| 2026 | SODA | Sparsifying Sums of Positive Semidefinite Matrices. | Arpon Basu, Pravesh K. Kothari, Yang P. Liu, Raghu Meka |
| 2026 | STOC | Sparse Linear Regression Is Easy on Random Supports. | Gautam Chandrasekaran, Raghu Meka, Konstantinos Stavropoulos |
| 2024 | COLT | Learning Neural Networks with Sparse Activations. | Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath, Raghu Meka |
| 2024 | COLT | Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension. | Gautam Chandrasekaran, Adam R. Klivans, Vasilis Kontonis, Raghu Meka, Konstantinos Stavropoulos |
| 2024 | COLT | On Convex Optimization with Semi-Sensitive Features. | Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang |
| 2024 | COLT | Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps. | Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi |
| 2024 | STOC | New Graph Decompositions and Combinatorial Boolean Matrix Multiplication Algorithms. | Amir Abboud, Nick Fischer, Zander Kelley, Shachar Lovett, Raghu Meka |
| 2024 | STOC | Explicit Separations between Randomized and Deterministic Number-on-Forehead Communication. | Zander Kelley, Shachar Lovett, Raghu Meka |
| 2023 | COLT | Learning Narrow One-Hidden-Layer ReLU Networks. | Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka |
| 2023 | EMNLP | On the Benefits of Learning to Route in Mixture-of-Experts Models. | Nishanth Dikkala, Nikhil Ghosh, Raghu Meka, Rina Panigrahy, Nikhil Vyas, Xin Wang |
| 2023 | FOCS | Strong Bounds for 3-Progressions. | Zander Kelley, Raghu Meka |
| 2023 | ICML | On User-Level Private Convex Optimization. | Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang |
| 2023 | SODA | Efficient resilient functions. | Peter Ivanov, Raghu Meka, Emanuele Viola |
| 2023 | STOC | Resolving Matrix Spencer Conjecture Up to Poly-logarithmic Rank. | Nikhil Bansal, Haotian Jiang, Raghu Meka |
| 2022 | ICALP | Smoothed Analysis of the Komls Conjecture. | Nikhil Bansal, Haotian Jiang, Raghu Meka, Sahil Singla, Makrand Sinha |
| 2022 | ICLR | Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs. | Sitan Chen, Jerry Li, Yuanzhi Li, Raghu Meka |
| 2021 | FOCS | Learning Deep ReLU Networks Is Fixed-Parameter Tractable. | Sitan Chen, Adam R. Klivans, Raghu Meka |
| 2021 | FOCS | On the Power of Preconditioning in Sparse Linear Regression. | Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi |
| 2021 | SODA | Online Discrepancy Minimization for Stochastic Arrivals. | Nikhil Bansal, Haotian Jiang, Raghu Meka, Sahil Singla, Makrand Sinha |
| 2020 | COLT | Learning Polynomials in Few Relevant Dimensions. | Sitan Chen, Raghu Meka |
| 2020 | COLT | Balancing Gaussian vectors in high dimension. | Paxton Turner, Raghu Meka, Philippe Rigollet |
| 2020 | FOCS | Extractors and Secret Sharing Against Bounded Collusion Protocols. | Eshan Chattopadhyay, Jesse Goodman, Vipul Goyal, Ashutosh Kumar, Xin Li, Raghu Meka, David Zuckerman |
| 2019 | FOCS | Leakage-Resilient Secret Sharing Against Colluding Parties. | Ashutosh Kumar, Raghu Meka, Amit Sahai |
| 2019 | SODA | On the discrepancy of random low degree set systems. | Nikhil Bansal, Raghu Meka |
| 2019 | STOC | Pseudorandom generators for width-3 branching programs. | Raghu Meka, Omer Reingold, Avishay Tal |
