Anaphora Resolution in Multi-Person Dialogues. - Prateek Jain 002; Manav Ratan Mital; Sumit Kumar; Amitabha Mukerjee; Achla M. Raina (SIGDIAL Workshop, 2004)

Multi-objective Optimization for Adaptive Web Site Generation. - Prateek Jain 002; Pabitra Mitra (PReMI, 2005)

Information-theoretic metric learning. - Jason V. Davis; Brian Kulis; Prateek Jain 002; Suvrit Sra; Inderjit S. Dhillon (ICML, 2007)

Fast image search for learned metrics. - Prateek Jain 002; Brian Kulis; Kristen Grauman (CVPR, 2008)

Rank minimization via online learning. - Raghu Meka; Prateek Jain 002; Constantine Caramanis; Inderjit S. Dhillon (ICML, 2008)

Online Metric Learning and Fast Similarity Search. - Prateek Jain 002; Brian Kulis; Inderjit S. Dhillon; Kristen Grauman (NIPS, 2008)

Simultaneous Unsupervised Learning of Disparate Clusterings. - Prateek Jain 002; Raghu Meka; Inderjit S. Dhillon (SDM, 2008)

Active learning for large multi-class problems. - Prateek Jain 002; Ashish Kapoor (CVPR, 2009)

Geometry-aware metric learning. - Zhengdong Lu; Prateek Jain 002; Inderjit S. Dhillon (ICML, 2009)

Matrix Completion from Power-Law Distributed Samples. - Raghu Meka; Prateek Jain 002; Inderjit S. Dhillon (NIPS, 2009)

Fast Similarity Search for Learned Metrics. - Brian Kulis; Prateek Jain 002; Kristen Grauman (IEEE Trans. Pattern Anal. Mach. Intell., 2009)

Far-sighted active learning on a budget for image and video recognition. - Sudheendra Vijayanarasimhan; Prateek Jain 002; Kristen Grauman (CVPR, 2010)

Inductive Regularized Learning of Kernel Functions. - Prateek Jain 002; Brian Kulis; Inderjit S. Dhillon (NIPS, 2010)

Guaranteed Rank Minimization via Singular Value Projection. - Prateek Jain 002; Raghu Meka; Inderjit S. Dhillon (NIPS, 2010)

Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning. - Prateek Jain 002; Sudheendra Vijayanarasimhan; Kristen Grauman (NIPS, 2010)

Orthogonal Matching Pursuit with Replacement. - Prateek Jain 002; Ambuj Tewari; Inderjit S. Dhillon (NIPS, 2011)

Similarity-based Learning via Data Driven Embeddings. - Purushottam Kar; Prateek Jain 002 (NIPS, 2011)

A Learning Framework for Self-Tuning Histograms - Raajay Viswanathan; Prateek Jain 002; Srivatsan Laxman; Arvind Arasu (CoRR, 2011)

Mirror Descent Based Database Privacy. - Prateek Jain 002; Abhradeep Thakurta (APPROX-RANDOM, 2012)

Improved multiple sequence alignments using coupled pattern mining. - K. S. M. Tozammel Hossain; Debprakash Patnaik; Srivatsan Laxman; Prateek Jain 002; Chris Bailey-Kellogg; Naren Ramakrishnan (BCB, 2012)

Multilabel Classification using Bayesian Compressed Sensing. - Ashish Kapoor; Raajay Viswanathan; Prateek Jain 002 (NIPS, 2012)

Supervised Learning with Similarity Functions. - Purushottam Kar; Prateek Jain 002 (NIPS, 2012)

The Interplay Between Stability and Regret in Online Learning - Ankan Saha; Prateek Jain 002; Ambuj Tewari (CoRR, 2012)

Metric and Kernel Learning Using a Linear Transformation. - Prateek Jain 002; Brian Kulis; Jason V. Davis; Inderjit S. Dhillon (J. Mach. Learn. Res., 2012)

Differentially Private Online Learning. - Prateek Jain 002; Pravesh Kothari; Abhradeep Thakurta (COLT, 2012)

Differentially Private Learning with Kernels. - Prateek Jain 002; Abhradeep Thakurta (ICML (3), 2013)

One-Bit Compressed Sensing: Provable Support and Vector Recovery. - Sivakanth Gopi; Praneeth Netrapalli; Prateek Jain 002; Aditya V. Nori (ICML (3), 2013)

