MAP estimation in Binary MRFs via Bipartite Multi-cuts. - Sashank Jakkam Reddi; Sunita Sarawagi; Sundar Vishwanathan (NIPS, 2010)

A Maximum Likelihood Approach For Selecting Sets of Alternatives. - Ariel D. Procaccia; Sashank Jakkam Reddi; Nisarg Shah 001 (UAI, 2012)

Incentive Decision Processes. - Sashank Jakkam Reddi; Emma Brunskill (UAI, 2012)

Scale Invariant Conditional Dependence Measures. - Sashank J. Reddi; Barnabás Póczos (ICML (3), 2013)

k-NN Regression on Functional Data with Incomplete Observations. - Sashank J. Reddi; Barnabás Póczos (UAI, 2014)

On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives. - Aaditya Ramdas; Sashank J. Reddi; Barnabás Póczos; Aarti Singh; Larry A. Wasserman (CoRR, 2014)

Kernel MMD, the Median Heuristic and Distance Correlation in High Dimensions. - Sashank J. Reddi; Aaditya Ramdas; Barnabás Póczos; Aarti Singh; Larry A. Wasserman (CoRR, 2014)

On the Decreasing Power of Kernel and Distance Based Nonparametric Hypothesis Tests in High Dimensions. - Aaditya Ramdas; Sashank Jakkam Reddi; Barnabás Póczos; Aarti Singh; Larry A. Wasserman (AAAI, 2015)

Doubly Robust Covariate Shift Correction. - Sashank Jakkam Reddi; Barnabás Póczos; Alexander J. Smola (AAAI, 2015)

On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives. - Sashank J. Reddi; Aaditya Ramdas; Barnabás Póczos; Aarti Singh; Larry A. Wasserman (AISTATS, 2015)

On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants. - Sashank J. Reddi; Ahmed Hefny; Suvrit Sra; Barnabás Póczos; Alexander J. Smola (NIPS, 2015)

Large-scale randomized-coordinate descent methods with non-separable linear constraints. - Sashank J. Reddi; Ahmed Hefny; Carlton Downey; Avinava Dubey; Suvrit Sra (UAI, 2015)

Communication Efficient Coresets for Empirical Loss Minimization. - Sashank J. Reddi; Barnabás Póczos; Alexander J. Smola (UAI, 2015)

Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing. - Aaditya Ramdas; Sashank J. Reddi; Barnabás Póczos; Aarti Singh; Larry A. Wasserman (CoRR, 2015)

Stochastic Frank-Wolfe methods for nonconvex optimization. - Sashank J. Reddi; Suvrit Sra; Barnabás Póczos; Alexander J. Smola (Allerton, 2016)

Fast incremental method for smooth nonconvex optimization. - Sashank J. Reddi; Suvrit Sra; Barnabás Póczos; Alexander J. Smola (CDC, 2016)

Stochastic Variance Reduction for Nonconvex Optimization. - Sashank J. Reddi; Ahmed Hefny; Suvrit Sra; Barnabás Póczos; Alexander J. Smola (ICML, 2016)

Variance Reduction in Stochastic Gradient Langevin Dynamics. - Kumar Avinava Dubey; Sashank J. Reddi; Sinead A. Williamson; Barnabás Póczos; Alexander J. Smola; Eric P. Xing (NIPS, 2016)

Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization. - Sashank J. Reddi; Suvrit Sra; Barnabás Póczos; Alexander J. Smola (NIPS, 2016)

Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds. - Hongyi Zhang; Sashank J. Reddi; Suvrit Sra (NIPS, 2016)

AIDE: Fast and Communication Efficient Distributed Optimization. - Sashank J. Reddi; Jakub Konecný; Peter Richtárik; Barnabás Póczos; Alexander J. Smola (CoRR, 2016)

Fast Incremental Method for Nonconvex Optimization. - Sashank J. Reddi; Suvrit Sra; Barnabás Póczos; Alexander J. Smola (CoRR, 2016)

Fast Stochastic Methods for Nonsmooth Nonconvex Optimization. - Sashank J. Reddi; Suvrit Sra; Barnabás Póczos; Alexander J. Smola (CoRR, 2016)

Fast stochastic optimization on Riemannian manifolds. - Hongyi Zhang; Sashank J. Reddi; Suvrit Sra (CoRR, 2016)

New Optimization Methods for Modern Machine Learning. - Sashank J. Reddi (2017)

A Generic Approach for Escaping Saddle points. - Sashank J. Reddi; Manzil Zaheer; Suvrit Sra; Barnabás Póczos; Francis R. Bach; Ruslan Salakhutdinov; Alexander J. Smola (AISTATS, 2018)

On the Convergence of Adam and Beyond. - Sashank J. Reddi; Satyen Kale; Sanjiv Kumar (ICLR, 2018)

Adaptive Methods for Nonconvex Optimization. - Manzil Zaheer; Sashank J. Reddi; Devendra Singh Sachan; Satyen Kale; Sanjiv Kumar (NeurIPS, 2018)

