Universal Matrix Completion. - Srinadh Bhojanapalli; Prateek Jain 002 (ICML, 2014)

Coherent Matrix Completion. - Yudong Chen 001; Srinadh Bhojanapalli; Sujay Sanghavi; Rachel A. Ward (ICML, 2014)

Tighter Low-rank Approximation via Sampling the Leveraged Element. - Srinadh Bhojanapalli; Prateek Jain 002; Sujay Sanghavi (SODA, 2015)

A New Sampling Technique for Tensors. - Srinadh Bhojanapalli; Sujay Sanghavi (CoRR, 2015)

Completing any low-rank matrix, provably. - Yudong Chen 001; Srinadh Bhojanapalli; Sujay Sanghavi; Rachel A. Ward (J. Mach. Learn. Res., 2015)

Dropping Convexity for Faster Semi-definite Optimization. - Srinadh Bhojanapalli; Anastasios Kyrillidis; Sujay Sanghavi (COLT, 2016)

Global Optimality of Local Search for Low Rank Matrix Recovery. - Srinadh Bhojanapalli; Behnam Neyshabur; Nati Srebro (NIPS, 2016)

Single Pass PCA of Matrix Products. - Shanshan Wu; Srinadh Bhojanapalli; Sujay Sanghavi; Alexandros G. Dimakis (NIPS, 2016)

Provable non-convex projected gradient descent for a class of constrained matrix optimization problems. - Dohyung Park; Anastasios Kyrillidis; Srinadh Bhojanapalli; Constantine Caramanis; Sujay Sanghavi (CoRR, 2016)

Implicit Regularization in Matrix Factorization. - Suriya Gunasekar; Blake E. Woodworth; Srinadh Bhojanapalli; Behnam Neyshabur; Nati Srebro (NIPS, 2017)

Exploring Generalization in Deep Learning. - Behnam Neyshabur; Srinadh Bhojanapalli; David McAllester; Nati Srebro (NIPS, 2017)

Stabilizing GAN Training with Multiple Random Projections. - Behnam Neyshabur; Srinadh Bhojanapalli; Ayan Chakrabarti (CoRR, 2017)

A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks. - Behnam Neyshabur; Srinadh Bhojanapalli; David McAllester; Nathan Srebro (CoRR, 2017)

Provable quantum state tomography via non-convex methods. - Anastasios Kyrillidis; Amir Kalev; Dohyung Park; Srinadh Bhojanapalli; Constantine Caramanis; Sujay Sanghavi (CoRR, 2017)

Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form. - Srinadh Bhojanapalli; Nicolas Boumal; Prateek Jain 002; Praneeth Netrapalli (COLT, 2018)

A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks. - Behnam Neyshabur; Srinadh Bhojanapalli; Nathan Srebro (ICLR (Poster), 2018)

Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks. - Behnam Neyshabur; Zhiyuan Li 005; Srinadh Bhojanapalli; Yann LeCun; Nathan Srebro (CoRR, 2018)

The role of over-parametrization in generalization of neural networks. - Behnam Neyshabur; Zhiyuan Li 005; Srinadh Bhojanapalli; Yann LeCun; Nathan Srebro (ICLR (Poster), 2019)

Semantic Label Smoothing for Sequence to Sequence Problems. - Michal Lukasik; Himanshu Jain; Aditya Krishna Menon; Seungyeon Kim 001; Srinadh Bhojanapalli; Felix X. Yu; Sanjiv Kumar (EMNLP (1), 2020)

Large Batch Optimization for Deep Learning: Training BERT in 76 minutes. - Yang You 001; Jing Li; Sashank J. Reddi; Jonathan Hseu; Sanjiv Kumar; Srinadh Bhojanapalli; Xiaodan Song; James Demmel; Kurt Keutzer; Cho-Jui Hsieh (ICLR, 2020)

Are Transformers universal approximators of sequence-to-sequence functions? - Chulhee Yun; Srinadh Bhojanapalli; Ankit Singh Rawat; Sashank J. Reddi; Sanjiv Kumar (ICLR, 2020)

Low-Rank Bottleneck in Multi-head Attention Models. - Srinadh Bhojanapalli; Chulhee Yun; Ankit Singh Rawat; Sashank J. Reddi; Sanjiv Kumar (ICML, 2020)

Does label smoothing mitigate label noise? - Michal Lukasik; Srinadh Bhojanapalli; Aditya Krishna Menon; Sanjiv Kumar (ICML, 2020)

An efficient nonconvex reformulation of stagewise convex optimization problems. - Rudy Bunel; Oliver Hinder; Srinadh Bhojanapalli; Krishnamurthy Dvijotham (NeurIPS, 2020)

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)

Modifying Memories in Transformer Models. - Chen Zhu 001; Ankit Singh Rawat; Manzil Zaheer; Srinadh Bhojanapalli; Daliang Li; Felix X. Yu; Sanjiv Kumar (CoRR, 2020)

A Simple and Effective Positional Encoding for Transformers. - Pu-Chin Chen; Henry Tsai; Srinadh Bhojanapalli; Hyung Won Chung; Yin-Wen Chang; Chun-Sung Ferng (EMNLP (1), 2021)

Understanding Robustness of Transformers for Image Classification. - Srinadh Bhojanapalli; Ayan Chakrabarti; Daniel Glasner; Daliang Li; Thomas Unterthiner; Andreas Veit (ICCV, 2021)

Coping with Label Shift via Distributionally Robust Optimisation. - Jingzhao Zhang; Aditya Krishna Menon; Andreas Veit; Srinadh Bhojanapalli; Sanjiv Kumar; Suvrit Sra (ICLR, 2021)

