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ORCIDSashank J. Reddi
Sashank Jakkam Reddi
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- affiliation: Carnegie Mellon University, Machine Learning Department
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2020 – today
- 2024
[c47]Khashayar Gatmiry, Zhiyuan Li, Sashank J. Reddi, Stefanie Jegelka:
Simplicity Bias via Global Convergence of Sharpness Minimization. ICML 2024- [c46]Khashayar Gatmiry, Nikunj Saunshi, Sashank J. Reddi, Stefanie Jegelka, Sanjiv Kumar:
Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning? ICML 2024 - [i44]Abhishek Panigrahi, Nikunj Saunshi, Kaifeng Lyu, Sobhan Miryoosefi, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar:
Efficient Stagewise Pretraining via Progressive Subnetworks. CoRR abs/2402.05913 (2024) - [i43]Stefani Karp, Nikunj Saunshi, Sobhan Miryoosefi, Sashank J. Reddi, Sanjiv Kumar:
Landscape-Aware Growing: The Power of a Little LAG. CoRR abs/2406.02469 (2024) - [i42]Ziwei Ji, Himanshu Jain, Andreas Veit, Sashank J. Reddi, Sadeep Jayasumana, Ankit Singh Rawat, Aditya Krishna Menon, Felix Yu, Sanjiv Kumar:
Efficient Document Ranking with Learnable Late Interactions. CoRR abs/2406.17968 (2024) - [i41]Nikunj Saunshi, Stefani Karp, Shankar Krishnan, Sobhan Miryoosefi, Sashank J. Reddi, Sanjiv Kumar:
On the Inductive Bias of Stacking Towards Improving Reasoning. CoRR abs/2409.19044 (2024) - 2023
- [c45]Tian Li, Manzil Zaheer, Ken Liu, Sashank J. Reddi, Hugh Brendan McMahan, Virginia Smith:
Differentially Private Adaptive Optimization with Delayed Preconditioners. ICLR 2023 - [c44]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 - [c43]Sashank J. Reddi, Sobhan Miryoosefi, Stefani Karp, Shankar Krishnan, Satyen Kale, Seungyeon Kim, Sanjiv Kumar:
Efficient Training of Language Models using Few-Shot Learning. ICML 2023: 14553-14568 - [c42]Khashayar Gatmiry, Zhiyuan Li, Tengyu Ma, Sashank J. Reddi, Stefanie Jegelka, Ching-Yao Chuang:
What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models. NeurIPS 2023 - [i40]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) - [i39]Khashayar Gatmiry, Zhiyuan Li
, Ching-Yao Chuang, Sashank J. Reddi, Tengyu Ma, Stefanie Jegelka:
The Inductive Bias of Flatness Regularization for Deep Matrix Factorization. CoRR abs/2306.13239 (2023) - 2022
- [c41]Zhiyuan Li, Srinadh Bhojanapalli, Manzil Zaheer, Sashank J. Reddi, Sanjiv Kumar:
Robust Training of Neural Networks Using Scale Invariant Architectures. ICML 2022: 12656-12684 - [c40]Tian Li, Manzil Zaheer, Sashank J. Reddi, Virginia Smith:
Private Adaptive Optimization with Side information. ICML 2022: 13086-13105 - [c39]Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar:
In defense of dual-encoders for neural ranking. ICML 2022: 15376-15400 - [i38]Jianyu Wang, Hang Qi, Ankit Singh Rawat, Sashank J. Reddi, Sagar Waghmare, Felix X. Yu, Gauri Joshi:
FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients. CoRR abs/2201.11865 (2022) - [i37]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) - [i36]Tian Li, Manzil Zaheer, Sashank J. Reddi, Virginia Smith:
Private Adaptive Optimization with Side Information. CoRR abs/2202.05963 (2022) - [i35]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) - [i34]Han Nguyen, Hai Pham, Sashank J. Reddi, Barnabás Póczos:
On the Algorithmic Stability and Generalization of Adaptive Optimization Methods. CoRR abs/2211.03970 (2022) - [i33]Tian Li, Manzil Zaheer, Ken Ziyu Liu, Sashank J. Reddi, H. Brendan McMahan, Virginia Smith:
Differentially Private Adaptive Optimization with Delayed Preconditioners. CoRR abs/2212.00309 (2022) - 2021
- [c38]Sashank J. Reddi, Rama Kumar Pasumarthi, Aditya Krishna Menon, Ankit Singh Rawat, Felix X. Yu, Seungyeon Kim, Andreas Veit, Sanjiv Kumar:
RankDistil: Knowledge Distillation for Ranking. AISTATS 2021: 2368-2376 - [c37]Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, Hugh Brendan McMahan:
Adaptive Federated Optimization. ICLR 2021 - [c36]Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Seungyeon Kim, Sanjiv Kumar:
A statistical perspective on distillation. ICML 2021: 7632-7642 - [c35]Ankit Singh Rawat, Aditya Krishna Menon, Wittawat Jitkrittum, Sadeep Jayasumana, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar:
Disentangling Sampling and Labeling Bias for Learning in Large-output Spaces. ICML 2021: 8890-8901 - [c34]Honglin Yuan, Manzil Zaheer, Sashank J. Reddi:
Federated Composite Optimization. ICML 2021: 12253-12266 - [c33]Erik Lindgren, Sashank J. Reddi, Ruiqi Guo, Sanjiv Kumar:
Efficient Training of Retrieval Models using Negative Cache. NeurIPS 2021: 4134-4146 - [c32]Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh:
