Abbeel NeurIPS Deep Reinforcement Learning Workshop, 2019 paper Discrete representations help reduce the size of the encoded observation space. We showed how utilizing a discrete bottleneck can improve goal-conditioned RL from pixels. |
Physics ResearchDuring my physics PhD I studied many-body quantum systems such as the Fractional Quantum Hall Effect, and discovered a universal topological characteristic of such states. Note: in theoretical physics alphabetical order is the usual convention for authorship. |
Emergent conformal symmetry and geometric transport properties of quantum Hall states on singular surfacesT. Can, Y.H. Chiu, M. Laskin, P. Wiegmann Physical review letters 117 (26), 266803, 2016 |
Population of the giant pairing vibrationM. Laskin, R.F. Casten, A.O. Macchiavelli, R.M. Clark, D. Bucurescu Physical Review C 93 (3), 034321, 2016 |
Collective field theory for quantum Hall statesT. Can, M. Laskin, P. Wiegmann Physical Review B 92 (23), 235141 , 2015 |
Geometry of quantum Hall states: Gravitational anomaly and transport coefficientsT. Can, M. Laskin, P. Wiegmann Annals of Physics 362, 752-794, 2015 |
Fractional quantum Hall effect in a curved space: gravitational anomaly and electromagnetic responseT. Can, M. Laskin, P. Wiegmann Physical review letters 113 (4), 046803, 2014 |
Field Theory for Fractional Quantum Hall StatesT. Can, M. Laskin, P. Wiegmann arXiv preprint arXiv:1412.8716, 2014 |
Some Aspects of the Giant-Pairing VibrationA.O. Macchiavelli, R.M. Clark, M. Laskin, R.F. Casten Bulletin of the American Physical Society, 2012 |
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