On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions. - Purushottam Kar; Bharath K. Sriperumbudur; Prateek Jain 002; Harish Karnick (ICML (3), 2013)

Memory Limited, Streaming PCA. - Ioannis Mitliagkas; Constantine Caramanis; Prateek Jain 002 (NIPS, 2013)

Phase Retrieval using Alternating Minimization. - Praneeth Netrapalli; Prateek Jain 002; Sujay Sanghavi (NIPS, 2013)

Low-rank matrix completion using alternating minimization. - Prateek Jain 002; Praneeth Netrapalli; Sujay Sanghavi (STOC, 2013)

Ad impression forecasting for sponsored search. - Abhirup Nath; Shibnath Mukherjee; Prateek Jain 002; Navin Goyal; Srivatsan Laxman (WWW, 2013)

Provable Inductive Matrix Completion. - Prateek Jain 002; Inderjit S. Dhillon (CoRR, 2013)

Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization. - Alekh Agarwal; Animashree Anandkumar; Prateek Jain 002; Praneeth Netrapalli; Rashish Tandon (CoRR, 2013)

Large-scale Multi-label Learning with Missing Labels. - Hsiang-Fu Yu; Prateek Jain 002; Inderjit S. Dhillon (CoRR, 2013)

Learning Mixtures of Discrete Product Distributions using Spectral Decompositions. - Prateek Jain 002; Sewoong Oh (COLT, 2014)

Learning Sparsely Used Overcomplete Dictionaries. - Alekh Agarwal; Animashree Anandkumar; Prateek Jain 002; Praneeth Netrapalli; Rashish Tandon (COLT, 2014)

(Near) Dimension Independent Risk Bounds for Differentially Private Learning. - Prateek Jain 002; Abhradeep Guha Thakurta (ICML, 2014)

Universal Matrix Completion. - Srinadh Bhojanapalli; Prateek Jain 002 (ICML, 2014)

Large-scale Multi-label Learning with Missing Labels. - Hsiang-Fu Yu; Prateek Jain 002; Purushottam Kar; Inderjit S. Dhillon (ICML, 2014)

Provable Tensor Factorization with Missing Data. - Prateek Jain 002; Sewoong Oh (NIPS, 2014)

On Iterative Hard Thresholding Methods for High-dimensional M-Estimation. - Prateek Jain 002; Ambuj Tewari; Purushottam Kar (NIPS, 2014)

Provable Submodular Minimization using Wolfe's Algorithm. - Deeparnab Chakrabarty; Prateek Jain 002; Pravesh Kothari (NIPS, 2014)

Online and Stochastic Gradient Methods for Non-decomposable Loss Functions. - Purushottam Kar; Harikrishna Narasimhan; Prateek Jain 002 (NIPS, 2014)

Non-convex Robust PCA. - Praneeth Netrapalli; U. N. Niranjan; Sujay Sanghavi; Animashree Anandkumar; Prateek Jain 002 (NIPS, 2014)

Efficient Matrix Sensing Using Rank-1 Gaussian Measurements. - Kai Zhong; Prateek Jain 002; Inderjit S. Dhillon (ALT, 2015)

Fast Exact Matrix Completion with Finite Samples. - Prateek Jain 002; Praneeth Netrapalli (COLT, 2015)

Surrogate Functions for Maximizing Precision at the Top. - Purushottam Kar; Harikrishna Narasimhan; Prateek Jain 002 (ICML, 2015)

Optimizing Non-decomposable Performance Measures: A Tale of Two Classes. - Harikrishna Narasimhan; Purushottam Kar; Prateek Jain 002 (ICML, 2015)

Robust Regression via Hard Thresholding. - Kush Bhatia; Prateek Jain 002; Purushottam Kar (NIPS, 2015)

Sparse Local Embeddings for Extreme Multi-label Classification. - Kush Bhatia; Himanshu Jain; Purushottam Kar; Manik Varma; Prateek Jain 002 (NIPS, 2015)

Predtron: A Family of Online Algorithms for General Prediction Problems. - Prateek Jain 002; Nagarajan Natarajan; Ambuj Tewari (NIPS, 2015)

Alternating Minimization for Regression Problems with Vector-valued Outputs. - Prateek Jain 002; Ambuj Tewari (NIPS, 2015)

