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ORCIDSrinadh Bhojanapalli
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2020 – today
- 2024
[c29]Nilesh Gupta, Devvrit, Ankit Singh Rawat, Srinadh Bhojanapalli, Prateek Jain, Inderjit S. Dhillon:
Dual-Encoders for Extreme Multi-label Classification. ICLR 2024- [c28]Shanda Li, Chong You, Guru Guruganesh, Joshua Ainslie, Santiago Ontañón, Manzil Zaheer, Sumit Sanghai, Yiming Yang, Sanjiv Kumar, Srinadh Bhojanapalli:
Functional Interpolation for Relative Positions improves Long Context Transformers. ICLR 2024 - [i42]Yashas Samaga, Varun Yerram, Chong You, Srinadh Bhojanapalli, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli:
HiRE: High Recall Approximate Top-k Estimation for Efficient LLM Inference. CoRR abs/2402.09360 (2024) - [i41]Jae Hun Ro, Srinadh Bhojanapalli, Zheng Xu, Yanxiang Zhang, Ananda Theertha Suresh:
Efficient Language Model Architectures for Differentially Private Federated Learning. CoRR abs/2403.08100 (2024) - [i40]Hanseul Cho, Jaeyoung Cha, Pranjal Awasthi, Srinadh Bhojanapalli, Anupam Gupta, Chulhee Yun:
Position Coupling: Leveraging Task Structure for Improved Length Generalization of Transformers. CoRR abs/2405.20671 (2024) - [i39]Asher Trockman, Hrayr Harutyunyan, J. Zico Kolter, Sanjiv Kumar, Srinadh Bhojanapalli:
Mimetic Initialization Helps State Space Models Learn to Recall. CoRR abs/2410.11135 (2024) - [i38]Hanseul Cho, Jaeyoung Cha, Srinadh Bhojanapalli, Chulhee Yun:
Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count. CoRR abs/2410.15787 (2024) - 2023
- [c27]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:
The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers. ICLR 2023 - [c26]Lovish Madaan, Srinadh Bhojanapalli, Himanshu Jain, Prateek Jain:
Treeformer: Dense Gradient Trees for Efficient Attention Computation. ICLR 2023 - [c25]Vaishnavh Nagarajan, Aditya Krishna Menon, Srinadh Bhojanapalli, Hossein Mobahi, Sanjiv Kumar:
On student-teacher deviations in distillation: does it pay to disobey? NeurIPS 2023 - [i37]Vaishnavh Nagarajan, Aditya Krishna Menon, Srinadh Bhojanapalli, Hossein Mobahi, Sanjiv Kumar:
On student-teacher deviations in distillation: does it pay to disobey? CoRR abs/2301.12923 (2023) - [i36]Samy Jelassi, Boris Hanin, Ziwei Ji, Sashank J. Reddi, Srinadh Bhojanapalli, Sanjiv Kumar:
Depth Dependence of μP Learning Rates in ReLU MLPs. CoRR abs/2305.07810 (2023) - [i35]Shanda Li, Chong You, Guru Guruganesh, Joshua Ainslie, Santiago Ontañón, Manzil Zaheer, Sumit Sanghai, Yiming Yang, Sanjiv Kumar, Srinadh Bhojanapalli:
Functional Interpolation for Relative Positions Improves Long Context Transformers. CoRR abs/2310.04418 (2023) - [i34]Nilesh Gupta, Devvrit Khatri, Ankit Singh Rawat, Srinadh Bhojanapalli, Prateek Jain, Inderjit S. Dhillon:
Efficacy of Dual-Encoders for Extreme Multi-Label Classification. CoRR abs/2310.10636 (2023) - 2022
- [j2]Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar:
Teacher's pet: understanding and mitigating biases in distillation. Trans. Mach. Learn. Res. 2022 (2022) - [c24]Zhiyuan Li
, Srinadh Bhojanapalli, Manzil Zaheer, Sashank J. Reddi, Sanjiv Kumar:
Robust Training of Neural Networks Using Scale Invariant Architectures. ICML 2022: 12656-12684 - [c23]Joan Puigcerver, Rodolphe Jenatton, Carlos Riquelme, Pranjal Awasthi, Srinadh Bhojanapalli:
On the Adversarial Robustness of Mixture of Experts. NeurIPS 2022 - [i33]Zhiyuan Li, Srinadh Bhojanapalli, Manzil Zaheer, Sashank J. Reddi, Sanjiv Kumar:
Robust Training of Neural Networks using Scale Invariant Architectures. CoRR abs/2202.00980 (2022) - [i32]Lovish Madaan, Srinadh Bhojanapalli, Himanshu Jain, Prateek Jain:
Treeformer: Dense Gradient Trees for Efficient Attention Computation. CoRR abs/2208.09015 (2022) - [i31]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:
Large Models are Parsimonious Learners: Activation Sparsity in Trained Transformers. CoRR abs/2210.06313 (2022) - [i30]Joan Puigcerver, Rodolphe Jenatton, Carlos Riquelme, Pranjal Awasthi, Srinadh Bhojanapalli:
On the Adversarial Robustness of Mixture of Experts. CoRR abs/2210.10253 (2022) - 2021