Stochastic Negative Mining for Learning with Large Output Spaces. - Sashank J. Reddi; Satyen Kale; Felix X. Yu; Daniel Niels Holtmann-Rice; Jiecao Chen; Sanjiv Kumar (AISTATS, 2019)

Escaping Saddle Points with Adaptive Gradient Methods. - Matthew Staib; Sashank J. Reddi; Satyen Kale; Sanjiv Kumar; Suvrit Sra (ICML, 2019)

Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces. - Chuan Guo 001; Ali Mousavi 003; Xiang Wu; Daniel Niels Holtmann-Rice; Satyen Kale; Sashank J. Reddi; Sanjiv Kumar (NeurIPS, 2019)

AdaCliP: Adaptive Clipping for Private SGD. - Venkatadheeraj Pichapati; Ananda Theertha Suresh; Felix X. Yu; Sashank J. Reddi; Sanjiv Kumar (CoRR, 2019)

SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning. - Sai Praneeth Karimireddy; Satyen Kale; Mehryar Mohri; Sashank J. Reddi; Sebastian U. Stich; Ananda Theertha Suresh (CoRR, 2019)

Why ADAM Beats SGD for Attention Models. - Jingzhao Zhang; Sai Praneeth Karimireddy; Andreas Veit; Seungyeon Kim 001; Sashank J. Reddi; Sanjiv Kumar; Suvrit Sra (CoRR, 2019)

Can gradient clipping mitigate label noise? - Aditya Krishna Menon; Ankit Singh Rawat; Sashank J. Reddi; Sanjiv Kumar (ICLR, 2020)

Learning to Learn by Zeroth-Order Oracle. - Yangjun Ruan; Yuanhao Xiong; Sashank J. Reddi; Sanjiv Kumar; Cho-Jui Hsieh (ICLR, 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)

SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. - Sai Praneeth Karimireddy; Satyen Kale; Mehryar Mohri; Sashank J. Reddi; Sebastian U. Stich; Ananda Theertha Suresh (ICML, 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)

Why are Adaptive Methods Good for Attention Models? - Jingzhao Zhang; Sai Praneeth Karimireddy; Andreas Veit; Seungyeon Kim 001; Sashank J. Reddi; Sanjiv Kumar; Suvrit Sra (NeurIPS, 2020)

Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets. - Ilqar Ramazanli; Han Nguyen; Hai Pham; Sashank J. Reddi; Barnabás Póczos (CoRR, 2020)

Doubly-stochastic mining for heterogeneous retrieval. - Ankit Singh Rawat; Aditya Krishna Menon; Andreas Veit; Felix X. Yu; Sashank J. Reddi; Sanjiv Kumar (CoRR, 2020)

Why distillation helps: a statistical perspective. - Aditya Krishna Menon; Ankit Singh Rawat; Sashank J. Reddi; Seungyeon Kim 001; Sanjiv Kumar (CoRR, 2020)

Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning. - Sai Praneeth Karimireddy; Martin Jaggi; Satyen Kale; Mehryar Mohri; Sashank J. Reddi; Sebastian U. Stich; Ananda Theertha Suresh (CoRR, 2020)

RankDistil: Knowledge Distillation for Ranking. - Sashank J. Reddi; Rama Kumar Pasumarthi; Aditya Krishna Menon; Ankit Singh Rawat; Felix X. Yu; Seungyeon Kim 001; Andreas Veit; Sanjiv Kumar (AISTATS, 2021)

Adaptive Federated Optimization. - Sashank J. Reddi; Zachary Charles; Manzil Zaheer; Zachary Garrett; Keith Rush; Jakub Konecný; Sanjiv Kumar; Hugh Brendan McMahan (ICLR, 2021)

A statistical perspective on distillation. - Aditya Krishna Menon; Ankit Singh Rawat; Sashank J. Reddi; Seungyeon Kim 001; Sanjiv Kumar (ICML, 2021)

Disentangling Sampling and Labeling Bias for Learning in Large-output Spaces. - Ankit Singh Rawat; Aditya Krishna Menon; Wittawat Jitkrittum; Sadeep Jayasumana; Felix X. Yu; Sashank J. Reddi; Sanjiv Kumar (ICML, 2021)

Federated Composite Optimization. - Honglin Yuan 002; Manzil Zaheer; Sashank J. Reddi (ICML, 2021)

Breaking the centralized barrier for cross-device federated learning. - Sai Praneeth Karimireddy; Martin Jaggi; Satyen Kale; Mehryar Mohri; Sashank J. Reddi; Sebastian U. Stich; Ananda Theertha Suresh (NeurIPS, 2021)

Efficient Training of Retrieval Models using Negative Cache. - Erik Lindgren; Sashank J. Reddi; Ruiqi Guo; Sanjiv Kumar (NeurIPS, 2021)

