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ORCIDPrateek Jain 0002
Person information
- affiliation: Google
- affiliation (former): Microsoft Research India
- affiliation (former): UT Austin, USA
- affiliation (former): IIT Kanpur, India
Other persons with the same name
- Prateek Jain — disambiguation page
- Prateek Jain 0001 — Nuance Research, Sunnyvale, CA, USA (and 2 more)
- Prateek Jain 0003 — Bell Labs, USA (and 1 more)
- Prateek Jain 0004 — Dhirubhai Ambani Institute of Information and Communication Technology
- Prateek Jain 0005 — University of California, Irvine, USA
- Prateek Jain 0006
![[0000-0002-8191-9785]](https://dblp.org/img/orcid-mark.12x12.png "0000-0002-8191-9785")
— Malaviya National Institute of Technology, Jaipur, India (and 1 more) - Prateek Jain 0007 — Jaypee Institute of Information Technology University, Noida, India
- Prateek Jain 0008 — Indian Institute of Technology Indore, Madhya Pradesh, India
- Prateek Jain 0009 — Worcester Polytechnic Institute, USA
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2020 – today
- 2023
[i93]Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain:
Optimal Algorithms for Latent Bandits with Cluster Structure. CoRR abs/2301.07040 (2023)- [i92]Xiyang Liu, Prateek Jain, Weihao Kong, Sewoong Oh, Arun Sai Suggala:
Near Optimal Private and Robust Linear Regression. CoRR abs/2301.13273 (2023) - [i91]Depen Morwani, Jatin Batra, Prateek Jain, Praneeth Netrapalli:
Simplicity Bias in 1-Hidden Layer Neural Networks. CoRR abs/2302.00457 (2023) - [i90]Walid Krichene, Prateek Jain, Shuang Song, Mukund Sundararajan, Abhradeep Thakurta, Li Zhang:
Multi-Task Differential Privacy Under Distribution Skew. CoRR abs/2302.07975 (2023) - [i89]Aniket Rege, Aditya Kusupati, Sharan Ranjit S, Alan Fan, Qingqing Cao, Sham M. Kakade, Prateek Jain, Ali Farhadi:
AdANNS: A Framework for Adaptive Semantic Search. CoRR abs/2305.19435 (2023) - 2022
- [j14]Ajaykrishna Karthikeyan, Naman Jain, Nagarajan Natarajan, Prateek Jain:
Learning Accurate Decision Trees with Bandit Feedback via Quantized Gradient Descent. Trans. Mach. Learn. Res. 2022 (2022) - [c109]Anish Acharya, Abolfazl Hashemi, Prateek Jain, Sujay Sanghavi, Inderjit S. Dhillon, Ufuk Topcu:
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent. AISTATS 2022: 11145-11168 - [c108]Prateek Varshney, Abhradeep Thakurta, Prateek Jain:
(Nearly) Optimal Private Linear Regression for Sub-Gaussian Data via Adaptive Clipping. COLT 2022: 1126-1166 - [c107]Naman Agarwal, Syomantak Chaudhuri, Prateek Jain, Dheeraj Mysore Nagaraj, Praneeth Netrapalli:
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs. ICLR 2022 - [c106]S. Deepak Narayanan, Aditya Sinha, Prateek Jain, Purushottam Kar, Sundararajan Sellamanickam:
IGLU: Efficient GCN Training via Lazy Updates. ICLR 2022 - [c105]Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir:
Reproducibility in Optimization: Theoretical Framework and Limits. NeurIPS 2022 - [c104]Fnu Devvrit, Aditya Sinha, Inderjit S. Dhillon, Prateek Jain:
S3GC: Scalable Self-Supervised Graph Clustering. NeurIPS 2022 - [c103]Aditya Kusupati, Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, Kaifeng Chen, Sham M. Kakade, Prateek Jain, Ali Farhadi:
Matryoshka Representation Learning. NeurIPS 2022 - [c102]Xiyang Liu, Weihao Kong, Prateek Jain, Sewoong Oh:
DP-PCA: Statistically Optimal and Differentially Private PCA. NeurIPS 2022 - [i88]Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir:
