Publications
[*] indicates alphabetical ordering of author names.
Papers
Teacher's pet: understanding and mitigating biases in distillation
Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar
Preprint 2021
[Arxiv]
Eigen Analysis of Self-Attention and its Reconstruction from Partial Computation [*]
Srinadh Bhojanapalli, Ayan Chakrabarti, Himanshu Jain, Sanjiv Kumar, Michal Lukasik, Andreas Veit
Preprint 2021
[Arxiv]
Demystifying the Better Performance of Position Encoding Variants for Transformer
Pu-Chin Chen*, Henry Tsai*, Srinadh Bhojanapalli*, Hyung Won Chung, Yin-Wen Chang, Chun-Sung Ferng
EMNLP 2021
[Arxiv]
Understanding Robustness of Transformers for Image Classification [*]
Srinadh Bhojanapalli, Ayan Chakrabarti, Daniel Glasner, Daliang Li, Thomas Unterthiner, Andreas Veit
ICCV 2021
[Arxiv]
On the Reproducibility of Neural Network Predictions
Srinadh Bhojanapalli, Kimberly Wilber, Andreas Veit, Ankit Singh Rawat, Seungyeon Kim, Aditya Menon, Sanjiv Kumar
Preprint 2021
[Arxiv]
Modifying Memories in Transformer Models
Chen Zhu, Ankit Singh Rawat, Manzil Zaheer, Srinadh Bhojanapalli, Daliang Li, Felix Yu, Sanjiv Kumar
Preprint 2020
[Arxiv]
Coping with Label Shift via Distributionally Robust Optimisation
Jingzhao Zhang, Aditya Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra
ICLR 2021
[Openreview], [Arxiv]
An efficient nonconvex reformulation of stagewise convex optimization problems
Rudy Bunel, Oliver Hinder, Srinadh Bhojanapalli, Krishnamurthy (Dj)Dvijotham
Neurips 2020
[Arxiv]
Semantic label smoothing for sequence to sequence problems
Michal Lukasik, Himanshu Jain, Aditya Menon, Seungyeon Kim, Srinadh Bhojanapalli, Felix Yu and Sanjiv Kumar
EMNLP 2020
[Arxiv]
O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers
Chulhee Yun, Yin-Wen Chang, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar
Neurips 2020
[Arxiv].
Does label smoothing mitigate label noise?
Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar
ICML 2020
[Arxiv].
Low-Rank Bottleneck in Multi-head Attention Models
Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar
ICML 2020
[Arxiv].
Are Transformers universal approximators of sequence-to-sequence functions?
Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank Reddi, Sanjiv Kumar
ICLR 2020
[Openreview].
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
Yang You, Jing Li, Sashank Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, Cho-Jui Hsieh
ICLR 2020
[Openreview], [Arxiv].
The role of over-parametrization in generalization of neural networks
Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro
ICLR 2019
[Openreview], [Arxiv].
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form [*]
Srinadh Bhojanapalli, Nicolas Boumal, Prateek Jain, Praneeth Netrapalli
COLT 2018
[Arxiv].
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks
Behnam Neyshabur, Srinadh Bhojanapalli, Nathan Srebro
ICLR 2018
[Arxiv].
Exploring Generalization in Deep Learning
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro
NIPS 2017
[Arxiv].
Implicit Regularization in Matrix Factorization
Suriya Gunasekar, Blake Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro
NIPS 2017
Stabilizing GAN Training with Multiple Random Projections
Behnam Neyshabur, Srinadh Bhojanapalli, Ayan Chakrabarti
preprint 2017, [Arxiv], [Project website].
[Arxiv], [slides].
Single Pass PCA of Matrix Products
Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi, Alex Dimakis
NIPS 2016
[Arxiv], [SPARK code].
Global Optimality of Local Search for Low Rank Matrix Recovery
Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro
NIPS 2016
[Arxiv].
Provable Burer-Monteiro factorization for a class of norm-constrained matrix problems
Dohyung Park, Anastasios Kyrillidis, Srinadh Bhojanapalli, Constantine Caramanis, Sujay Sanghavi
preprint 2016, [Arxiv].
Dropping Convexity for Faster Semi-definite Optimization
Srinadh Bhojanapalli, Anastasios Kyrillidis, Sujay Sanghavi
COLT 2016
[Arxiv], [COLT], [slides].
A New Sampling Technique for Tensors
Srinadh Bhojanapalli, Sujay Sanghavi
preprint 2015, [Arxiv], [slides].
Tighter Low-rank Approximation via Sampling the Leveraged Element
Srinadh Bhojanapalli, Prateek Jain, Sujay Sanghavi
SODA 2015
[Arxiv], [SODA], [slides].
Completing any Low-rank Matrix, Provably
Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel Ward
JMLR 2015
[Arxiv], [JMLR].
Universal Matrix Completion
Srinadh Bhojanapalli, Prateek Jain
ICML 2014
[Arxiv], [ICML], [slides], [video].
Coherent Matrix Completion
Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel Ward
ICML 2014
[Arxiv], [ICML], [slides], [video].
PhD Thesis
Large Scale Matrix Factorization with Guarantees: Sampling and Bi-linearity [pdf]
UT Austin, 2015.
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