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

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

Small-Bias Spaces for Group Products. - Raghu Meka; David Zuckerman (APPROX-RANDOM, 2009)

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

An Invariance Principle for Polytopes. - Prahladh Harsha; Adam R. Klivans; Raghu Meka (Electron. Colloquium Comput. Complex., 2009)

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

Bounding the average sensitivity and noise sensitivity of polynomial threshold functions. - Ilias Diakonikolas; Prahladh Harsha; Adam R. Klivans; Raghu Meka; Prasad Raghavendra; Rocco A. Servedio; Li-Yang Tan (STOC, 2010)

Almost Optimal Explicit Johnson-Lindenstrauss Transformations - Raghu Meka (CoRR, 2010)

Polynomial-Time Approximation Schemes for Knapsack and Related Counting Problems using Branching Programs. - Parikshit Gopalan; Adam R. Klivans; Raghu Meka (Electron. Colloquium Comput. Complex., 2010)

Almost Optimal Explicit Johnson-Lindenstrauss Transformations. - Daniel Kane 001; Raghu Meka; Jelani Nelson (Electron. Colloquium Comput. Complex., 2010)

Almost Optimal Explicit Johnson-Lindenstrauss Families. - Daniel Kane 001; Raghu Meka; Jelani Nelson (APPROX-RANDOM, 2011)

An FPTAS for #Knapsack and Related Counting Problems. - Parikshit Gopalan; Adam R. Klivans; Raghu Meka; Daniel Stefankovic; Santosh S. Vempala; Eric Vigoda (FOCS, 2011)

Making the long code shorter, with applications to the Unique Games Conjecture. - Boaz Barak; Parikshit Gopalan; Johan Håstad; Raghu Meka; Prasad Raghavendra; David Steurer (Electron. Colloquium Comput. Complex., 2011)

A PTAS for Computing the Supremum of Gaussian Processes. - Raghu Meka (FOCS, 2012)

DNF Sparsification and a Faster Deterministic Counting. - Parikshit Gopalan; Raghu Meka; Omer Reingold (Electron. Colloquium Comput. Complex., 2012)

Better pseudorandom generators from milder pseudorandom restrictions. - Parikshit Gopalan; Raghu Meka; Omer Reingold; Luca Trevisan 001; Salil P. Vadhan (Electron. Colloquium Comput. Complex., 2012)

Pseudorandomness from Shrinkage. - Russell Impagliazzo; Raghu Meka; David Zuckerman (Electron. Colloquium Comput. Complex., 2012)

Learning Functions of Halfspaces using Prefix Covers. - Parikshit Gopalan; Adam R. Klivans; Raghu Meka (COLT, 2012)

Learning Halfspaces Under Log-Concave Densities: Polynomial Approximations and Moment Matching. - Daniel M. Kane; Adam R. Klivans; Raghu Meka (COLT, 2013)

A PRG for lipschitz functions of polynomials with applications to sparsest cut. - Daniel M. Kane; Raghu Meka (STOC, 2013)

DNF sparsification and a faster deterministic counting algorithm. - Parikshit Gopalan; Raghu Meka; Omer Reingold (Comput. Complex., 2013)

Volumetric Spanners and their Applications to Machine Learning. - Elad Hazan; Zohar Shay Karnin; Raghu Meka (CoRR, 2013)

Moment-Matching Polynomials. - Adam R. Klivans; Raghu Meka (Electron. Colloquium Comput. Complex., 2013)

Association schemes, non-commutative polynomial concentration, and sum-of-squares lower bounds for planted clique. - Raghu Meka; Avi Wigderson (Electron. Colloquium Comput. Complex., 2013)

Pseudorandom Generators for Combinatorial Shapes. - Parikshit Gopalan; Raghu Meka; Omer Reingold; David Zuckerman (SIAM J. Comput., 2013)

Pseudorandom Generators for Polynomial Threshold Functions. - Raghu Meka; David Zuckerman (SIAM J. Comput., 2013)

