Sanjeev Arora

Publication Activity (10 Years)

Years Active: 1990-2024
Publications (10 Years): 123

Top Venues

CoRR

ICML

NeurIPS

ICLR

Publications

Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, Sanjeev Arora, Danqi Chen
LESS: Selecting Influential Data for Targeted Instruction Tuning. CoRR (2024)
Alexis Chevalier, Jiayi Geng, Alexander Wettig, Howard Chen, Sebastian Mizera, Toni Annala, Max Jameson Aragon, Arturo Rodríguez Fanlo, Simon Frieder, Simon Machado, Akshara Prabhakar, Ellie Thieu, Jiachen T. Wang, Zirui Wang, Xindi Wu, Mengzhou Xia, Wenhan Jia, Jiatong Yu, Jun-Jie Zhu, Zhiyong Jason Ren, Sanjeev Arora, Danqi Chen
Language Models as Science Tutors. CoRR (2024)
Sadhika Malladi, Tianyu Gao, Eshaan Nichani, Alex Damian, Jason D. Lee, Danqi Chen, Sanjeev Arora
Fine-Tuning Language Models with Just Forward Passes. NeurIPS (2023)
Xinran Gu, Kaifeng Lyu, Longbo Huang, Sanjeev Arora
Why (and When) does Local SGD Generalize Better than SGD? CoRR (2023)
Nikunj Saunshi, Arushi Gupta, Mark Braverman, Sanjeev Arora
Understanding Influence Functions and Datamodels via Harmonic Analysis. ICLR (2023)
Haoyu Zhao, Abhishek Panigrahi, Rong Ge, Sanjeev Arora
Do Transformers Parse while Predicting the Masked Word? EMNLP (2023)
Abhishek Panigrahi, Nikunj Saunshi, Haoyu Zhao, Sanjeev Arora
Task-Specific Skill Localization in Fine-tuned Language Models. CoRR (2023)
Xinran Gu, Kaifeng Lyu, Sanjeev Arora, Jingzhao Zhang, Longbo Huang
A Quadratic Synchronization Rule for Distributed Deep Learning. CoRR (2023)
Sadhika Malladi, Tianyu Gao, Eshaan Nichani, Alex Damian, Jason D. Lee, Danqi Chen, Sanjeev Arora
Fine-Tuning Language Models with Just Forward Passes. CoRR (2023)
Vedant Shah, Frederik Träuble, Ashish Malik, Hugo Larochelle, Michael Mozer, Sanjeev Arora, Yoshua Bengio, Anirudh Goyal
Unlearning via Sparse Representations. CoRR (2023)
Dingli Yu, Simran Kaur, Arushi Gupta, Jonah Brown-Cohen, Anirudh Goyal, Sanjeev Arora
Skill-Mix: a Flexible and Expandable Family of Evaluations for AI models. CoRR (2023)
Abhishek Panigrahi, Nikunj Saunshi, Haoyu Zhao, Sanjeev Arora
Task-Specific Skill Localization in Fine-tuned Language Models. ICML (2023)
Sadhika Malladi, Alexander Wettig, Dingli Yu, Danqi Chen, Sanjeev Arora
A Kernel-Based View of Language Model Fine-Tuning. ICML (2023)
Abhishek Panigrahi, Sadhika Malladi, Mengzhou Xia, Sanjeev Arora
Trainable Transformer in Transformer. CoRR (2023)
Haoyu Zhao, Abhishek Panigrahi, Rong Ge, Sanjeev Arora
Do Transformers Parse while Predicting the Masked Word? CoRR (2023)
Sanjeev Arora, Anirudh Goyal
A Theory for Emergence of Complex Skills in Language Models. CoRR (2023)
Xinran Gu, Kaifeng Lyu, Longbo Huang, Sanjeev Arora
Why (and When) does Local SGD Generalize Better than SGD? ICLR (2023)
Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy
Understanding Contrastive Learning Requires Incorporating Inductive Biases. CoRR (2022)
Sadhika Malladi, Kaifeng Lyu, Abhishek Panigrahi, Sanjeev Arora
On the SDEs and Scaling Rules for Adaptive Gradient Algorithms. NeurIPS (2022)
Yi Zhang, Arushi Gupta, Nikunj Saunshi, Sanjeev Arora
On Predicting Generalization using GANs. ICLR (2022)
Kaifeng Lyu, Zhiyuan Li, Sanjeev Arora
Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction. CoRR (2022)
Arushi Gupta, Nikunj Saunshi, Dingli Yu, Kaifeng Lyu, Sanjeev Arora
New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound. CoRR (2022)
Zhou Lu, Wenhan Xia, Sanjeev Arora, Elad Hazan
Adaptive Gradient Methods with Local Guarantees. CoRR (2022)
