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

research

∙ 02/28/2022

Understanding Contrastive Learning Requires Incorporating Inductive Biases

Contrastive learning is a popular form of self-supervised learning that ...

Nikunj Saunshi, et al. ∙

research

∙ 11/28/2021

On Predicting Generalization using GANs

Research on generalization bounds for deep networks seeks to give ways t...

Yi Zhang, et al. ∙

research

∙ 10/07/2020

A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks

Autoregressive language models pretrained on large corpora have been suc...

Nikunj Saunshi, et al. ∙

research

∙ 08/03/2020

Predicting What You Already Know Helps: Provable Self-Supervised Learning

Self-supervised representation learning solves auxiliary prediction task...

Jason D. Lee, et al. ∙

research

∙ 02/25/2020

A Sample Complexity Separation between Non-Convex and Convex Meta-Learning

One popular trend in meta-learning is to learn from many training tasks ...

Nikunj Saunshi, et al. ∙

research

∙ 02/24/2020

Provable Representation Learning for Imitation Learning via Bi-level Optimization

A common strategy in modern learning systems is to learn a representatio...

Sanjeev Arora, et al. ∙

research

∙ 02/25/2019

A Theoretical Analysis of Contrastive Unsupervised Representation Learning

Recent empirical works have successfully used unlabeled data to learn fe...

Sanjeev Arora, et al. ∙

research

∙ 05/14/2018

A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors

Motivations like domain adaptation, transfer learning, and feature learn...

Mikhail Khodak, et al. ∙

research

∙ 04/19/2017

A Large Self-Annotated Corpus for Sarcasm

We introduce the Self-Annotated Reddit Corpus (SARC), a large corpus for...

Mikhail Khodak, et al. ∙

Nikunj Saunshi

Featured Co-authors

Yi Zhang

Simon S. Du

Tengyu Ma

Jason D. Lee

Akshay Krishnamurthy

Sanjeev Arora

Sham Kakade

Yingyu Liang

Qi Lei

Surbhi Goel

Dipendra Misra

Understanding Contrastive Learning Requires Incorporating Inductive Biases

On Predicting Generalization using GANs

A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks

Predicting What You Already Know Helps: Provable Self-Supervised Learning

A Sample Complexity Separation between Non-Convex and Convex Meta-Learning

Provable Representation Learning for Imitation Learning via Bi-level Optimization

A Theoretical Analysis of Contrastive Unsupervised Representation Learning

A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors

A Large Self-Annotated Corpus for Sarcasm