Research
I'm interested in developing robust, multi-task algorithms for reinforcement learning and unsupervised learning.
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Generalized Hindsight for Reinforcement Learning
Alexander C. Li,
Lerrel Pinto,
Pieter Abbeel
NeurIPS 2020
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bibtex
We present Generalized Hindsight: an approximate inverse reinforcement learning technique for relabeling behaviors with the right tasks. Given a behavior generated under one task, Generalized Hindsight finds a different task that the behavior is better suited for.
Relabeling a trajectory with this different task and training with an off-policy RL algorithm improves sample-efficiency and asymptotic performance on a suite of multi-task navigation and manipulation tasks.
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Sub-policy Adaptation for Hierarchical Reinforcement Learning
Alexander C. Li*,
Carlos Florensa*,
Ignasi Clavera,
Pieter Abbeel
International Conference on Learning Representations (ICLR), 2020
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project page /
bibtex
We develop a new hierarchical RL algorithm that can efficiently adapt pre-trained skills on related tasks, and directly learn effective emergent skills by simultaneously training the entire hierarchy.
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Autoregressive Models: What Are They Good For?
Murtaza Dalal*,
Alexander C. Li*,
Rohan Taori*
Workshop on Information Theory and Machine Learning, NeurIPS, 2019
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bibtex
We attempt to use powerful autoregressive models for image-to-image translation and outlier detection, with poor results. We analyze the failure modes and find that (a) differentiating through these models creates an ill-conditioned optimization problem and (b) their density estimates are incredibly sensitive to distributional shifts.
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Sunspot Rotation and the M-Class Flare in Solar Active Region NOAA 11158
Alexander C. Li &
Yang Liu
Solar Physics, 2015
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We develop a novel algorithm for estimating sunspot rotation speed that is more robust to feature evolution than traditional methods based on time-slices. We use this technique to gain more accurate estimates of magnetic energy buildup in rotating sunspots.
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Head TA, CS 294-158: Deep Unsupervised Learning, Spring 2020
Head Content TA, EECS 126: Stochastic Processes, Fall 2019
TA, CS 188: Artificial Intelligence, Spring 2019
TA, CS 188: Artificial Intelligence, Fall 2018
Academic Intern, CS 189: Machine Learning, Spring 2018
Reader, CS 70: Discrete Mathematics & Probability, Fall 2017
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Mentor, Google Code Corps 2017
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