On the Reproducibility of Neural Network Predictions. - Srinadh Bhojanapalli; Kimberly Wilber; Andreas Veit; Ankit Singh Rawat; Seungyeon Kim 001; Aditya Krishna Menon; Sanjiv Kumar (CoRR, 2021)

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 (CoRR, 2021)

Eigen Analysis of Self-Attention and its Reconstruction from Partial Computation. - Srinadh Bhojanapalli; Ayan Chakrabarti; Himanshu Jain; Sanjiv Kumar; Michal Lukasik; Andreas Veit (CoRR, 2021)

Leveraging redundancy in attention with Reuse Transformers. - Srinadh Bhojanapalli; Ayan Chakrabarti; Andreas Veit; Michal Lukasik; Himanshu Jain; Frederick Liu; Yin-Wen Chang; Sanjiv Kumar (CoRR, 2021)

Robust Training of Neural Networks Using Scale Invariant Architectures. - Zhiyuan Li 005; Srinadh Bhojanapalli; Manzil Zaheer; Sashank J. Reddi; Sanjiv Kumar (ICML, 2022)

On the Adversarial Robustness of Mixture of Experts. - Joan Puigcerver; Rodolphe Jenatton; Carlos Riquelme; Pranjal Awasthi; Srinadh Bhojanapalli (NeurIPS, 2022)

Large Models are Parsimonious Learners: Activation Sparsity in Trained Transformers. - Zonglin Li; Chong You; Srinadh Bhojanapalli; Daliang Li; Ankit Singh Rawat; Sashank J. Reddi; Ke Ye; Felix Chern; Felix X. Yu; Ruiqi Guo; Sanjiv Kumar (CoRR, 2022)

Teacher's pet: understanding and mitigating biases in distillation. - Michal Lukasik; Srinadh Bhojanapalli; Aditya Krishna Menon; Sanjiv Kumar (Trans. Mach. Learn. Res., 2022)

The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers. - Zonglin Li; Chong You; Srinadh Bhojanapalli; Daliang Li; Ankit Singh Rawat; Sashank J. Reddi; Ke Ye; Felix Chern; Felix X. Yu; Ruiqi Guo; Sanjiv Kumar (ICLR, 2023)

Treeformer: Dense Gradient Trees for Efficient Attention Computation. - Lovish Madaan; Srinadh Bhojanapalli; Himanshu Jain; Prateek Jain 002 (ICLR, 2023)

On student-teacher deviations in distillation: does it pay to disobey? - Vaishnavh Nagarajan; Aditya Krishna Menon; Srinadh Bhojanapalli; Hossein Mobahi; Sanjiv Kumar (NeurIPS, 2023)

Depth Dependence of μP Learning Rates in ReLU MLPs. - Samy Jelassi; Boris Hanin; Ziwei Ji; Sashank J. Reddi; Srinadh Bhojanapalli; Sanjiv Kumar (CoRR, 2023)

Efficacy of Dual-Encoders for Extreme Multi-Label Classification. - Nilesh Gupta; Devvrit Khatri; Ankit Singh Rawat; Srinadh Bhojanapalli; Prateek Jain 002; Inderjit S. Dhillon (CoRR, 2023)

Dual-Encoders for Extreme Multi-label Classification. - Nilesh Gupta; Devvrit; Ankit Singh Rawat; Srinadh Bhojanapalli; Prateek Jain 002; Inderjit S. Dhillon (ICLR, 2024)

Functional Interpolation for Relative Positions improves Long Context Transformers. - Shanda Li; Chong You; Guru Guruganesh; Joshua Ainslie; Santiago Ontañón; Manzil Zaheer; Sumit Sanghai; Yiming Yang; Sanjiv Kumar; Srinadh Bhojanapalli (ICLR, 2024)

Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure. - Hanseul Cho 002; Jaeyoung Cha; Pranjal Awasthi; Srinadh Bhojanapalli; Anupam Gupta 001; Chulhee Yun (NeurIPS, 2024)

HiRE: High Recall Approximate Top-k Estimation for Efficient LLM Inference. - Yashas Samaga; Varun Yerram; Chong You; Srinadh Bhojanapalli; Sanjiv Kumar; Prateek Jain 002; Praneeth Netrapalli (CoRR, 2024)

Efficient Language Model Architectures for Differentially Private Federated Learning. - Jae Hun Ro; Srinadh Bhojanapalli; Zheng Xu 002; Yanxiang Zhang; Ananda Theertha Suresh (CoRR, 2024)

Position Coupling: Leveraging Task Structure for Improved Length Generalization of Transformers. - Hanseul Cho 002; Jaeyoung Cha; Pranjal Awasthi; Srinadh Bhojanapalli; Anupam Gupta 001; Chulhee Yun (CoRR, 2024)

Mimetic Initialization Helps State Space Models Learn to Recall. - Asher Trockman; Hrayr Harutyunyan; J. Zico Kolter; Sanjiv Kumar; Srinadh Bhojanapalli (CoRR, 2024)

Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count. - Hanseul Cho 002; Jaeyoung Cha; Srinadh Bhojanapalli; Chulhee Yun (ICLR, 2025)

Spark Transformer: Reactivating Sparsity in FFN and Attention. - Chong You; Kan Wu; Zhipeng Jia; Lin Chen; Srinadh Bhojanapalli; Jiaxian Guo; Utku Evci; Jan Wassenberg; Praneeth Netrapalli; Jeremiah J. Willcock; Suvinay Subramanian; Felix Chern; et al. (CoRR, 2025)