Arushi Gupta, Nikunj Saunshi, Dingli Yu, Kaifeng Lyu, Sanjeev Arora
New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound. NeurIPS (2022)
Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy
Understanding Contrastive Learning Requires Incorporating Inductive Biases. ICML (2022)
Sanjeev Arora, Zhiyuan Li, Abhishek Panigrahi
Understanding Gradient Descent on Edge of Stability in Deep Learning. CoRR (2022)
Zhiyuan Li, Tianhao Wang, Sanjeev Arora
What Happens after SGD Reaches Zero Loss? --A Mathematical Framework. ICLR (2022)
Sadhika Malladi, Kaifeng Lyu, Abhishek Panigrahi, Sanjeev Arora
On the SDEs and Scaling Rules for Adaptive Gradient Algorithms. CoRR (2022)
Kaifeng Lyu, Zhiyuan Li, Sanjeev Arora
Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction. NeurIPS (2022)
Nikunj Saunshi, Arushi Gupta, Mark Braverman, Sanjeev Arora
Understanding Influence Functions and Datamodels via Harmonic Analysis. CoRR (2022)
Sadhika Malladi, Alexander Wettig, Dingli Yu, Danqi Chen, Sanjeev Arora
A Kernel-Based View of Language Model Fine-Tuning. CoRR (2022)
Sanjeev Arora, Zhiyuan Li, Abhishek Panigrahi
Understanding Gradient Descent on the Edge of Stability in Deep Learning. ICML (2022)
Zhiyuan Li, Tianhao Wang, Jason D. Lee, Sanjeev Arora
Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent. CoRR (2022)
Zhiyuan Li, Tianhao Wang, Jason D. Lee, Sanjeev Arora
Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent. NeurIPS (2022)
Kaifeng Lyu, Zhiyuan Li, Runzhe Wang, Sanjeev Arora
Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias. NeurIPS (2021)
Sanjeev Arora, Yi Zhang
Rip van Winkle's Razor: A Simple Estimate of Overfit to Test Data. CoRR (2021)
Zhiyuan Li, Tianhao Wang, Sanjeev Arora
What Happens after SGD Reaches Zero Loss? -A Mathematical Framework. CoRR (2021)
Zhiyuan Li, Yi Zhang, Sanjeev Arora
Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets? ICLR (2021)
Yangsibo Huang, Samyak Gupta, Zhao Song, Kai Li, Sanjeev Arora
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning. NeurIPS (2021)
Yangsibo Huang, Samyak Gupta, Zhao Song, Kai Li, Sanjeev Arora
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning. CoRR (2021)
Nikunj Saunshi, Sadhika Malladi, Sanjeev Arora
A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks. ICLR (2021)
Zhiyuan Li, Sadhika Malladi, Sanjeev Arora
On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs). CoRR (2021)
Yi Zhang, Arushi Gupta, Nikunj Saunshi, Sanjeev Arora
On Predicting Generalization using GANs. CoRR (2021)
Zhiyuan Li, Sadhika Malladi, Sanjeev Arora
On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs). NeurIPS (2021)
Sanjeev Arora
Technical perspective: Why don't today's deep nets overfit to their training data? Commun. ACM 64 (3) (2021)
Sanjeev Arora
Opening the Black Box of Deep Learning: Some Lessons and Take-aways. SIGMETRICS (Abstracts) (2021)
Kaifeng Lyu, Zhiyuan Li, Runzhe Wang, Sanjeev Arora
Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias. CoRR (2021)
Sanjeev Arora, Simon S. Du, Sham M. Kakade, Yuping Luo, Nikunj Saunshi
Provable Representation Learning for Imitation Learning via Bi-level Optimization. ICML (2020)
Zhiyuan Li, Yi Zhang, Sanjeev Arora
Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets? CoRR (2020)
Zhiyuan Li, Sanjeev Arora
An Exponential Learning Rate Schedule for Deep Learning. ICLR (2020)
Zhiyuan Li, Kaifeng Lyu, Sanjeev Arora
Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate. NeurIPS (2020)