Distilling Double Descent. - Andrew Cotter; Aditya Krishna Menon; Harikrishna Narasimhan; Ankit Singh Rawat; Sashank J. Reddi; Yichen Zhou (CoRR, 2021)

A Field Guide to Federated Optimization. - Jianyu Wang; Zachary Charles; Zheng Xu 002; Gauri Joshi; H. Brendan McMahan; Blaise Agüera y Arcas; Maruan Al-Shedivat; Galen Andrew; Salman Avestimehr; Katharine Daly; Deepesh Data; Suhas N. Diggavi; et al. (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)

Private Adaptive Optimization with Side information. - Tian Li 005; Manzil Zaheer; Sashank J. Reddi; Virginia Smith (ICML, 2022)

In defense of dual-encoders for neural ranking. - Aditya Krishna Menon; Sadeep Jayasumana; Ankit Singh Rawat; Seungyeon Kim 001; Sashank J. Reddi; Sanjiv Kumar (ICML, 2022)

FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients. - Jianyu Wang; Hang Qi; Ankit Singh Rawat; Sashank J. Reddi; Sagar Waghmare; Felix X. Yu; Gauri Joshi (CoRR, 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)

On the Algorithmic Stability and Generalization of Adaptive Optimization Methods. - Han Nguyen; Hai Pham; Sashank J. Reddi; Barnabás Póczos (CoRR, 2022)

Differentially Private Adaptive Optimization with Delayed Preconditioners. - Tian Li 005; Manzil Zaheer; Ken Liu; Sashank J. Reddi; Hugh Brendan McMahan; Virginia Smith (ICLR, 2023)

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)

Efficient Training of Language Models using Few-Shot Learning. - Sashank J. Reddi; Sobhan Miryoosefi; Stefani Karp; Shankar Krishnan; Satyen Kale; Seungyeon Kim 001; Sanjiv Kumar (ICML, 2023)

What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models. - Khashayar Gatmiry; Zhiyuan Li 005; Tengyu Ma 001; Sashank J. Reddi; Stefanie Jegelka; Ching-Yao Chuang (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)

The Inductive Bias of Flatness Regularization for Deep Matrix Factorization. - Khashayar Gatmiry; Zhiyuan Li 005; Ching-Yao Chuang; Sashank J. Reddi; Tengyu Ma 001; Stefanie Jegelka (CoRR, 2023)

Simplicity Bias via Global Convergence of Sharpness Minimization. - Khashayar Gatmiry; Zhiyuan Li 005; Sashank J. Reddi; Stefanie Jegelka (ICML, 2024)

Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning? - Khashayar Gatmiry; Nikunj Saunshi; Sashank J. Reddi; Stefanie Jegelka; Sanjiv Kumar (ICML, 2024)

On the Inductive Bias of Stacking Towards Improving Reasoning. - Nikunj Saunshi; Stefani Karp; Shankar Krishnan; Sobhan Miryoosefi; Sashank Jakkam Reddi; Sanjiv Kumar (NeurIPS, 2024)

Landscape-Aware Growing: The Power of a Little LAG. - Stefani Karp; Nikunj Saunshi; Sobhan Miryoosefi; Sashank J. Reddi; Sanjiv Kumar (CoRR, 2024)

Efficient Document Ranking with Learnable Late Interactions. - Ziwei Ji; Himanshu Jain; Andreas Veit; Sashank J. Reddi; Sadeep Jayasumana; Ankit Singh Rawat; Aditya Krishna Menon; Felix X. Yu; Sanjiv Kumar (CoRR, 2024)

A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs. - Ankit Singh Rawat; Veeranjaneyulu Sadhanala; Afshin Rostamizadeh; Ayan Chakrabarti; Wittawat Jitkrittum; Vladimir Feinberg; Seungyeon Kim 001; Hrayr Harutyunyan; Nikunj Saunshi; Zachary Nado; Rakesh Shivanna; Sashank J. Reddi; et al. (CoRR, 2024)

On the Role of Depth and Looping for In-Context Learning with Task Diversity. - Khashayar Gatmiry; Nikunj Saunshi; Sashank J. Reddi; Stefanie Jegelka; Sanjiv Kumar (CoRR, 2024)

Efficient stagewise pretraining via progressive subnetworks. - Abhishek Panigrahi; Nikunj Saunshi; Kaifeng Lyu; Sobhan Miryoosefi; Sashank J. Reddi; Satyen Kale; Sanjiv Kumar (ICLR, 2025)

Reasoning with Latent Thoughts: On the Power of Looped Transformers. - Nikunj Saunshi; Nishanth Dikkala; Zhiyuan Li 005; Sanjiv Kumar; Sashank J. Reddi (ICLR, 2025)

Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation. - Michal Lukasik; Lin Chen; Harikrishna Narasimhan; Aditya Krishna Menon; Wittawat Jitkrittum; Felix X. Yu; Sashank J. Reddi; Gang Fu; MohammadHossein Bateni; Sanjiv Kumar (CoRR, 2025)