| 2018 | COLT | Efficient Algorithms for Outlier-Robust Regression. | Adam R. Klivans, Pravesh K. Kothari, Raghu Meka |
| 2018 | ICML | Learning One Convolutional Layer with Overlapping Patches. | Surbhi Goel, Adam R. Klivans, Raghu Meka |
| 2017 | FOCS | Learning Graphical Models Using Multiplicative Weights. | Adam R. Klivans, Raghu Meka |
| 2017 | SODA | Explicit Resilient Functions Matching Ajtai-Linial. | Raghu Meka |
| 2017 | STOC | Approximating rectangles by juntas and weakly-exponential lower bounds for LP relaxations of CSPs. | Pravesh K. Kothari, Raghu Meka, Prasad Raghavendra |
| 2015 | FOCS | Pseudorandomness via the Discrete Fourier Transform. | Parikshit Gopalan, Daniel M. Kane, Raghu Meka |
| 2015 | STOC | Rectangles Are Nonnegative Juntas. | Mika Gs, Shachar Lovett, Raghu Meka, Thomas Watson, David Zuckerman |
| 2015 | STOC | Almost Optimal Pseudorandom Generators for Spherical Caps: Extended Abstract. | Pravesh K. Kothari, Raghu Meka |
| 2015 | STOC | Sum-of-squares Lower Bounds for Planted Clique. | Raghu Meka, Aaron Potechin, Avi Wigderson |
| 2014 | COLT | Computational Limits for Matrix Completion. | Moritz Hardt, Raghu Meka, Prasad Raghavendra, Benjamin Weitz |
| 2014 | COLT | Volumetric Spanners: an Efficient Exploration Basis for Learning. | Elad Hazan, Zohar Shay Karnin, Raghu Meka |
| 2014 | ICALP | Fast Pseudorandomness for Independence and Load Balancing - (Extended Abstract). | Raghu Meka, Omer Reingold, Guy N. Rothblum, Ron D. Rothblum |
| 2013 | COLT | Learning Halfspaces Under Log-Concave Densities: Polynomial Approximations and Moment Matching. | Daniel M. Kane, Adam R. Klivans, Raghu Meka |
| 2013 | STOC | A PRG for lipschitz functions of polynomials with applications to sparsest cut. | Daniel M. Kane, Raghu Meka |
| 2012 | FOCS | Making the Long Code Shorter. | Boaz Barak, Parikshit Gopalan, Johan Hstad, Raghu Meka, Prasad Raghavendra, David Steurer |
| 2012 | FOCS | Better Pseudorandom Generators from Milder Pseudorandom Restrictions. | Parikshit Gopalan, Raghu Meka, Omer Reingold, Luca Trevisan, Salil P. Vadhan |
| 2012 | FOCS | Pseudorandomness from Shrinkage. | Russell Impagliazzo, Raghu Meka, David Zuckerman |
| 2012 | FOCS | Constructive Discrepancy Minimization by Walking on the Edges. | Shachar Lovett, Raghu Meka |
| 2012 | FOCS | A PTAS for Computing the Supremum of Gaussian Processes. | Raghu Meka |
| 2011 | FOCS | An FPTAS for #Knapsack and Related Counting Problems. | Parikshit Gopalan, Adam R. Klivans, Raghu Meka, Daniel Stefankovic, Santosh S. Vempala, Eric Vigoda |
| 2011 | STOC | Pseudorandom generators for combinatorial shapes. | Parikshit Gopalan, Raghu Meka, Omer Reingold, David Zuckerman |
| 2010 | STOC | 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 |
| 2010 | STOC | An invariance principle for polytopes. | Prahladh Harsha, Adam R. Klivans, Raghu Meka |
| 2010 | STOC | Pseudorandom generators for polynomial threshold functions. | Raghu Meka, David Zuckerman |
| 2008 | ICML | Rank minimization via online learning. | Raghu Meka, Prateek Jain, Constantine Caramanis, Inderjit S. Dhillon |
| 2008 | SDM | Simultaneous Unsupervised Learning of Disparate Clusterings. | Prateek Jain, Raghu Meka, Inderjit S. Dhillon |