Zhiyuan Li, Kaifeng Lyu, Sanjeev Arora
Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate. CoRR (2020)
Yangsibo Huang, Zhao Song, Kai Li, Sanjeev Arora
InstaHide: Instance-hiding Schemes for Private Distributed Learning. CoRR (2020)
Yangsibo Huang, Yushan Su, Sachin Ravi, Zhao Song, Sanjeev Arora, Kai Li
Privacy-preserving Learning via Deep Net Pruning. CoRR (2020)
Nikunj Saunshi, Sadhika Malladi, Sanjeev Arora
A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks. CoRR (2020)
Sanjeev Arora, Simon S. Du, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang, Dingli Yu
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks. ICLR (2020)
Nikunj Saunshi, Yi Zhang, Mikhail Khodak, Sanjeev Arora
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning. ICML (2020)
Yi Zhang, Orestis Plevrakis, Simon S. Du, Xingguo Li, Zhao Song, Sanjeev Arora
Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality. NeurIPS (2020)
Yi Zhang, Orestis Plevrakis, Simon S. Du, Xingguo Li, Zhao Song, Sanjeev Arora
Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality. CoRR (2020)
Yangsibo Huang, Zhao Song, Danqi Chen, Kai Li, Sanjeev Arora
TextHide: Tackling Data Privacy in Language Understanding Tasks. CoRR (2020)
Yangsibo Huang, Zhao Song, Kai Li, Sanjeev Arora
InstaHide: Instance-hiding Schemes for Private Distributed Learning. ICML (2020)
Sanjeev Arora, Simon S. Du, Sham M. Kakade, Yuping Luo, Nikunj Saunshi
Provable Representation Learning for Imitation Learning via Bi-level Optimization. CoRR (2020)
Sanjeev Arora
The Quest for Mathematical Understanding of Deep Learning (Invited Talk). FSTTCS (2020)
Yangsibo Huang, Zhao Song, Danqi Chen, Kai Li, Sanjeev Arora
TextHide: Tackling Data Privacy for Language Understanding Tasks. EMNLP (Findings) (2020)
Nikunj Saunshi, Yi Zhang, Mikhail Khodak, Sanjeev Arora
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning. CoRR (2020)
Arushi Gupta, Sanjeev Arora
A Simple Saliency Method That Passes the Sanity Checks. CoRR (2019)
Sanjeev Arora, Nadav Cohen, Wei Hu, Yuping Luo
Implicit Regularization in Deep Matrix Factorization. CoRR (2019)
Sanjeev Arora, Hrishikesh Khandeparkar, Mikhail Khodak, Orestis Plevrakis, Nikunj Saunshi
A Theoretical Analysis of Contrastive Unsupervised Representation Learning. CoRR (2019)
Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang
On Exact Computation with an Infinitely Wide Neural Net. CoRR (2019)
Sanjeev Arora, Zhiyuan Li, Kaifeng Lyu
Theoretical Analysis of Auto Rate-Tuning by Batch Normalization. ICLR (Poster) (2019)
Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruosong Wang
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks. ICML (2019)
Nikunj Saunshi, Orestis Plevrakis, Sanjeev Arora, Mikhail Khodak, Hrishikesh Khandeparkar
A Theoretical Analysis of Contrastive Unsupervised Representation Learning. ICML (2019)
Zhiyuan Li, Ruosong Wang, Dingli Yu, Simon S. Du, Wei Hu, Ruslan Salakhutdinov, Sanjeev Arora
Enhanced Convolutional Neural Tangent Kernels. CoRR (2019)
Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruosong Wang
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks. CoRR (2019)
Sanjeev Arora, Simon S. Du, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang, Dingli Yu
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks. CoRR (2019)
Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang
On Exact Computation with an Infinitely Wide Neural Net. NeurIPS (2019)
Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li, Wei Hu, Sanjeev Arora, Rong Ge