Breaking the centralized barrier for cross-device federated learning. NeurIPS 2021: 28663-28676 - [i32]Andrew Cotter, Aditya Krishna Menon, Harikrishna Narasimhan, Ankit Singh Rawat, Sashank J. Reddi, Yichen Zhou:
Distilling Double Descent. CoRR abs/2102.06849 (2021) - [i31]Ankit Singh Rawat, Aditya Krishna Menon, Wittawat Jitkrittum, Sadeep Jayasumana, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar:
Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces. CoRR abs/2105.05736 (2021) - [i30]Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horváth, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu:
A Field Guide to Federated Optimization. CoRR abs/2107.06917 (2021) - 2020
- [c31]Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Can gradient clipping mitigate label noise? ICLR 2020 - [c30]Yangjun Ruan, Yuanhao Xiong, Sashank J. Reddi, Sanjiv Kumar, Cho-Jui Hsieh:
Learning to Learn by Zeroth-Order Oracle. ICLR 2020 - [c29]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 - [c28]Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Are Transformers universal approximators of sequence-to-sequence functions? ICLR 2020 - [c27]Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Low-Rank Bottleneck in Multi-head Attention Models. ICML 2020: 864-873 - [c26]Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh:
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. ICML 2020: 5132-5143 - [c25]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 - [c24]Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra:
Why are Adaptive Methods Good for Attention Models? NeurIPS 2020 - [i29]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) - [i28]Ilqar Ramazanli, Han Nguyen, Hai Pham, Sashank J. Reddi, Barnabás Póczos:
Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets. CoRR abs/2002.08528 (2020) - [i27]Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, H. Brendan McMahan:
Adaptive Federated Optimization. CoRR abs/2003.00295 (2020) - [i26]Ankit Singh Rawat, Aditya Krishna Menon, Andreas Veit, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar:
Doubly-stochastic mining for heterogeneous retrieval. CoRR abs/2004.10915 (2020) - [i25]Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Seungyeon Kim, Sanjiv Kumar:
Why distillation helps: a statistical perspective. CoRR abs/2005.10419 (2020) - [i24]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) - [i23]Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh:
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning. CoRR abs/2008.03606 (2020) - [i22]Honglin Yuan, Manzil Zaheer, Sashank J. Reddi:
Federated Composite Optimization. CoRR abs/2011.08474 (2020)
2010 – 2019
- 2019
- [c23]Sashank J. Reddi, Satyen Kale, Felix X. Yu, Daniel Niels Holtmann-Rice, Jiecao Chen, Sanjiv Kumar:
Stochastic Negative Mining for Learning with Large Output Spaces. AISTATS 2019: 1940-1949 - [c22]Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra:
Escaping Saddle Points with Adaptive Gradient Methods. ICML 2019: 5956-5965 - [c21]Chuan Guo, Ali Mousavi, Xiang Wu, Daniel Niels Holtmann-Rice, Satyen Kale, Sashank J. Reddi, Sanjiv Kumar:
Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces. NeurIPS 2019: 4944-4954 - [i21]Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra:
Escaping Saddle Points with Adaptive Gradient Methods. CoRR abs/1901.09149 (2019) - [i20]Sashank J. Reddi, Satyen Kale, Sanjiv Kumar:
On the Convergence of Adam and Beyond. CoRR abs/1904.09237 (2019) - [i19]Venkatadheeraj Pichapati, Ananda Theertha Suresh, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar:
AdaCliP: Adaptive Clipping for Private SGD. CoRR abs/1908.07643 (2019) - [i18]Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh:
SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning. CoRR abs/1910.06378 (2019) - [i17]Yangjun Ruan, Yuanhao Xiong, Sashank J. Reddi, Sanjiv Kumar, Cho-Jui Hsieh:
Learning to Learn by Zeroth-Order Oracle. CoRR abs/1910.09464 (2019) - [i16]Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra:
Why ADAM Beats SGD for Attention Models. CoRR abs/1912.03194 (2019) - [i15]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
- [c20]Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis R. Bach, Ruslan Salakhutdinov, Alexander J. Smola:
A Generic Approach for Escaping Saddle points. AISTATS 2018: 1233-1242 - [c19]Sashank J. Reddi, Satyen Kale, Sanjiv Kumar:
On the Convergence of Adam and Beyond. ICLR 2018 - [c18]Manzil Zaheer, Sashank J. Reddi, Devendra Singh Sachan, Satyen Kale, Sanjiv Kumar:
