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A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks
Autoregressive language models pretrained on large corpora have been suc...
10/07/2020 ∙ by Nikunj Saunshi, et al. ∙
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Predicting What You Already Know Helps: Provable Self-Supervised Learning
Self-supervised representation learning solves auxiliary prediction task...
08/03/2020 ∙ by Jason D. Lee, et al. ∙
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A Sample Complexity Separation between Non-Convex and Convex Meta-Learning
One popular trend in meta-learning is to learn from many training tasks ...
02/25/2020 ∙ by Nikunj Saunshi, et al. ∙
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Provable Representation Learning for Imitation Learning via Bi-level Optimization
A common strategy in modern learning systems is to learn a representatio...
02/24/2020 ∙ by Sanjeev Arora, et al. ∙
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A Theoretical Analysis of Contrastive Unsupervised Representation Learning
Recent empirical works have successfully used unlabeled data to learn fe...
02/25/2019 ∙ by Sanjeev Arora, et al. ∙
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A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors
Motivations like domain adaptation, transfer learning, and feature learn...
05/14/2018 ∙ by Mikhail Khodak, et al. ∙
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A Large Self-Annotated Corpus for Sarcasm
We introduce the Self-Annotated Reddit Corpus (SARC), a large corpus for...
04/19/2017 ∙ by Mikhail Khodak, et al. ∙
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Nikunj Saunshi
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Princeton University
Second Year Masters student in Computer Science at Princeton University, Interest in Theoretical Machine Learning, Associate Engineer at Samsung Electronics, South Korea in the R&D center from 2014-2016