Reproducibility in Optimization: Theoretical Framework and Limits. CoRR abs/2202.04598 (2022) - [i87]Aditya Kusupati, Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, Kaifeng Chen, Sham M. Kakade, Prateek Jain, Ali Farhadi:
Matryoshka Representations for Adaptive Deployment. CoRR abs/2205.13147 (2022) - [i86]Xiyang Liu, Weihao Kong, Prateek Jain, Sewoong Oh:
DP-PCA: Statistically Optimal and Differentially Private PCA. CoRR abs/2205.13709 (2022) - [i85]Kushal Majmundar, Sachin Goyal, Praneeth Netrapalli, Prateek Jain:
MET: Masked Encoding for Tabular Data. CoRR abs/2206.08564 (2022) - [i84]Prateek Varshney, Abhradeep Thakurta, Prateek Jain:
(Nearly) Optimal Private Linear Regression via Adaptive Clipping. CoRR abs/2207.04686 (2022) - [i83]Lovish Madaan, Srinadh Bhojanapalli, Himanshu Jain, Prateek Jain:
Treeformer: Dense Gradient Trees for Efficient Attention Computation. CoRR abs/2208.09015 (2022) - [i82]Anshul Nasery, Sravanti Addepalli, Praneeth Netrapalli, Prateek Jain:
DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization. CoRR abs/2208.09139 (2022) - [i81]Prateek Jain, Soumyabrata Pal:
Online Low Rank Matrix Completion. CoRR abs/2209.03997 (2022) - [i80]Sravanti Addepalli, Anshul Nasery, R. Venkatesh Babu, Praneeth Netrapalli, Prateek Jain:
Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks. CoRR abs/2210.01360 (2022) - [i79]Soumyabrata Pal, Prateek Varshney, Prateek Jain, Abhradeep Guha Thakurta, Gagan Madan, Gaurav Aggarwal, Pradeep Shenoy, Gaurav Srivastava:
Private and Efficient Meta-Learning with Low Rank and Sparse Decomposition. CoRR abs/2210.03505 (2022) - [i78]Naman Agarwal, Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli:
Multi-User Reinforcement Learning with Low Rank Rewards. CoRR abs/2210.05355 (2022) - 2021
- [j13]Prateek Jain, Dheeraj M. Nagaraj, Praneeth Netrapalli:
Making the Last Iterate of SGD Information Theoretically Optimal. SIAM J. Optim. 31(2): 1108-1130 (2021) - [c101]Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang:
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates. ICML 2021: 1877-1887 - [c100]Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain:
Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization. ICML 2021: 9255-9264 - [c99]Harshay Shah, Prateek Jain, Praneeth Netrapalli:
Do Input Gradients Highlight Discriminative Features? NeurIPS 2021: 2046-2059 - [c98]Suhas S. Kowshik, Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli:
Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems. NeurIPS 2021: 8518-8531 - [c97]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Statistically and Computationally Efficient Linear Meta-representation Learning. NeurIPS 2021: 18487-18500 - [c96]Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan, Raghav Somani, Jae Sung Park, Krishna Pillutla, Prateek Jain, Sham M. Kakade, Ali Farhadi:
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes. NeurIPS 2021: 23900-23913 - [c95]Prateek Jain, John Rush, Adam D. Smith, Shuang Song, Abhradeep Guha Thakurta:
Differentially Private Model Personalization. NeurIPS 2021: 29723-29735 - [c94]Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli:
Streaming Linear System Identification with Reverse Experience Replay. NeurIPS 2021: 30140-30152 - [i77]Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain:
Optimal Regret Algorithm for Pseudo-1d Bandit Convex Optimization. CoRR abs/2102.07387 (2021) - [i76]Ajaykrishna Karthikeyan, Naman Jain, Nagarajan Natarajan, Prateek Jain:
Learning Accurate Decision Trees with Bandit Feedback via Quantized Gradient Descent. CoRR abs/2102.07567 (2021) - [i75]Harshay Shah, Prateek Jain, Praneeth Netrapalli:
Do Input Gradients Highlight Discriminative Features? CoRR abs/2102.12781 (2021) - [i74]Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli:
Streaming Linear System Identification with Reverse Experience Replay. CoRR abs/2103.05896 (2021) - [i73]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Sample Efficient Linear Meta-Learning by Alternating Minimization. CoRR abs/2105.08306 (2021) - [i72]Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli:
Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems. CoRR abs/2105.11558 (2021) - [i71]Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan, Raghav Somani, Jae Sung Park, Krishna Pillutla, Prateek Jain, Sham M. Kakade, Ali Farhadi:
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes. CoRR abs/2106.01487 (2021) - [i70]Anish Acharya, Abolfazl Hashemi, Prateek Jain, Sujay Sanghavi, Inderjit S. Dhillon, Ufuk Topcu:
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent. CoRR abs/2106.08882 (2021) - [i69]Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang:
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates. CoRR abs/2107.09802 (2021) - [i68]S. Deepak Narayanan, Aditya Sinha, Prateek Jain, Purushottam Kar, Sundararajan Sellamanickam:
IGLU: Efficient GCN Training via Lazy Updates. CoRR abs/2109.13995 (2021) - [i67]Naman Agarwal, Syomantak Chaudhuri, Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli:
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs. CoRR abs/2110.08440 (2021) - [i66]Ameya Daigavane, Gagan Madan, Aditya Sinha, Abhradeep Guha Thakurta, Gaurav Aggarwal, Prateek Jain:
Node-Level Differentially Private Graph Neural Networks. CoRR abs/2111.15521 (2021) - 2020
- [j12]Anthony Man-Cho So, Prateek Jain, Wing-Kin Ma, Gesualdo Scutari:
Nonconvex Optimization for Signal Processing and Machine Learning [From the Guest Editors]. IEEE Signal Process. Mag. 37(5): 15-17 (2020) - [c93]Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain:
DROCC: Deep Robust One-Class Classification. ICML 2020: 3711-3721 - [c92]Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain, Sham M. Kakade, Ali Farhadi:
Soft Threshold Weight Reparameterization for Learnable Sparsity. ICML 2020: 5544-5555 - [c91]Dheeraj Nagaraj, Xian Wu, Guy Bresler, Prateek Jain, Praneeth Netrapalli:
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms. NeurIPS 2020 - [c90]Oindrila Saha, Aditya Kusupati, Harsha Vardhan Simhadri, Manik Varma, Prateek Jain:
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference. NeurIPS 2020 - [c89]Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli:
The Pitfalls of Simplicity Bias in Neural Networks. NeurIPS 2020 - [c88]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method. NeurIPS 2020 - [i65]Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain, Sham M. Kakade, Ali Farhadi:
Soft Threshold Weight Reparameterization for Learnable Sparsity. CoRR abs/2002.03231 (2020) - [i64]Oindrila Saha, Aditya Kusupati, Harsha Vardhan Simhadri, Manik Varma, Prateek Jain:
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference. CoRR abs/2002.11921 (2020) - [i63]Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain:
DROCC: Deep Robust One-Class Classification. CoRR abs/2002.12718 (2020) - [i62]Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli:
The Pitfalls of Simplicity Bias in Neural Networks. CoRR abs/2006.07710 (2020) - [i61]Guy Bresler, Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli, Xian Wu:
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms. CoRR abs/2006.08916 (2020) - [i60]Bhaskar Mukhoty, Govind Gopakumar, Prateek Jain, Purushottam Kar:
Globally-convergent Iteratively Reweighted Least Squares for Robust Regression Problems. CoRR abs/2006.14211 (2020) - [i59]Nagarajan Natarajan, Ajaykrishna Karthikeyan, Prateek Jain, Ivan Radicek, Sriram K. Rajamani, Sumit Gulwani, Johannes Gehrke:
Programming by Rewards. CoRR abs/2007.06835 (2020) - [i58]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method. CoRR abs/2010.01848 (2020)
2010 – 2019
- 2019
- [c87]Vivek Gupta, Rahul Wadbude, Nagarajan Natarajan, Harish Karnick, Prateek Jain, Piyush Rai:
Distributional Semantics Meets Multi-Label Learning. AAAI 2019: 3747-3754 - [c86]Bhaskar Mukhoty, Govind Gopakumar, Prateek Jain, Purushottam Kar:
Globally-convergent Iteratively Reweighted Least Squares for Robust Regression Problems. AISTATS 2019: 313-322 - [c85]Nagarajan Natarajan, Danny Simmons, Naren Datha, Prateek Jain, Sumit Gulwani:
Learning Natural Programs from a Few Examples in Real-Time. AISTATS 2019: 1714-1722 - [c84]Pengkai Zhu, Durmus Alp Emre Acar, Nan Feng, Prateek Jain, Venkatesh Saligrama:
Cost aware Inference for IoT Devices. AISTATS 2019: 2770-2779 - [c83]Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli:
Making the Last Iterate of SGD Information Theoretically Optimal. COLT 2019: 1752-1755 - [c82]Arun Sai Suggala, Kush Bhatia, Pradeep Ravikumar, Prateek Jain:
Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression. COLT 2019: 2892-2897 - [c81]Rong Ge, Prateek Jain, Sham M. Kakade, Rahul Kidambi, Dheeraj M. Nagaraj, Praneeth Netrapalli:
Open Problem: Do Good Algorithms Necessarily Query Bad Points? COLT 2019: 3190-3193 - [c80]Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli:
SGD without Replacement: Sharper Rates for General Smooth Convex Functions. ICML 2019: 4703-4711 - [c79]Kai Zhong, Zhao Song, Prateek Jain, Inderjit S. Dhillon:
Provable Non-linear Inductive Matrix Completion. NeurIPS 2019: 11435-11445 - [c78]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Efficient Algorithms for Smooth Minimax Optimization. NeurIPS 2019: 12659-12670 - [c77]Don Kurian Dennis, Durmus Alp Emre Acar, Vikram Mandikal, Vinu Sankar Sadasivan, Venkatesh Saligrama, Harsha Vardhan Simhadri, Prateek Jain:
Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices. NeurIPS 2019: 12896-12906 - [c76]Shishir G. Patil, Don Kurian Dennis, Chirag Pabbaraju, Nadeem Shaheer, Harsha Vardhan Simhadri, Vivek Seshadri, Manik Varma, Prateek Jain:
GesturePod: Enabling On-device Gesture-based Interaction for White Cane Users. UIST 2019: 403-415 - [i57]Aditya Kusupati, Manish Singh, Kush Bhatia, Ashish Kumar, Prateek Jain, Manik Varma:
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network. CoRR abs/1901.02358 (2019) - [i56]Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli:
SGD without Replacement: Sharper Rates for General Smooth Convex Functions. CoRR abs/1903.01463 (2019) - [i55]Arun Sai Suggala, Kush Bhatia, Pradeep Ravikumar, Prateek Jain:
Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression. CoRR abs/1903.08192 (2019) - [i54]Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli:
Making the Last Iterate of SGD Information Theoretically Optimal. CoRR abs/1904.12443 (2019) - [i53]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Efficient Algorithms for Smooth Minimax Optimization. CoRR abs/1907.01543 (2019) - [i52]Chirag Pabbaraju, Prateek Jain:
Learning Functions over Sets via Permutation Adversarial Networks. CoRR abs/1907.05638 (2019) - [i51]Sahil Bhatia, Saswat Padhi, Nagarajan Natarajan, Rahul Sharma, Prateek Jain:
OASIS: ILP-Guided Synthesis of Loop Invariants. CoRR abs/1911.11728 (2019) - 2018
- [j11]Saswat Padhi, Prateek Jain, Daniel Perelman, Oleksandr Polozov, Sumit Gulwani, Todd D. Millstein:
FlashProfile: a framework for synthesizing data profiles. Proc. ACM Program. Lang. 2(OOPSLA): 150:1-150:28 (2018) - [c75]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford:
Accelerating Stochastic Gradient Descent for Least Squares Regression. COLT 2018: 545-604 - [c74]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 - [c73]Ashwin Kalyan, Abhishek Mohta, Oleksandr Polozov, Dhruv Batra, Prateek Jain, Sumit Gulwani:
Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples. ICLR (Poster) 2018 - [c72]Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade:
On the insufficiency of existing momentum schemes for Stochastic Optimization. ICLR 2018 - [c71]Prateek Jain, Om Dipakbhai Thakkar, Abhradeep Thakurta:
Differentially Private Matrix Completion Revisited. ICML 2018: 2220-2229 - [c70]Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade:
On the Insufficiency of Existing Momentum Schemes for Stochastic Optimization. ITA 2018: 1-9 - [c69]Aditya Kusupati, Manish Singh, Kush Bhatia, Ashish Kumar, Prateek Jain, Manik Varma:
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network. NeurIPS 2018: 9031-9042 - [c68]Raghav Somani, Chirag Gupta, Prateek Jain, Praneeth Netrapalli:
Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds. NeurIPS 2018: 10837-10847 - [c67]Don Kurian Dennis, Chirag Pabbaraju, Harsha Vardhan Simhadri, Prateek Jain:
Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices. NeurIPS 2018: 10976-10987 - [i50]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) - [i49]Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade:
On the insufficiency of existing momentum schemes for Stochastic Optimization. CoRR abs/1803.05591 (2018) - [i48]Ashwin J. Vijayakumar, Abhishek Mohta, Oleksandr Polozov, Dhruv Batra, Prateek Jain, Sumit Gulwani:
Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples. CoRR abs/1804.01186 (2018) - [i47]Kai Zhong, Zhao Song, Prateek Jain, Inderjit S. Dhillon:
Nonlinear Inductive Matrix Completion based on One-layer Neural Networks. CoRR abs/1805.10477 (2018) - 2017
- [j10]Prateek Jain, Purushottam Kar:
Non-convex Optimization for Machine Learning. Found. Trends Mach. Learn. 10(3-4): 142-336 (2017) - [j9]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford:
Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification. J. Mach. Learn. Res. 18: 223:1-223:42 (2017) - [j8]Prateek Jain, Ambuj Tewari, Inderjit S. Dhillon:
Partial Hard Thresholding. IEEE Trans. Inf. Theory 63(5): 3029-3038 (2017) - [c66]Apoorv Aggarwal, Sandip Ghoshal, Ankith M. S. Shetty, Suhit Sinha, Ganesh Ramakrishnan, Purushottam Kar, Prateek Jain:
Scalable Optimization of Multivariate Performance Measures in Multi-instance Multi-label Learning. AAAI 2017: 1698-1704 - [c65]Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli:
Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot. AISTATS 2017: 479-488 - [c64]Sumit Gulwani, Prateek Jain:
Programming by Examples: PL Meets ML. APLAS 2017: 3-20 - [c63]Yeshwanth Cherapanamjeri, Prateek Jain, Praneeth Netrapalli:
Thresholding Based Outlier Robust PCA. COLT 2017: 593-628 - [c62]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, Aaron Sidford:
A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares). FSTTCS 2017: 2:1-2:10 - [c61]Kamalika Chaudhuri, Prateek Jain, Nagarajan Natarajan:
Active Heteroscedastic Regression. ICML 2017: 694-702 - [c60]Yeshwanth Cherapanamjeri, Kartik Gupta, Prateek Jain:
Nearly Optimal Robust Matrix Completion. ICML 2017: 797-805 - [c59]Chirag Gupta, Arun Sai Suggala, Ankit Goyal, Harsha Vardhan Simhadri, Bhargavi Paranjape, Ashish Kumar, Saurabh Goyal, Raghavendra Udupa, Manik Varma, Prateek Jain:
ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices. ICML 2017: 1331-1340 - [c58]Kai Zhong, Zhao Song, Prateek Jain, Peter L. Bartlett, Inderjit S. Dhillon:
Recovery Guarantees for One-hidden-layer Neural Networks. ICML 2017: 4140-4149 - [c57]Kai Zhong, Prateek Jain, Ashish Kapoor:
Fast second-order cone programming for safe mission planning. ICRA 2017: 79-86 - [c56]Kush Bhatia, Prateek Jain, Parameswaran Kamalaruban, Purushottam Kar:
Consistent Robust Regression. NIPS 2017: 2110-2119 - [c55]Aditi Raghunathan, Prateek Jain, Ravishankar Krishnaswamy:
Learning Mixture of Gaussians with Streaming Data. NIPS 2017: 6605-6614 - [i46]Yeshwanth Cherapanamjeri, Prateek Jain, Praneeth Netrapalli:
Thresholding based Efficient Outlier Robust PCA. CoRR abs/1702.05571 (2017) - [i45]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford:
Accelerating Stochastic Gradient Descent. CoRR abs/1704.08227 (2017) - [i44]Kai Zhong, Zhao Song, Prateek Jain, Peter L. Bartlett, Inderjit S. Dhillon:
Recovery Guarantees for One-hidden-layer Neural Networks. CoRR abs/1706.03175 (2017) - [i43]Aditi Raghunathan, Ravishankar Krishnaswamy, Prateek Jain:
Learning Mixture of Gaussians with Streaming Data. CoRR abs/1707.02391 (2017) - [i42]Saswat Padhi, Prateek Jain, Daniel Perelman, Oleksandr Polozov, Sumit Gulwani, Todd D. Millstein:
FlashProfile: Interactive Synthesis of Syntactic Profiles. CoRR abs/1709.05725 (2017) - [i41]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, Aaron Sidford:
A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares). CoRR abs/1710.09430 (2017) - [i40]Prateek Jain, Purushottam Kar:
Non-convex Optimization for Machine Learning. CoRR abs/1712.07897 (2017) - [i39]Prateek Jain, Om Thakkar, Abhradeep Thakurta:
Differentially Private Matrix Completion, Revisited. CoRR abs/1712.09765 (2017) - 2016
- [j7]Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli:
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization. SIAM J. Optim. 26(4): 2775-2799 (2016) - [c54]Anima Anandkumar, Prateek Jain, Yang Shi, U. N. Niranjan:
Tensor vs. Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations. AISTATS 2016: 268-276 - [c53]Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford:
Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm. COLT 2016: 1147-1164 - [c52]Vidyadhar Rao, Prateek Jain, C. V. Jawahar:
Diverse Yet Efficient Retrieval using Locality Sensitive Hashing. ICMR 2016: 189-196 - [c51]Prateek Jain, Nikhil Rao, Inderjit S. Dhillon:
Structured Sparse Regression via Greedy Hard Thresholding. NIPS 2016: 1516-1524 - [c50]Kai Zhong, Prateek Jain, Inderjit S. Dhillon:
Mixed Linear Regression with Multiple Components. NIPS 2016: 2190-2198 - [c49]Fan Yang, Rina Foygel Barber, Prateek Jain, John D. Lafferty:
Selective inference for group-sparse linear models. NIPS 2016: 2469-2477 - [c48]Nagarajan Natarajan, Prateek Jain:
Regret Bounds for Non-decomposable Metrics with Missing Labels. NIPS 2016: 2874-2882 - [e1]