Adaptive Methods for Nonconvex Optimization. NeurIPS 2018: 9815-9825 - [i14]Sashank J. Reddi, Satyen Kale, Felix X. Yu, Daniel N. Holtmann-Rice, Jiecao Chen, Sanjiv Kumar:
Stochastic Negative Mining for Learning with Large Output Spaces. CoRR abs/1810.07076 (2018) - 2017
- [b1]Sashank J. Reddi:
New Optimization Methods for Modern Machine Learning. Carnegie Mellon University, USA, 2017 - [i13]Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis R. Bach, Ruslan Salakhutdinov, Alexander J. Smola:
A Generic Approach for Escaping Saddle points. CoRR abs/1709.01434 (2017) - 2016
- [c17]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Stochastic Frank-Wolfe methods for nonconvex optimization. Allerton 2016: 1244-1251 - [c16]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Fast incremental method for smooth nonconvex optimization. CDC 2016: 1971-1977 - [c15]Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Stochastic Variance Reduction for Nonconvex Optimization. ICML 2016: 314-323 - [c14]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization. NIPS 2016: 1145-1153 - [c13]Kumar Avinava Dubey, Sashank J. Reddi, Sinead A. Williamson, Barnabás Póczos, Alexander J. Smola, Eric P. Xing:
Variance Reduction in Stochastic Gradient Langevin Dynamics. NIPS 2016: 1154-1162 - [c12]Hongyi Zhang, Sashank J. Reddi, Suvrit Sra:
Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds. NIPS 2016: 4592-4600 - [i12]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Fast Incremental Method for Nonconvex Optimization. CoRR abs/1603.06159 (2016) - [i11]Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Stochastic Variance Reduction for Nonconvex Optimization. CoRR abs/1603.06160 (2016) - [i10]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Fast Stochastic Methods for Nonsmooth Nonconvex Optimization. CoRR abs/1605.06900 (2016) - [i9]Hongyi Zhang, Sashank J. Reddi, Suvrit Sra:
Fast stochastic optimization on Riemannian manifolds. CoRR abs/1605.07147 (2016) - [i8]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Stochastic Frank-Wolfe Methods for Nonconvex Optimization. CoRR abs/1607.08254 (2016) - [i7]Sashank J. Reddi, Jakub Konecný, Peter Richtárik, Barnabás Póczos, Alexander J. Smola:
AIDE: Fast and Communication Efficient Distributed Optimization. CoRR abs/1608.06879 (2016) - 2015
- [c11]Sashank Jakkam Reddi, Barnabás Póczos, Alexander J. Smola:
Doubly Robust Covariate Shift Correction. AAAI 2015: 2949-2955 - [c10]Aaditya Ramdas, Sashank Jakkam Reddi, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
On the Decreasing Power of Kernel and Distance Based Nonparametric Hypothesis Tests in High Dimensions. AAAI 2015: 3571-3577 - [c9]Sashank J. Reddi, Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives. AISTATS 2015 - [c8]Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants. NIPS 2015: 2647-2655 - [c7]Sashank J. Reddi, Barnabás Póczos, Alexander J. Smola:
Communication Efficient Coresets for Empirical Loss Minimization. UAI 2015: 752-761 - [c6]Sashank J. Reddi, Ahmed Hefny, Carlton Downey, Avinava Dubey, Suvrit Sra:
Large-scale randomized-coordinate descent methods with non-separable linear constraints. UAI 2015: 762-771 - [i6]Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants. CoRR abs/1506.06840 (2015) - [i5]Aaditya Ramdas, Sashank J. Reddi, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing. CoRR abs/1508.00655 (2015) - 2014
- [c5]Sashank J. Reddi, Barnabás Póczos:
k-NN Regression on Functional Data with Incomplete Observations. UAI 2014: 692-701 - [i4]Sashank J. Reddi, Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
Kernel MMD, the Median Heuristic and Distance Correlation in High Dimensions. CoRR abs/1406.2083 (2014) - [i3]Aaditya Ramdas, Sashank J. Reddi, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives. CoRR abs/1411.6314 (2014) - 2013
- [c4]Sashank J. Reddi, Barnabás Póczos:
Scale Invariant Conditional Dependence Measures. ICML (3) 2013: 1355-1363 - 2012
- [c3]Sashank Jakkam Reddi, Emma Brunskill:
Incentive Decision Processes. UAI 2012: 418-427 - [c2]Ariel D. Procaccia, Sashank Jakkam Reddi, Nisarg Shah:
A Maximum Likelihood Approach For Selecting Sets of Alternatives. UAI 2012: 695-704 - [i2]Sashank Jakkam Reddi, Emma Brunskill:
Incentive Decision Processes. CoRR abs/1210.4877 (2012) - [i1]Ariel D. Procaccia, Sashank Jakkam Reddi, Nisarg Shah:
A Maximum Likelihood Approach For Selecting Sets of Alternatives. CoRR abs/1210.4882 (2012) - 2010
- [c1]Sashank Jakkam Reddi, Sunita Sarawagi, Sundar Vishwanathan:
MAP estimation in Binary MRFs via Bipartite Multi-cuts. NIPS 2010: 955-963
Coauthor Index
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