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Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method

Oct 05, 2020
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh


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Programming by Rewards

Jul 14, 2020
Nagarajan Natarajan, Ajaykrishna Karthikeyan, Prateek Jain, Ivan Radicek, Sriram Rajamani, Sumit Gulwani, Johannes Gehrke


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Globally-convergent Iteratively Reweighted Least Squares for Robust Regression Problems

Jun 25, 2020
Bhaskar Mukhoty, Govind Gopakumar, Prateek Jain, Purushottam Kar

* Proceedings of Machine Learning Research (PMLR) 89:313-322, 2019 
* 30 pages, 5 figures, appeared as a publication in the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 2019 

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Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms

Jun 16, 2020
Guy Bresler, Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli, Xian Wu


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The Pitfalls of Simplicity Bias in Neural Networks

Jun 13, 2020
Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli


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COVID-19: Strategies for Allocation of Test Kits

Apr 03, 2020
Arpita Biswas, Shruthi Bannur, Prateek Jain, Srujana Merugu


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Soft Threshold Weight Reparameterization for Learnable Sparsity

Mar 11, 2020
Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain, Sham Kakade, Ali Farhadi

* 17 pages, 9 figures 

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DROCC: Deep Robust One-Class Classification

Feb 28, 2020
Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain


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RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference

Feb 27, 2020
Oindrila Saha, Aditya Kusupati, Harsha Vardhan Simhadri, Manik Varma, Prateek Jain

* 16 pages, 8 figures 

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Rich-Item Recommendations for Rich-Users via GCNN: Exploiting Dynamic and Static Side Information

Jan 28, 2020
Amar Budhiraja, Gaurush Hiranandani, Navya Yarrabelly, Ayush Choure, Oluwasanmi Koyejo, Prateek Jain

* The first two authors contributed equally. 14 pages, 2 figures and 5 tables 

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OASIS: ILP-Guided Synthesis of Loop Invariants

Nov 26, 2019
Sahil Bhatia, Saswat Padhi, Nagarajan Natarajan, Rahul Sharma, Prateek Jain


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Learning Functions over Sets via Permutation Adversarial Networks

Jul 12, 2019
Chirag Pabbaraju, Prateek Jain


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Efficient Algorithms for Smooth Minimax Optimization

Jul 02, 2019
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh


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Making the Last Iterate of SGD Information Theoretically Optimal

May 29, 2019
Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli

* 3 figures, Accepted for presentation at COLT 2019 

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Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression

Mar 19, 2019
Arun Sai Suggala, Kush Bhatia, Pradeep Ravikumar, Prateek Jain


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SGD without Replacement: Sharper Rates for General Smooth Convex Functions

Mar 04, 2019
Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli


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FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network

Jan 08, 2019
Aditya Kusupati, Manish Singh, Kush Bhatia, Ashish Kumar, Prateek Jain, Manik Varma

* 23 pages, 10 figures, Published at Advances in Neural Information Processing Systems (NeurIPS) 2018 

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Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples

Sep 09, 2018
Ashwin Kalyan, Abhishek Mohta, Oleksandr Polozov, Dhruv Batra, Prateek Jain, Sumit Gulwani

* Published in ICLR 2018, International Conference on Learning Representations (2018) 

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On the insufficiency of existing momentum schemes for Stochastic Optimization

Jul 31, 2018
Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade

* 28 pages, 10 figures. Updated acknowledgements. Appeared as an oral presentation at International Conference on Learning Representations (ICLR), 2018. Code implementing the ASGD method can be found at https://github.com/rahulkidambi/AccSGD 

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Accelerating Stochastic Gradient Descent For Least Squares Regression

Jul 31, 2018
Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford

* 54 pages, 3 figures, 1 table; updated acknowledgements, minor title change. Paper appeared in the proceedings of the Conference on Learning Theory (COLT), 2018 

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Parallelizing Stochastic Gradient Descent for Least Squares Regression: mini-batching, averaging, and model misspecification

Jul 31, 2018
Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford

* 39 pages. Published in the Journal of Machine Learning Research (JMLR) 

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A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares)

Jul 21, 2018
Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, Aaron Sidford

* Lemma 1 has been updated in v2 

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Differentially Private Matrix Completion Revisited

Jun 12, 2018
Prateek Jain, Om Thakkar, Abhradeep Thakurta

* Updated version. Accepted for presentation at International Conference on Machine Learning (ICML) 2018 

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Nonlinear Inductive Matrix Completion based on One-layer Neural Networks

May 26, 2018
Kai Zhong, Zhao Song, Prateek Jain, Inderjit S. Dhillon


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Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form

Mar 01, 2018
Srinadh Bhojanapalli, Nicolas Boumal, Prateek Jain, Praneeth Netrapalli

* 24 pages 

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Non-convex Optimization for Machine Learning

Dec 21, 2017
Prateek Jain, Purushottam Kar

* Foundations and Trends in Machine Learning: Vol. 10: No. 3-4, pp 142-336 (2017) 
* The official publication is available from now publishers via http://dx.doi.org/10.1561/2200000058 

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