Deterministic Coupon Collection and Better Strong Dispersers. - Raghu Meka; Omer Reingold; Yuan Zhou 007 (APPROX-RANDOM, 2014)

Computational Limits for Matrix Completion. - Moritz Hardt; Raghu Meka; Prasad Raghavendra; Benjamin Weitz (COLT, 2014)

Volumetric Spanners: an Efficient Exploration Basis for Learning. - Elad Hazan; Zohar Shay Karnin; Raghu Meka (COLT, 2014)

Fast Pseudorandomness for Independence and Load Balancing - (Extended Abstract). - Raghu Meka; Omer Reingold; Guy N. Rothblum; Ron D. Rothblum (ICALP (1), 2014)

Pseudorandomness for concentration bounds and signed majorities. - Parikshit Gopalan; Daniel M. Kane; Raghu Meka (CoRR, 2014)

Almost Optimal Pseudorandom Generators for Spherical Caps. - Pravesh Kothari; Raghu Meka (CoRR, 2014)

Communication with Imperfectly Shared Randomness. - Clément L. Canonne; Venkatesan Guruswami; Raghu Meka; Madhu Sudan 001 (Electron. Colloquium Comput. Complex., 2014)

Bounding the Sensitivity of Polynomial Threshold Functions. - Prahladh Harsha; Adam R. Klivans; Raghu Meka (Theory Comput., 2014)

Almost Optimal Pseudorandom Generators for Spherical Caps: Extended Abstract. - Pravesh K. Kothari; Raghu Meka (STOC, 2015)

Sum-of-squares Lower Bounds for Planted Clique. - Raghu Meka; Aaron Potechin; Avi Wigderson (STOC, 2015)

Explicit resilient functions matching Ajtai-Linial. - Raghu Meka (Electron. Colloquium Comput. Complex., 2015)

Making the Long Code Shorter. - Boaz Barak; Parikshit Gopalan; Johan Håstad; Raghu Meka; Prasad Raghavendra; David Steurer (SIAM J. Comput., 2015)

Constructive Discrepancy Minimization by Walking on the Edges. - Shachar Lovett; Raghu Meka (SIAM J. Comput., 2015)

Rectangles Are Nonnegative Juntas. - Mika Göös; Shachar Lovett; Raghu Meka; Thomas Watson 001; David Zuckerman (SIAM J. Comput., 2016)

Special Section on the Fifty-Fourth Annual IEEE Symposium on Foundations of Computer Science (FOCS 2013). - Moritz Hardt; Yuval Ishai; Raghu Meka; Virginia Vassilevska Williams (SIAM J. Comput., 2016)

Anti-concentration for Polynomials of Independent Random Variables. - Raghu Meka; Oanh Nguyen; Van Vu (Theory Comput., 2016)

Learning Graphical Models Using Multiplicative Weights. - Adam R. Klivans; Raghu Meka (FOCS, 2017)

Efficient Algorithms for Outlier-Robust Regression. - Adam R. Klivans; Pravesh K. Kothari; Raghu Meka (COLT, 2018)

Learning One Convolutional Layer with Overlapping Patches. - Surbhi Goel; Adam R. Klivans; Raghu Meka (ICML, 2018)

Leakage-Resilient Secret Sharing. - Ashutosh Kumar 002; Raghu Meka; Amit Sahai (Electron. Colloquium Comput. Complex., 2018)

Pseudorandom Generators for Width-3 Branching Programs. - Raghu Meka; Omer Reingold; Avishay Tal (Electron. Colloquium Comput. Complex., 2018)

Pseudorandomness via the Discrete Fourier Transform. - Parikshit Gopalan; Daniel M. Kane; Raghu Meka (SIAM J. Comput., 2018)

Leakage-Resilient Secret Sharing Against Colluding Parties. - Ashutosh Kumar 002; Raghu Meka; Amit Sahai (FOCS, 2019)