Tighter Low-rank Approximation via Sampling the Leveraged Element. - Srinadh Bhojanapalli; Prateek Jain 002; Sujay Sanghavi (SODA, 2015)

Computing Matrix Squareroot via Non Convex Local Search. - Prateek Jain 002; Chi Jin 001; Sham M. Kakade; Praneeth Netrapalli (CoRR, 2015)

To Drop or Not to Drop: Robustness, Consistency and Differential Privacy Properties of Dropout. - Prateek Jain 002; Vivek Kulkarni; Abhradeep Thakurta; Oliver Williams (CoRR, 2015)

Locally Non-linear Embeddings for Extreme Multi-label Learning. - Kush Bhatia; Himanshu Jain; Purushottam Kar; Prateek Jain 002; Manik Varma (CoRR, 2015)

Diverse Yet Efficient Retrieval using Hash Functions. - Vidyadhar Rao; Prateek Jain 002; C. V. Jawahar (CoRR, 2015)

Tensor vs. Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations. - Anima Anandkumar; Prateek Jain 002; Yang Shi 005; U. N. Niranjan (AISTATS, 2016)

Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm. - Prateek Jain 002; Chi Jin 001; Sham M. Kakade; Praneeth Netrapalli; Aaron Sidford (COLT, 2016)

Diverse Yet Efficient Retrieval using Locality Sensitive Hashing. - Vidyadhar Rao; Prateek Jain 002; C. V. Jawahar (ICMR, 2016)

Structured Sparse Regression via Greedy Hard Thresholding. - Prateek Jain 002; Nikhil Rao 001; Inderjit S. Dhillon (NIPS, 2016)

Regret Bounds for Non-decomposable Metrics with Missing Labels. - Nagarajan Natarajan; Prateek Jain 002 (NIPS, 2016)

Selective inference for group-sparse linear models. - Fan Yang; Rina Foygel Barber; Prateek Jain 002; John D. Lafferty (NIPS, 2016)

Mixed Linear Regression with Multiple Components. - Kai Zhong; Prateek Jain 002; Inderjit S. Dhillon (NIPS, 2016)

Matching Matrix Bernstein with Little Memory: Near-Optimal Finite Sample Guarantees for Oja's Algorithm. - Prateek Jain 002; Chi Jin 001; Sham M. Kakade; Praneeth Netrapalli; Aaron Sidford (CoRR, 2016)

Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging. - Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli; Aaron Sidford (CoRR, 2016)

Efficient and Consistent Robust Time Series Analysis. - Kush Bhatia; Prateek Jain 002; Parameswaran Kamalaruban; Purushottam Kar (CoRR, 2016)

Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization. - Alekh Agarwal; Animashree Anandkumar; Prateek Jain 002; Praneeth Netrapalli (SIAM J. Optim., 2016)

Scalable Optimization of Multivariate Performance Measures in Multi-instance Multi-label Learning. - Apoorv Aggarwal; Sandip Ghoshal; Ankith M. S. Shetty; Suhit Sinha; Ganesh Ramakrishnan; Purushottam Kar; Prateek Jain 002 (AAAI, 2017)

Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot. - Prateek Jain 002; Chi Jin 001; Sham M. Kakade; Praneeth Netrapalli (AISTATS, 2017)

Programming by Examples: PL Meets ML. - Sumit Gulwani; Prateek Jain 002 (APLAS, 2017)

Thresholding Based Outlier Robust PCA. - Yeshwanth Cherapanamjeri; Prateek Jain 002; Praneeth Netrapalli (COLT, 2017)

A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares). - Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli; Venkata Krishna Pillutla; Aaron Sidford (FSTTCS, 2017)

Active Heteroscedastic Regression. - Kamalika Chaudhuri; Prateek Jain 002; Nagarajan Natarajan (ICML, 2017)

Nearly Optimal Robust Matrix Completion. - Yeshwanth Cherapanamjeri; Kartik Gupta; Prateek Jain 002 (ICML, 2017)

ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices. - Chirag Gupta; Arun Sai Suggala; Ankit Goyal; Harsha Vardhan Simhadri; Bhargavi Paranjape; Ashish Kumar 007; Saurabh Goyal; Raghavendra Udupa; Manik Varma; Prateek Jain 002 (ICML, 2017)

Recovery Guarantees for One-hidden-layer Neural Networks. - Kai Zhong; Zhao Song 002; Prateek Jain 002; Peter L. Bartlett; Inderjit S. Dhillon (ICML, 2017)