- [c22]Pu-Chin Chen, Henry Tsai, Srinadh Bhojanapalli, Hyung Won Chung, Yin-Wen Chang, Chun-Sung Ferng:
A Simple and Effective Positional Encoding for Transformers. EMNLP (1) 2021: 2974-2988 - [c21]Srinadh Bhojanapalli, Ayan Chakrabarti, Daniel Glasner, Daliang Li, Thomas Unterthiner, Andreas Veit:
Understanding Robustness of Transformers for Image Classification. ICCV 2021: 10211-10221 - [c20]Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra:
Coping with Label Shift via Distributionally Robust Optimisation. ICLR 2021 - [i29]Srinadh Bhojanapalli, Kimberly Wilber, Andreas Veit, Ankit Singh Rawat, Seungyeon Kim, Aditya Krishna Menon, Sanjiv Kumar:
On the Reproducibility of Neural Network Predictions. CoRR abs/2102.03349 (2021) - [i28]Srinadh Bhojanapalli, Ayan Chakrabarti, Daniel Glasner, Daliang Li, Thomas Unterthiner, Andreas Veit:
Understanding Robustness of Transformers for Image Classification. CoRR abs/2103.14586 (2021) - [i27]Pu-Chin Chen, Henry Tsai, Srinadh Bhojanapalli, Hyung Won Chung, Yin-Wen Chang, Chun-Sung Ferng:
Demystifying the Better Performance of Position Encoding Variants for Transformer. CoRR abs/2104.08698 (2021) - [i26]Srinadh Bhojanapalli, Ayan Chakrabarti, Himanshu Jain, Sanjiv Kumar, Michal Lukasik, Andreas Veit:
Eigen Analysis of Self-Attention and its Reconstruction from Partial Computation. CoRR abs/2106.08823 (2021) - [i25]Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar:
Teacher's pet: understanding and mitigating biases in distillation. CoRR abs/2106.10494 (2021) - [i24]Srinadh Bhojanapalli, Ayan Chakrabarti, Andreas Veit, Michal Lukasik, Himanshu Jain, Frederick Liu, Yin-Wen Chang, Sanjiv Kumar:
Leveraging redundancy in attention with Reuse Transformers. CoRR abs/2110.06821 (2021) - 2020
- [c19]Michal Lukasik, Himanshu Jain, Aditya Krishna Menon, Seungyeon Kim, Srinadh Bhojanapalli, Felix X. Yu, Sanjiv Kumar:
Semantic Label Smoothing for Sequence to Sequence Problems. EMNLP (1) 2020: 4992-4998 - [c18]Yang You, Jing Li, Sashank J. Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, Cho-Jui Hsieh:
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes. ICLR 2020 - [c17]Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Are Transformers universal approximators of sequence-to-sequence functions? ICLR 2020 - [c16]Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Low-Rank Bottleneck in Multi-head Attention Models. ICML 2020: 864-873 - [c15]Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar:
Does label smoothing mitigate label noise? ICML 2020: 6448-6458 - [c14]Rudy Bunel, Oliver Hinder, Srinadh Bhojanapalli, Krishnamurthy Dvijotham:
An efficient nonconvex reformulation of stagewise convex optimization problems. NeurIPS 2020 - [c13]Chulhee Yun, Yin-Wen Chang, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers. NeurIPS 2020 - [i23]Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Low-Rank Bottleneck in Multi-head Attention Models. CoRR abs/2002.07028 (2020) - [i22]Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar:
Does label smoothing mitigate label noise? CoRR abs/2003.02819 (2020) - [i21]Chulhee Yun, Yin-Wen Chang, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
$O(n)$ Connections are Expressive Enough: Universal Approximability of Sparse Transformers. CoRR abs/2006.04862 (2020) - [i20]Michal Lukasik, Himanshu Jain, Aditya Krishna Menon, Seungyeon Kim, Srinadh Bhojanapalli, Felix X. Yu, Sanjiv Kumar:
Semantic Label Smoothing for Sequence to Sequence Problems. CoRR abs/2010.07447 (2020) - [i19]Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra:
Coping with Label Shift via Distributionally Robust Optimisation. CoRR abs/2010.12230 (2020) - [i18]Rudy Bunel, Oliver Hinder, Srinadh Bhojanapalli, Krishnamurthy Dvijotham:
An efficient nonconvex reformulation of stagewise convex optimization problems. CoRR abs/2010.14322 (2020) - [i17]Chen Zhu, Ankit Singh Rawat, Manzil Zaheer, Srinadh Bhojanapalli, Daliang Li, Felix X. Yu, Sanjiv Kumar:
Modifying Memories in Transformer Models. CoRR abs/2012.00363 (2020)
2010 – 2019
- 2019
- [c12]Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro:
