Prateek Jain

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 148

Top Venues

CoRR

NeurIPS

ICML

NIPS

Publications

Nilesh Gupta, Devvrit, Ankit Singh Rawat, Srinadh Bhojanapalli, Prateek Jain, Inderjit S. Dhillon
Dual-Encoders for Extreme Multi-label Classification. ICLR (2024)
Jinhyuk Lee, Zhuyun Dai, Xiaoqi Ren, Blair Chen, Daniel Cer, Jeremy R. Cole, Kai Hui, Michael Boratko, Rajvi Kapadia, Wen Ding, Yi Luan, Sai Meher Karthik Duddu, Gustavo Hernández Ábrego, Weiqiang Shi, Nithi Gupta, Aditya Kusupati, Prateek Jain, Siddhartha Reddy Jonnalagadda, Ming-Wei Chang, Iftekhar Naim
Gecko: Versatile Text Embeddings Distilled from Large Language Models. CoRR (2024)
Soumyabrata Pal, Prateek Varshney, Gagan Madan, Prateek Jain, Abhradeep Thakurta, Gaurav Aggarwal, Pradeep Shenoy, Gaurav Srivastava
Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components. AISTATS (2024)
Aishwarya P. S, Pranav Ajit Nair, Yashas Samaga, Toby Boyd, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli
Tandem Transformers for Inference Efficient LLMs. CoRR (2024)
Gagan Jain, Nidhi Hegde, Aditya Kusupati, Arsha Nagrani, Shyamal Buch, Prateek Jain, Anurag Arnab, Sujoy Paul
Mixture of Nested Experts: Adaptive Processing of Visual Tokens. CoRR (2024)
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 (2024)
Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain
Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints. NeurIPS (2023)
Depen Morwani, Jatin Batra, Prateek Jain, Praneeth Netrapalli
Simplicity Bias in 1-Hidden Layer Neural Networks. CoRR (2023)
Soumyabrata Pal, Prateek Jain
Online Low Rank Matrix Completion. ICLR (2023)
Dheeraj Mysore Nagaraj, Suhas S. Kowshik, Naman Agarwal, Praneeth Netrapalli, Prateek Jain
Multi-User Reinforcement Learning with Low Rank Rewards. ICML (2023)
Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain
Optimal Algorithms for Latent Bandits with Cluster Structure. AISTATS (2023)
Walid Krichene, Prateek Jain, Shuang Song, Mukund Sundararajan, Abhradeep Thakurta, Li Zhang
Multi-Task Differential Privacy Under Distribution Skew. CoRR (2023)
Nilesh Gupta, Devvrit Khatri, Ankit S. Rawat, Srinadh Bhojanapalli, Prateek Jain, Inderjit S. Dhillon
Efficacy of Dual-Encoders for Extreme Multi-Label Classification. CoRR (2023)
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. NeurIPS (2023)
Sravanti Addepalli, Anshul Nasery, Venkatesh Babu Radhakrishnan, Praneeth Netrapalli, Prateek Jain
Feature Reconstruction From Outputs Can Mitigate Simplicity Bias in Neural Networks. ICLR (2023)
Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain
Optimal Algorithms for Latent Bandits with Cluster Structure. CoRR (2023)
Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain
Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints. CoRR (2023)
Ramnath Kumar, Anshul Mittal, Nilesh Gupta, Aditya Kusupati, Inderjit S. Dhillon, Prateek Jain
EHI: End-to-end Learning of Hierarchical Index for Efficient Dense Retrieval. CoRR (2023)
Xiyang Liu, Prateek Jain, Weihao Kong, Sewoong Oh, Arun Sai Suggala
Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency. NeurIPS (2023)
Xiyang Liu, Prateek Jain, Weihao Kong, Sewoong Oh, Arun Sai Suggala
Near Optimal Private and Robust Linear Regression. CoRR (2023)
Walid Krichene, Prateek Jain, Shuang Song, Mukund Sundararajan, Abhradeep Guha Thakurta, Li Zhang
Multi-Task Differential Privacy Under Distribution Skew. ICML (2023)
Depen Morwani, Jatin Batra, Prateek Jain, Praneeth Netrapalli
Simplicity Bias in 1-Hidden Layer Neural Networks. NeurIPS (2023)
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 (2023)
Anshul Nasery, Hardik Shah, Arun Sai Suggala, Prateek Jain
Regularized Latency Surrogates. CoRR (2023)
Lovish Madaan, Srinadh Bhojanapalli, Himanshu Jain, Prateek Jain
Treeformer: Dense Gradient Trees for Efficient Attention Computation. ICLR (2023)
Devvrit, Sneha Kudugunta, Aditya Kusupati, Tim Dettmers, Kaifeng Chen, Inderjit S. Dhillon, Yulia Tsvetkov, Hannaneh Hajishirzi, Sham M. Kakade, Ali Farhadi, Prateek Jain
MatFormer: Nested Transformer for Elastic Inference. CoRR (2023)
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)
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)
Fnu Devvrit, Aditya Sinha, Inderjit S. Dhillon, Prateek Jain
S3GC: Scalable Self-Supervised Graph Clustering. NeurIPS (2022)