Fast second-order cone programming for safe mission planning. - Kai Zhong; Prateek Jain 002; Ashish Kapoor (ICRA, 2017)

Consistent Robust Regression. - Kush Bhatia; Prateek Jain 002; Parameswaran Kamalaruban; Purushottam Kar (NIPS, 2017)

Learning Mixture of Gaussians with Streaming Data. - Aditi Raghunathan; Prateek Jain 002; Ravishankar Krishnaswamy (NIPS, 2017)

Accelerating Stochastic Gradient Descent. - Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli; Aaron Sidford (CoRR, 2017)

Thresholding based Efficient Outlier Robust PCA. - Yeshwanth Cherapanamjeri; Prateek Jain 002; Praneeth Netrapalli (CoRR, 2017)

FlashProfile: Interactive Synthesis of Syntactic Profiles. - Saswat Padhi; Prateek Jain 002; Daniel Perelman; Oleksandr Polozov; Sumit Gulwani; Todd D. Millstein (CoRR, 2017)

Non-convex Optimization for Machine Learning. - Prateek Jain 002; Purushottam Kar (Found. Trends Mach. Learn., 2017)

Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification. - Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli; Aaron Sidford (J. Mach. Learn. Res., 2017)

Partial Hard Thresholding. - Prateek Jain 002; Ambuj Tewari; Inderjit S. Dhillon (IEEE Trans. Inf. Theory, 2017)

Accelerating Stochastic Gradient Descent for Least Squares Regression. - Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli; Aaron Sidford (COLT, 2018)

Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form. - Srinadh Bhojanapalli; Nicolas Boumal; Prateek Jain 002; Praneeth Netrapalli (COLT, 2018)

Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples. - Ashwin Kalyan; Abhishek Mohta; Oleksandr Polozov; Dhruv Batra; Prateek Jain 002; Sumit Gulwani (ICLR (Poster), 2018)

On the insufficiency of existing momentum schemes for Stochastic Optimization. - Rahul Kidambi; Praneeth Netrapalli; Prateek Jain 002; Sham M. Kakade (ICLR, 2018)

Differentially Private Matrix Completion Revisited. - Prateek Jain 002; Om Dipakbhai Thakkar; Abhradeep Thakurta (ICML, 2018)

Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices. - Don Kurian Dennis; Chirag Pabbaraju; Harsha Vardhan Simhadri; Prateek Jain 002 (NeurIPS, 2018)

FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network. - Aditya Kusupati; Manish Singh; Kush Bhatia; Ashish Kumar 007; Prateek Jain 002; Manik Varma (NeurIPS, 2018)

Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds. - Raghav Somani; Chirag Gupta; Prateek Jain 002; Praneeth Netrapalli (NeurIPS, 2018)

Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples. - Ashwin J. Vijayakumar; Abhishek Mohta; Oleksandr Polozov; Dhruv Batra; Prateek Jain 002; Sumit Gulwani (CoRR, 2018)

Nonlinear Inductive Matrix Completion based on One-layer Neural Networks. - Kai Zhong; Zhao Song 002; Prateek Jain 002; Inderjit S. Dhillon (CoRR, 2018)

FlashProfile: a framework for synthesizing data profiles. - Saswat Padhi; Prateek Jain 002; Daniel Perelman; Oleksandr Polozov; Sumit Gulwani; Todd D. Millstein (Proc. ACM Program. Lang., 2018)

Distributional Semantics Meets Multi-Label Learning. - Vivek Gupta 001; Rahul Wadbude; Nagarajan Natarajan; Harish Karnick; Prateek Jain 002; Piyush Rai (AAAI, 2019)

Globally-convergent Iteratively Reweighted Least Squares for Robust Regression Problems. - Bhaskar Mukhoty; Govind Gopakumar; Prateek Jain 002; Purushottam Kar (AISTATS, 2019)

Learning Natural Programs from a Few Examples in Real-Time. - Nagarajan Natarajan; Danny Simmons; Naren Datha; Prateek Jain 002; Sumit Gulwani (AISTATS, 2019)

Cost aware Inference for IoT Devices. - Pengkai Zhu; Durmus Alp Emre Acar; Nan Feng; Prateek Jain 002; Venkatesh Saligrama (AISTATS, 2019)