The role of over-parametrization in generalization of neural networks. ICLR (Poster) 2019 - [i16]Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Are Transformers universal approximators of sequence-to-sequence functions? CoRR abs/1912.10077 (2019) - 2018
- [c11]Srinadh Bhojanapalli, Nicolas Boumal, Prateek Jain, Praneeth Netrapalli:
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form. COLT 2018: 3243-3270 - [c10]Behnam Neyshabur, Srinadh Bhojanapalli, Nathan Srebro:
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks. ICLR (Poster) 2018 - [c9]Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro:
Implicit Regularization in Matrix Factorization. ITA 2018: 1-10 - [i15]Srinadh Bhojanapalli, Nicolas Boumal, Prateek Jain, Praneeth Netrapalli:
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form. CoRR abs/1803.00186 (2018) - [i14]Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro:
Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks. CoRR abs/1805.12076 (2018) - 2017
- [c8]Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nati Srebro:
Exploring Generalization in Deep Learning. NIPS 2017: 5947-5956 - [c7]Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro:
Implicit Regularization in Matrix Factorization. NIPS 2017: 6151-6159 - [i13]Behnam Neyshabur, Srinadh Bhojanapalli, Ayan Chakrabarti:
Stabilizing GAN Training with Multiple Random Projections. CoRR abs/1705.07831 (2017) - [i12]Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro:
Implicit Regularization in Matrix Factorization. CoRR abs/1705.09280 (2017) - [i11]Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro:
Exploring Generalization in Deep Learning. CoRR abs/1706.08947 (2017) - [i10]Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro:
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks. CoRR abs/1707.09564 (2017) - [i9]Anastasios Kyrillidis, Amir Kalev, Dohyung Park, Srinadh Bhojanapalli, Constantine Caramanis, Sujay Sanghavi:
Provable quantum state tomography via non-convex methods. CoRR abs/1711.02524 (2017) - 2016
- [c6]Srinadh Bhojanapalli, Anastasios Kyrillidis, Sujay Sanghavi:
Dropping Convexity for Faster Semi-definite Optimization. COLT 2016: 530-582 - [c5]Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi, Alexandros G. Dimakis:
Single Pass PCA of Matrix Products. NIPS 2016: 2577-2585 - [c4]Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro:
Global Optimality of Local Search for Low Rank Matrix Recovery. NIPS 2016: 3873-3881 - [i8]Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro:
Global Optimality of Local Search for Low Rank Matrix Recovery. CoRR abs/1605.07221 (2016) - [i7]Dohyung Park, Anastasios Kyrillidis, Srinadh Bhojanapalli, Constantine Caramanis, Sujay Sanghavi:
Provable non-convex projected gradient descent for a class of constrained matrix optimization problems. CoRR abs/1606.01316 (2016) - [i6]Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi, Alexandros G. Dimakis:
Single Pass PCA of Matrix Products. CoRR abs/1610.06656 (2016) - 2015
- [j1]Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel A. Ward:
Completing any low-rank matrix, provably. J. Mach. Learn. Res. 16: 2999-3034 (2015) - [c3]Srinadh Bhojanapalli, Prateek Jain, Sujay Sanghavi:
Tighter Low-rank Approximation via Sampling the Leveraged Element. SODA 2015: 902-920 - [i5]Srinadh Bhojanapalli, Sujay Sanghavi:
A New Sampling Technique for Tensors. CoRR abs/1502.05023 (2015) - [i4]Srinadh Bhojanapalli, Anastasios Kyrillidis, Sujay Sanghavi:
Dropping Convexity for Faster Semi-definite Optimization. CoRR abs/1509.03917 (2015) - 2014
- [c2]Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel A. Ward:
Coherent Matrix Completion. ICML 2014: 674-682 - [c1]Srinadh Bhojanapalli, Prateek Jain:
Universal Matrix Completion. ICML 2014: 1881-1889 - [i3]Srinadh Bhojanapalli, Prateek Jain:
Universal Matrix Completion. CoRR abs/1402.2324 (2014) - [i2]Srinadh Bhojanapalli, Prateek Jain, Sujay Sanghavi:
Tighter Low-rank Approximation via Sampling the Leveraged Element. CoRR abs/1410.3886 (2014) - 2013
- [i1]Srinadh Bhojanapalli, Yudong Chen, Sujay Sanghavi, Rachel A. Ward:
Coherent Matrix Completion. CoRR abs/1306.2979 (2013)
Coauthor Index
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