Lovish Madaan, Srinadh Bhojanapalli, Himanshu Jain, Prateek Jain
Treeformer: Dense Gradient Trees for Efficient Attention Computation. CoRR (2022)
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 (2022)
Anshul Nasery, Sravanti Addepalli, Praneeth Netrapalli, Prateek Jain
DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization. CoRR (2022)
S. Deepak Narayanan, Aditya Sinha, Prateek Jain, Purushottam Kar, Sundararajan Sellamanickam
IGLU: Efficient GCN Training via Lazy Updates. ICLR (2022)
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 (2022)
Naman Agarwal, Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli
Multi-User Reinforcement Learning with Low Rank Rewards. CoRR (2022)
Prateek Varshney, Abhradeep Thakurta, Prateek Jain
(Nearly) Optimal Private Linear Regression via Adaptive Clipping. CoRR (2022)
Xiyang Liu, Weihao Kong, Prateek Jain, Sewoong Oh
DP-PCA: Statistically Optimal and Differentially Private PCA. NeurIPS (2022)
Xiyang Liu, Weihao Kong, Prateek Jain, Sewoong Oh
DP-PCA: Statistically Optimal and Differentially Private PCA. CoRR (2022)
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)
Sravanti Addepalli, Anshul Nasery, R. Venkatesh Babu, Praneeth Netrapalli, Prateek Jain
Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks. CoRR (2022)
Kushal Majmundar, Sachin Goyal, Praneeth Netrapalli, Prateek Jain
MET: Masked Encoding for Tabular Data. CoRR (2022)
Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir
Reproducibility in Optimization: Theoretical Framework and Limits. CoRR (2022)
Prateek Varshney, Abhradeep Thakurta, Prateek Jain
(Nearly) Optimal Private Linear Regression for Sub-Gaussian Data via Adaptive Clipping. COLT (2022)
Prateek Jain, Soumyabrata Pal
Online Low Rank Matrix Completion. CoRR (2022)
Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir
Reproducibility in Optimization: Theoretical Framework and Limits. NeurIPS (2022)
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)
Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain
Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization. ICML (2021)
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)
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
Statistically and Computationally Efficient Linear Meta-representation Learning. NeurIPS (2021)
Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli
Streaming Linear System Identification with Reverse Experience Replay. CoRR (2021)
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 (2021)
Prateek Jain, John Rush, Adam D. Smith, Shuang Song, Abhradeep Guha Thakurta
Differentially Private Model Personalization. NeurIPS (2021)
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 (2021)
Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain
Optimal Regret Algorithm for Pseudo-1d Bandit Convex Optimization. CoRR (2021)
Ameya Daigavane, Gagan Madan, Aditya Sinha, Abhradeep Guha Thakurta, Gaurav Aggarwal, Prateek Jain
Node-Level Differentially Private Graph Neural Networks. CoRR (2021)
S. Deepak Narayanan, Aditya Sinha, Prateek Jain, Purushottam Kar, Sundararajan Sellamanickam
IGLU: Efficient GCN Training via Lazy Updates. CoRR (2021)
Suhas S. Kowshik, Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli
Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems. NeurIPS (2021)
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)
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 (2021)
Harshay Shah, Prateek Jain, Praneeth Netrapalli
Do Input Gradients Highlight Discriminative Features? CoRR (2021)
Prateek Jain, Dheeraj M. Nagaraj, Praneeth Netrapalli
Making the Last Iterate of SGD Information Theoretically Optimal. SIAM J. Optim. 31 (2) (2021)
Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli
Streaming Linear System Identification with Reverse Experience Replay. NeurIPS (2021)
Harshay Shah, Prateek Jain, Praneeth Netrapalli
Do Input Gradients Highlight Discriminative Features? NeurIPS (2021)
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 (2021)
Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli
Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems. CoRR (2021)
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
Sample Efficient Linear Meta-Learning by Alternating Minimization. CoRR (2021)
Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli
The Pitfalls of Simplicity Bias in Neural Networks. NeurIPS (2020)
Oindrila Saha, Aditya Kusupati, Harsha Vardhan Simhadri, Manik Varma, Prateek Jain
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference. CoRR (2020)
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method. CoRR (2020)
Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain
DROCC: Deep Robust One-Class Classification. ICML (2020)
Nagarajan Natarajan, Ajaykrishna Karthikeyan, Prateek Jain, Ivan Radicek, Sriram K. Rajamani, Sumit Gulwani, Johannes Gehrke
Programming by Rewards. CoRR (2020)
Dheeraj Nagaraj, Xian Wu, Guy Bresler, Prateek Jain, Praneeth Netrapalli
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms. NeurIPS (2020)
Bhaskar Mukhoty, Govind Gopakumar, Prateek Jain, Purushottam Kar
Globally-convergent Iteratively Reweighted Least Squares for Robust Regression Problems. CoRR (2020)
Oindrila Saha, Aditya Kusupati, Harsha Vardhan Simhadri, Manik Varma, Prateek Jain
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference. NeurIPS (2020)
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method. NeurIPS (2020)
Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain, Sham M. Kakade, Ali Farhadi
Soft Threshold Weight Reparameterization for Learnable Sparsity. CoRR (2020)
Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain, Sham M. Kakade, Ali Farhadi
Soft Threshold Weight Reparameterization for Learnable Sparsity. ICML (2020)
Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli
The Pitfalls of Simplicity Bias in Neural Networks. CoRR (2020)
Guy Bresler, Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli, Xian Wu
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms. CoRR (2020)
Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain
DROCC: Deep Robust One-Class Classification. CoRR (2020)
Kai Zhong, Zhao Song, Prateek Jain, Inderjit S. Dhillon
Provable Non-linear Inductive Matrix Completion. NeurIPS (2019)
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)
Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli
Making the Last Iterate of SGD Information Theoretically Optimal. CoRR (2019)
Chirag Pabbaraju, Prateek Jain
Learning Functions over Sets via Permutation Adversarial Networks. CoRR (2019)
Arun Sai Suggala, Kush Bhatia, Pradeep Ravikumar, Prateek Jain
Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression. COLT (2019)
Sahil Bhatia, Saswat Padhi, Nagarajan Natarajan, Rahul Sharma, Prateek Jain
OASIS: ILP-Guided Synthesis of Loop Invariants. CoRR (2019)
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 (2019)
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
Efficient Algorithms for Smooth Minimax Optimization. CoRR (2019)
Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli
SGD without Replacement: Sharper Rates for General Smooth Convex Functions. CoRR (2019)
Pengkai Zhu, Durmus Alp Emre Acar, Nan Feng, Prateek Jain, Venkatesh Saligrama
Cost aware Inference for IoT Devices. AISTATS (2019)
Nagarajan Natarajan, Danny Simmons, Naren Datha, Prateek Jain, Sumit Gulwani
Learning Natural Programs from a Few Examples in Real-Time. AISTATS (2019)
Arun Sai Suggala, Kush Bhatia, Pradeep Ravikumar, Prateek Jain
Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression. CoRR (2019)
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)
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
Efficient Algorithms for Smooth Minimax Optimization. NeurIPS (2019)
Bhaskar Mukhoty, Govind Gopakumar, Prateek Jain, Purushottam Kar
Globally-convergent Iteratively Reweighted Least Squares for Robust Regression Problems. AISTATS (2019)
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)
Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli
SGD without Replacement: Sharper Rates for General Smooth Convex Functions. ICML (2019)
Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli
Making the Last Iterate of SGD Information Theoretically Optimal. COLT (2019)
Vivek Gupta, Rahul Wadbude, Nagarajan Natarajan, Harish Karnick, Prateek Jain, Piyush Rai
Distributional Semantics Meets Multi-Label Learning. AAAI (2019)
Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade
On the insufficiency of existing momentum schemes for Stochastic Optimization. ICLR (2018)

Prateek Jain.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Prateek Jain