Open Problem: Do Good Algorithms Necessarily Query Bad Points? - Rong Ge 001; Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Dheeraj M. Nagaraj; Praneeth Netrapalli (COLT, 2019)

Making the Last Iterate of SGD Information Theoretically Optimal. - Prateek Jain 002; Dheeraj Nagaraj; Praneeth Netrapalli (COLT, 2019)

Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression. - Arun Sai Suggala; Kush Bhatia; Pradeep Ravikumar; Prateek Jain 002 (COLT, 2019)

SGD without Replacement: Sharper Rates for General Smooth Convex Functions. - Dheeraj Nagaraj; Prateek Jain 002; Praneeth Netrapalli (ICML, 2019)

Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices. - Don Kurian Dennis; Durmus Alp Emre Acar; Vikram Mandikal; Vinu Sankar Sadasivan; Venkatesh Saligrama; Harsha Vardhan Simhadri; Prateek Jain 002 (NeurIPS, 2019)

Efficient Algorithms for Smooth Minimax Optimization. - Kiran Koshy Thekumparampil; Prateek Jain 002; Praneeth Netrapalli; Sewoong Oh (NeurIPS, 2019)

Provable Non-linear Inductive Matrix Completion. - Kai Zhong; Zhao Song 002; Prateek Jain 002; Inderjit S. Dhillon (NeurIPS, 2019)

GesturePod: Enabling On-device Gesture-based Interaction for White Cane Users. - Shishir G. Patil; Don Kurian Dennis; Chirag Pabbaraju; Nadeem Shaheer; Harsha Vardhan Simhadri; Vivek Seshadri; Manik Varma; Prateek Jain 002 (UIST, 2019)

Learning Functions over Sets via Permutation Adversarial Networks. - Chirag Pabbaraju; Prateek Jain 002 (CoRR, 2019)

OASIS: ILP-Guided Synthesis of Loop Invariants. - Sahil Bhatia; Saswat Padhi; Nagarajan Natarajan; Rahul Sharma 001; Prateek Jain 002 (CoRR, 2019)

DROCC: Deep Robust One-Class Classification. - Sachin Goyal; Aditi Raghunathan; Moksh Jain; Harsha Vardhan Simhadri; Prateek Jain 002 (ICML, 2020)

Soft Threshold Weight Reparameterization for Learnable Sparsity. - Aditya Kusupati; Vivek Ramanujan; Raghav Somani; Mitchell Wortsman; Prateek Jain 002; Sham M. Kakade; Ali Farhadi (ICML, 2020)

Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms. - Dheeraj Nagaraj; Xian Wu 009; Guy Bresler; Prateek Jain 002; Praneeth Netrapalli (NeurIPS, 2020)

RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference. - Oindrila Saha; Aditya Kusupati; Harsha Vardhan Simhadri; Manik Varma; Prateek Jain 002 (NeurIPS, 2020)

The Pitfalls of Simplicity Bias in Neural Networks. - Harshay Shah; Kaustav Tamuly; Aditi Raghunathan; Prateek Jain 002; Praneeth Netrapalli (NeurIPS, 2020)

Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method. - Kiran Koshy Thekumparampil; Prateek Jain 002; Praneeth Netrapalli; Sewoong Oh (NeurIPS, 2020)

Programming by Rewards. - Nagarajan Natarajan; Ajaykrishna Karthikeyan; Prateek Jain 002; Ivan Radicek; Sriram K. Rajamani; Sumit Gulwani; Johannes Gehrke (CoRR, 2020)

Nonconvex Optimization for Signal Processing and Machine Learning [From the Guest Editors]. - Anthony Man-Cho So; Prateek Jain 002; Wing-Kin Ma; Gesualdo Scutari (IEEE Signal Process. Mag., 2020)

Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates. - Steve Chien; Prateek Jain 002; Walid Krichene; Steffen Rendle; Shuang Song 001; Abhradeep Thakurta; Li Zhang 001 (ICML, 2021)

Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization. - Aadirupa Saha; Nagarajan Natarajan; Praneeth Netrapalli; Prateek Jain 002 (ICML, 2021)

Streaming Linear System Identification with Reverse Experience Replay. - Prateek Jain 002; Suhas S. Kowshik; Dheeraj Nagaraj; Praneeth Netrapalli (NeurIPS, 2021)