Average Bias and Polynomial Sources. - Arnab Bhattacharyya 001; Philips George John; Suprovat Ghoshal; Raghu Meka (Electron. Colloquium Comput. Complex., 2019)

Learning Polynomials in Few Relevant Dimensions. - Sitan Chen; Raghu Meka (COLT, 2020)

Balancing Gaussian vectors in high dimension. - Paxton Turner; Raghu Meka; Philippe Rigollet (COLT, 2020)

Extractors and Secret Sharing Against Bounded Collusion Protocols. - Eshan Chattopadhyay; Jesse Goodman; Vipul Goyal; Ashutosh Kumar 002; Xin Li 006; Raghu Meka; David Zuckerman (FOCS, 2020)

Learning Some Popular Gaussian Graphical Models without Condition Number Bounds. - Jonathan A. Kelner; Frederic Koehler; Raghu Meka; Ankur Moitra (NeurIPS, 2020)

Bounded Collusion Protocols, Cylinder-Intersection Extractors and Leakage-Resilient Secret Sharing. - Ashutosh Kumar 002; Raghu Meka; David Zuckerman (Electron. Colloquium Comput. Complex., 2020)

Improved lifting theorems via robust sunflowers. - Shachar Lovett; Raghu Meka; Jiapeng Zhang (Electron. Colloquium Comput. Complex., 2020)

On the discrepancy of random low degree set systems. - Nikhil Bansal 001; Raghu Meka (Random Struct. Algorithms, 2020)

Pseudorandom Generators for Read-Once Monotone Branching Programs. - Dean Doron; Raghu Meka; Omer Reingold; Avishay Tal; Salil P. Vadhan (APPROX-RANDOM, 2021)

Learning Deep ReLU Networks Is Fixed-Parameter Tractable. - Sitan Chen; Adam R. Klivans; Raghu Meka (FOCS, 2021)

On the Power of Preconditioning in Sparse Linear Regression. - Jonathan A. Kelner; Frederic Koehler; Raghu Meka; Dhruv Rohatgi (FOCS, 2021)

Efficiently Learning One Hidden Layer ReLU Networks From Queries. - Sitan Chen; Adam R. Klivans; Raghu Meka (NeurIPS, 2021)

Online Discrepancy Minimization for Stochastic Arrivals. - Nikhil Bansal 001; Haotian Jiang; Raghu Meka; Sahil Singla 001; Makrand Sinha (SODA, 2021)

Efficiently Learning Any One Hidden Layer ReLU Network From Queries. - Sitan Chen; Adam R. Klivans; Raghu Meka (CoRR, 2021)

Monotone Branching Programs: Pseudorandomness and Circuit Complexity. - Dean Doron; Raghu Meka; Omer Reingold; Avishay Tal; Salil P. Vadhan (Electron. Colloquium Comput. Complex., 2021)

Random restrictions and PRGs for PTFs in Gaussian Space. - Zander Kelley; Raghu Meka (Electron. Colloquium Comput. Complex., 2021)

Smoothed Analysis of the Komlós Conjecture. - Nikhil Bansal 001; Haotian Jiang; Raghu Meka; Sahil Singla 001; Makrand Sinha (ICALP, 2022)

Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs. - Sitan Chen; Jerry Li 001; Yuanzhi Li; Raghu Meka (ICLR, 2022)

Prefix Discrepancy, Smoothed Analysis, and Combinatorial Vector Balancing. - Nikhil Bansal 001; Haotian Jiang; Raghu Meka; Sahil Singla 001; Makrand Sinha (ITCS, 2022)

Lifting with Sunflowers. - Shachar Lovett; Raghu Meka; Ian Mertz; Toniann Pitassi; Jiapeng Zhang (ITCS, 2022)

Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. - Sitan Chen; Aravind Gollakota; Adam R. Klivans; Raghu Meka (NeurIPS, 2022)