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets. CoRR (2019)
Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li, Wei Hu, Rong Ge, Sanjeev Arora
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets. NeurIPS (2019)
Sanjeev Arora, Nadav Cohen, Noah Golowich, Wei Hu
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks. ICLR (Poster) (2019)
Zhiyuan Li, Sanjeev Arora
An Exponential Learning Rate Schedule for Deep Learning. CoRR (2019)
Sanjeev Arora, Nadav Cohen, Wei Hu, Yuping Luo
Implicit Regularization in Deep Matrix Factorization. NeurIPS (2019)
Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang
Stronger Generalization Bounds for Deep Nets via a Compression Approach. ICML (2018)
Sanjeev Arora, Wei Hu, Pravesh K. Kothari
An Analysis of the t-SNE Algorithm for Data Visualization. COLT (2018)
Kiran Vodrahalli, Po-Hsuan Chen, Yingyu Liang, Christopher Baldassano, Janice Chen, Esther Yong, Christopher J. Honey, Uri Hasson, Peter J. Ramadge, Kenneth A. Norman, Sanjeev Arora
Mapping between fMRI responses to movies and their natural language annotations. NeuroImage 180 (Part) (2018)
Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, Andrej Risteski
Linear Algebraic Structure of Word Senses, with Applications to Polysemy. Trans. Assoc. Comput. Linguistics 6 (2018)
Mikhail Khodak, Nikunj Saunshi, Yingyu Liang, Tengyu Ma, Brandon Stewart, Sanjeev Arora
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors. ACL (1) (2018)
Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang
Stronger generalization bounds for deep nets via a compression approach. CoRR (2018)
Sanjeev Arora, Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli
A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-n-Grams, and LSTMs. ICLR (Poster) (2018)
Sanjeev Arora, Nadav Cohen, Elad Hazan
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization. ICML (2018)
Sanjeev Arora, Wei Hu, Pravesh K. Kothari
An Analysis of the t-SNE Algorithm for Data Visualization. CoRR (2018)
Sanjeev Arora, Andrej Risteski, Yi Zhang
Do GANs learn the distribution? Some Theory and Empirics. ICLR (Poster) (2018)
Sanjeev Arora, Rong Ge, Yoni Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu
Learning topic models - provably and efficiently. Commun. ACM 61 (4) (2018)
Mikhail Khodak, Nikunj Saunshi, Yingyu Liang, Tengyu Ma, Brandon Stewart, Sanjeev Arora
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors. CoRR (2018)
Sanjeev Arora, Elad Hazan, Holden Lee, Karan Singh, Cyril Zhang, Yi Zhang
Towards Provable Control for Unknown Linear Dynamical Systems. ICLR (Workshop) (2018)
Sanjeev Arora, Nadav Cohen, Noah Golowich, Wei Hu
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks. CoRR (2018)
Sanjeev Arora, Zhiyuan Li, Kaifeng Lyu
Theoretical Analysis of Auto Rate-Tuning by Batch Normalization. CoRR (2018)
Sanjeev Arora, Nadav Cohen, Elad Hazan
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization. CoRR (2018)
Sanjeev Arora, Yingyu Liang, Tengyu Ma
A Simple but Tough-to-Beat Baseline for Sentence Embeddings. ICLR (Poster) (2017)
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang
Generalization and Equilibrium in Generative Adversarial Nets (GANs). ICML (2017)
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang
Generalization and Equilibrium in Generative Adversarial Nets (GANs). CoRR (2017)

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Publication Activity (10 Years)

Top Topics

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Sanjeev Arora