Differentially Private Model Personalization. - Prateek Jain 002; John Rush; Adam D. Smith 001; Shuang Song 001; Abhradeep Guha Thakurta (NeurIPS, 2021)

Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems. - Suhas S. Kowshik; Dheeraj Nagaraj; Prateek Jain 002; Praneeth Netrapalli (NeurIPS, 2021)

LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes. - Aditya Kusupati; Matthew Wallingford; Vivek Ramanujan; Raghav Somani; Jae Sung Park; Krishna Pillutla; Prateek Jain 002; Sham M. Kakade; Ali Farhadi (NeurIPS, 2021)

Do Input Gradients Highlight Discriminative Features? - Harshay Shah; Prateek Jain 002; Praneeth Netrapalli (NeurIPS, 2021)

Statistically and Computationally Efficient Linear Meta-representation Learning. - Kiran Koshy Thekumparampil; Prateek Jain 002; Praneeth Netrapalli; Sewoong Oh (NeurIPS, 2021)

Sample Efficient Linear Meta-Learning by Alternating Minimization. - Kiran Koshy Thekumparampil; Prateek Jain 002; Praneeth Netrapalli; Sewoong Oh (CoRR, 2021)

Node-Level Differentially Private Graph Neural Networks. - Ameya Daigavane; Gagan Madan; Aditya Sinha; Abhradeep Guha Thakurta; Gaurav Aggarwal; Prateek Jain 002 (CoRR, 2021)

Robust Training in High Dimensions via Block Coordinate Geometric Median Descent. - Anish Acharya; Abolfazl Hashemi; Prateek Jain 002; Sujay Sanghavi; Inderjit S. Dhillon; Ufuk Topcu (AISTATS, 2022)

(Nearly) Optimal Private Linear Regression for Sub-Gaussian Data via Adaptive Clipping. - Prateek Varshney; Abhradeep Thakurta; Prateek Jain 002 (COLT, 2022)

Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs. - Naman Agarwal; Syomantak Chaudhuri; Prateek Jain 002; Dheeraj Mysore Nagaraj; Praneeth Netrapalli (ICLR, 2022)

IGLU: Efficient GCN Training via Lazy Updates. - S. Deepak Narayanan; Aditya Sinha; Prateek Jain 002; Purushottam Kar; Sundararajan Sellamanickam (ICLR, 2022)

Reproducibility in Optimization: Theoretical Framework and Limits. - Kwangjun Ahn; Prateek Jain 002; Ziwei Ji; Satyen Kale; Praneeth Netrapalli; Gil I. Shamir (NeurIPS, 2022)

S3GC: Scalable Self-Supervised Graph Clustering. - Devvrit; Aditya Sinha; Inderjit S. Dhillon; Prateek Jain 002 (NeurIPS, 2022)

Matryoshka Representation Learning. - Aditya Kusupati; Gantavya Bhatt; Aniket Rege; Matthew Wallingford; Aditya Sinha; Vivek Ramanujan; William Howard-Snyder; Kaifeng Chen; Sham M. Kakade; Prateek Jain 002; Ali Farhadi (NeurIPS, 2022)

DP-PCA: Statistically Optimal and Differentially Private PCA. - Xiyang Liu; Weihao Kong; Prateek Jain 002; Sewoong Oh (NeurIPS, 2022)

Matryoshka Representations for Adaptive Deployment. - Aditya Kusupati; Gantavya Bhatt; Aniket Rege; Matthew Wallingford; Aditya Sinha; Vivek Ramanujan; William Howard-Snyder; Kaifeng Chen; Sham M. Kakade; Prateek Jain 002; Ali Farhadi (CoRR, 2022)

MET: Masked Encoding for Tabular Data. - Kushal Majmundar; Sachin Goyal; Praneeth Netrapalli; Prateek Jain 002 (CoRR, 2022)

(Nearly) Optimal Private Linear Regression via Adaptive Clipping. - Prateek Varshney; Abhradeep Thakurta; Prateek Jain 002 (CoRR, 2022)

DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization. - Anshul Nasery; Sravanti Addepalli; Praneeth Netrapalli; Prateek Jain 002 (CoRR, 2022)

Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks. - Sravanti Addepalli; Anshul Nasery; R. Venkatesh Babu; Praneeth Netrapalli; Prateek Jain 002 (CoRR, 2022)