Sketching based Representations for Robust Image Classification with Provable Guarantees. - Nishanth Dikkala; Sankeerth Rao Karingula; Raghu Meka; Jelani Nelson; Rina Panigrahy; Xin Wang 016 (NeurIPS, 2022)

Lower Bounds on Randomly Preconditioned Lasso via Robust Sparse Designs. - Jonathan A. Kelner; Frederic Koehler; Raghu Meka; Dhruv Rohatgi (NeurIPS, 2022)

Distributional Hardness Against Preconditioned Lasso via Erasure-Robust Designs. - Jonathan A. Kelner; Frederic Koehler; Raghu Meka; Dhruv Rohatgi (CoRR, 2022)

Efficient resilient functions. - Peter Ivanov; Raghu Meka; Emanuele Viola (Electron. Colloquium Comput. Complex., 2022)

Approximating Rectangles by Juntas and Weakly Exponential Lower Bounds for LP Relaxations of CSPs. - Pravesh K. Kothari; Raghu Meka; Prasad Raghavendra (SIAM J. Comput., 2022)

Learning Narrow One-Hidden-Layer ReLU Networks. - Sitan Chen; Zehao Dou; Surbhi Goel; Adam R. Klivans; Raghu Meka (COLT, 2023)

On the Benefits of Learning to Route in Mixture-of-Experts Models. - Nishanth Dikkala; Nikhil Ghosh; Raghu Meka; Rina Panigrahy; Nikhil Vyas 001; Xin Wang 016 (EMNLP, 2023)

Strong Bounds for 3-Progressions. - Zander Kelley; Raghu Meka (FOCS, 2023)

On User-Level Private Convex Optimization. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Raghu Meka; Chiyuan Zhang (ICML, 2023)

User-Level Differential Privacy With Few Examples Per User. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Raghu Meka; Chiyuan Zhang (NeurIPS, 2023)

Feature Adaptation for Sparse Linear Regression. - Jonathan A. Kelner; Frederic Koehler; Raghu Meka; Dhruv Rohatgi (NeurIPS, 2023)

Resolving Matrix Spencer Conjecture Up to Poly-logarithmic Rank. - Nikhil Bansal 001; Haotian Jiang; Raghu Meka (STOC, 2023)

Simple Mechanisms for Representing, Indexing and Manipulating Concepts. - Yuanzhi Li; Raghu Meka; Rina Panigrahy; Kulin Shah (CoRR, 2023)

New Graph Decompositions and Combinatorial Boolean Matrix Multiplication Algorithms. - Amir Abboud; Nick Fischer; Zander Kelley; Shachar Lovett; Raghu Meka (Electron. Colloquium Comput. Complex., 2023)

Explicit separations between randomized and deterministic Number-on-Forehead communication. - Zander Kelley; Shachar Lovett; Raghu Meka (Electron. Colloquium Comput. Complex., 2023)

Special Issue: APPROX-RANDOM 2020: Guest Editor's Foreword. - Jaroslaw Byrka; Raghu Meka (Theory Comput., 2023)

Learning Neural Networks with Sparse Activations. - Pranjal Awasthi; Nishanth Dikkala; Pritish Kamath; Raghu Meka (COLT, 2024)

Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension. - Gautam Chandrasekaran; Adam R. Klivans; Vasilis Kontonis; Raghu Meka; Konstantinos Stavropoulos (COLT, 2024)

On Convex Optimization with Semi-Sensitive Features. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Raghu Meka; Chiyuan Zhang (COLT, 2024)

Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps. - Jonathan A. Kelner; Frederic Koehler; Raghu Meka; Dhruv Rohatgi (COLT, 2024)

Tight Lower Bound for Multicolor Discrepancy. - Pasin Manurangsi; Raghu Meka (CoRR, 2025)

Sparsifying Sums of Positive Semidefinite Matrices. - Arpon Basu; Pravesh K. Kothari; Yang P. Liu; Raghu Meka (CoRR, 2025)