Private and Efficient Meta-Learning with Low Rank and Sparse Decomposition. - Soumyabrata Pal; Prateek Varshney; Prateek Jain 002; Abhradeep Guha Thakurta; Gagan Madan; Gaurav Aggarwal; Pradeep Shenoy; Gaurav Srivastava 004 (CoRR, 2022)

Learning Accurate Decision Trees with Bandit Feedback via Quantized Gradient Descent. - Ajaykrishna Karthikeyan; Naman Jain; Nagarajan Natarajan; Prateek Jain 002 (Trans. Mach. Learn. Res., 2022)

Optimal Algorithms for Latent Bandits with Cluster Structure. - Soumyabrata Pal; Arun Sai Suggala; Karthikeyan Shanmugam 001; Prateek Jain 002 (AISTATS, 2023)

Feature Reconstruction From Outputs Can Mitigate Simplicity Bias in Neural Networks. - Sravanti Addepalli; Anshul Nasery; Venkatesh Babu Radhakrishnan; Praneeth Netrapalli; Prateek Jain 002 (ICLR, 2023)

Treeformer: Dense Gradient Trees for Efficient Attention Computation. - Lovish Madaan; Srinadh Bhojanapalli; Himanshu Jain; Prateek Jain 002 (ICLR, 2023)

Online Low Rank Matrix Completion. - Soumyabrata Pal; Prateek Jain 002 (ICLR, 2023)

Multi-Task Differential Privacy Under Distribution Skew. - Walid Krichene; Prateek Jain 002; Shuang Song 001; Mukund Sundararajan; Abhradeep Guha Thakurta; Li Zhang 001 (ICML, 2023)

Multi-User Reinforcement Learning with Low Rank Rewards. - Dheeraj Mysore Nagaraj; Suhas S. Kowshik; Naman Agarwal; Praneeth Netrapalli; Prateek Jain 002 (ICML, 2023)

Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency. - Xiyang Liu; Prateek Jain 002; Weihao Kong; Sewoong Oh; Arun Sai Suggala (NeurIPS, 2023)

Simplicity Bias in 1-Hidden Layer Neural Networks. - Depen Morwani; Jatin Batra; Prateek Jain 002; Praneeth Netrapalli (NeurIPS, 2023)

Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints. - Soumyabrata Pal; Arun Sai Suggala; Karthikeyan Shanmugam 001; Prateek Jain 002 (NeurIPS, 2023)

AdANNS: A Framework for Adaptive Semantic Search. - Aniket Rege; Aditya Kusupati; Sharan Ranjit S; Alan Fan; Qingqing Cao; Sham M. Kakade; Prateek Jain 002; Ali Farhadi (NeurIPS, 2023)

Near Optimal Private and Robust Linear Regression. - Xiyang Liu; Prateek Jain 002; Weihao Kong; Sewoong Oh; Arun Sai Suggala (CoRR, 2023)

End-to-End Neural Network Compression via 𝓁1/𝓁2 Regularized Latency Surrogates. - Anshul Nasery; Hardik Shah; Arun Sai Suggala; Prateek Jain 002 (CoRR, 2023)

Efficacy of Dual-Encoders for Extreme Multi-Label Classification. - Nilesh Gupta; Devvrit Khatri; Ankit Singh Rawat; Srinadh Bhojanapalli; Prateek Jain 002; Inderjit S. Dhillon (CoRR, 2023)

Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components. - Soumyabrata Pal; Prateek Varshney; Gagan Madan; Prateek Jain 002; Abhradeep Thakurta; Gaurav Aggarwal; Pradeep Shenoy; Gaurav Srivastava 004 (AISTATS, 2024)

End-to-End Neural Network Compression via l1/l2 Regularized Latency Surrogates. - Anshul Nasery; Hardik Shah; Arun Sai Suggala; Prateek Jain 002 (CVPR Workshops, 2024)

LookupViT: Compressing Visual Information to a Limited Number of Tokens. - Rajat Koner; Gagan Jain; Prateek Jain 002; Volker Tresp; Sujoy Paul (ECCV (86), 2024)

Dual-Encoders for Extreme Multi-label Classification. - Nilesh Gupta; Devvrit; Ankit Singh Rawat; Srinadh Bhojanapalli; Prateek Jain 002; Inderjit S. Dhillon (ICLR, 2024)

Tandem Transformers for Inference Efficient LLMs. - Aishwarya P. S.; Pranav Ajit Nair; Yashas Samaga; Toby Boyd; Sanjiv Kumar; Prateek Jain 002; Praneeth Netrapalli (ICML, 2024)

MatFormer: Nested Transformer for Elastic Inference. - Devvrit; Sneha Kudugunta; Aditya Kusupati; Tim Dettmers; Kaifeng Chen; Inderjit S. Dhillon; Yulia Tsvetkov; Hanna Hajishirzi; Sham M. Kakade; Ali Farhadi; Prateek Jain 002 (NeurIPS, 2024)

Mixture of Nested Experts: Adaptive Processing of Visual Tokens. - Gagan Jain; Nidhi Hegde 003; Aditya Kusupati; Arsha Nagrani; Shyamal Buch; Prateek Jain 002; Anurag Arnab; Sujoy Paul (NeurIPS, 2024)

Time-Reversal Provides Unsupervised Feedback to LLMs. - Yerram Varun; Rahul Madhavan; Sravanti Addepalli; Arun Sai Suggala; Karthikeyan Shanmugam 001; Prateek Jain 002 (NeurIPS, 2024)

HiRE: High Recall Approximate Top-k Estimation for Efficient LLM Inference. - Yashas Samaga; Varun Yerram; Chong You; Srinadh Bhojanapalli; Sanjiv Kumar; Prateek Jain 002; Praneeth Netrapalli (CoRR, 2024)

Gecko: Versatile Text Embeddings Distilled from Large Language Models. - Jinhyuk Lee; Zhuyun Dai; Xiaoqi Ren; Blair Chen; Daniel Cer; Jeremy R. Cole; Kai Hui 001; Michael Boratko; Rajvi Kapadia; Wen Ding; Yi Luan; Sai Meher Karthik Duddu; et al. (CoRR, 2024)

EHI: End-to-end Learning of Hierarchical Index for Efficient Dense Retrieval. - Ramnath Kumar; Anshul Mittal; Nilesh Gupta; Aditya Kusupati; Inderjit S. Dhillon; Prateek Jain 002 (Trans. Mach. Learn. Res., 2024)

Does Safety Training of LLMs Generalize to Semantically Related Natural Prompts? - Sravanti Addepalli; Yerram Varun; Arun Sai Suggala; Karthikeyan Shanmugam 001; Prateek Jain 002 (ICLR, 2025)

Masked Generative Nested Transformers with Decode Time Scaling. - Sahil Goyal; Debapriya Tula; Gagan Jain; Pradeep Shenoy; Prateek Jain 002; Sujoy Paul (ICML, 2025)

Matryoshka Quantization. - Pranav Ajit Nair; Puranjay Datta; Jeff Dean; Prateek Jain 002; Aditya Kusupati (ICML, 2025)

Matryoshka Model Learning for Improved Elastic Student Models. - Chetan Verma; Aditya Srinivas Timmaraju; Cho-Jui Hsieh; Suyash Damle; Ngot Bui; Yang Zhang; Wen Chen; Xin Liu; Prateek Jain 002; Inderjit S. Dhillon (KDD (2), 2025)

Interleaved Gibbs Diffusion for Constrained Generation. - Gautham Govind Anil; Sachin Yadav; Dheeraj Nagaraj; Karthikeyan Shanmugam 001; Prateek Jain 002 (CoRR, 2025)

Faster Approx. Top-K: Harnessing the Full Power of Two Stages. - Yashas Samaga; Varun Yerram; Spandana Raj Babbula; Prateek Jain 002; Praneeth Netrapalli (CoRR, 2025)

Spark Transformer: Reactivating Sparsity in FFN and Attention. - Chong You; Kan Wu; Zhipeng Jia; Lin Chen; Srinadh Bhojanapalli; Jiaxian Guo; Utku Evci; Jan Wassenberg; Praneeth Netrapalli; Jeremiah J. Willcock; Suvinay Subramanian; Felix Chern; et al. (CoRR, 2025)

Compressing Many-Shots in In-Context Learning. - Devvrit Khatri; Pranamya Kulkarni; Nilesh Gupta; Yerram Varun; Liqian Peng; Jay Yagnik; Praneeth Netrapalli; Cho-Jui Hsieh; Alec Go; Inderjit S. Dhillon; Aditya Kusupati; Prateek Jain 002 (CoRR, 2025)