default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDRaghu Meka
Person information
SPARQL queries 
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[c68]Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath, Raghu Meka:
Learning Neural Networks with Sparse Activations. COLT 2024: 406-425- [c67]Gautam Chandrasekaran, Adam R. Klivans, Vasilis Kontonis, Raghu Meka, Konstantinos Stavropoulos:
Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension. COLT 2024: 876-922 - [c66]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
On Convex Optimization with Semi-Sensitive Features. COLT 2024: 1916-1938 - [c65]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps. COLT 2024: 2840-2886 - [c64]Amir Abboud
, Nick Fischer, Zander Kelley, Shachar Lovett, Raghu Meka:
New Graph Decompositions and Combinatorial Boolean Matrix Multiplication Algorithms. STOC 2024: 935-943 - [c63]Zander Kelley, Shachar Lovett, Raghu Meka:
Explicit Separations between Randomized and Deterministic Number-on-Forehead Communication. STOC 2024: 1299-1310 - [i77]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps. CoRR abs/2402.15409 (2024) - [i76]Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath, Raghu Meka:
Learning Neural Networks with Sparse Activations. CoRR abs/2406.17989 (2024) - [i75]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
On Convex Optimization with Semi-Sensitive Features. CoRR abs/2406.19040 (2024) - [i74]Gautam Chandrasekaran, Adam R. Klivans, Vasilis Kontonis, Raghu Meka, Konstantinos Stavropoulos:
Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension. CoRR abs/2407.00966 (2024) - 2023
- [j17]Jaroslaw Byrka, Raghu Meka:
Special Issue: APPROX-RANDOM 2020: Guest Editor's Foreword. Theory Comput. 19: 1-3 (2023) - [c62]Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka:
Learning Narrow One-Hidden-Layer ReLU Networks. COLT 2023: 5580-5614 - [c61]Nishanth Dikkala, Nikhil Ghosh, Raghu Meka, Rina Panigrahy, Nikhil Vyas, Xin Wang:
On the Benefits of Learning to Route in Mixture-of-Experts Models. EMNLP 2023: 9376-9396 - [c60]Zander Kelley, Raghu Meka:
Strong Bounds for 3-Progressions. FOCS 2023: 933-973 - [c59]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
On User-Level Private Convex Optimization. ICML 2023: 11283-11299 - [c58]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
User-Level Differential Privacy With Few Examples Per User. NeurIPS 2023 - [c57]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Feature Adaptation for Sparse Linear Regression. NeurIPS 2023 - [c56]Peter Ivanov, Raghu Meka, Emanuele Viola:
Efficient resilient functions. SODA 2023: 2867-2874 - [c55]Nikhil Bansal, Haotian Jiang, Raghu Meka:
Resolving Matrix Spencer Conjecture Up to Poly-logarithmic Rank. STOC 2023: 1814-1819 - [i73]Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka:
Learning Narrow One-Hidden-Layer ReLU Networks. CoRR abs/2304.10524 (2023) - [i72]Badih Ghazi, Pritish Kamath, Ravi Kumar, Raghu Meka, Pasin Manurangsi, Chiyuan Zhang:
On User-Level Private Convex Optimization. CoRR abs/2305.04912 (2023) - [i71]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Feature Adaptation for Sparse Linear Regression. CoRR abs/2305.16892 (2023) - [i70]Zander Kelley, Shachar Lovett, Raghu Meka:
Explicit separations between randomized and deterministic Number-on-Forehead communication. CoRR abs/2308.12451 (2023) - [i69]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
User-Level Differential Privacy With Few Examples Per User. CoRR abs/2309.12500 (2023) - [i68]Yuanzhi Li, Raghu Meka, Rina Panigrahy, Kulin Shah:
Simple Mechanisms for Representing, Indexing and Manipulating Concepts. CoRR abs/2310.12143 (2023) - [i67]Amir Abboud, Nick Fischer, Zander Kelley, Shachar Lovett, Raghu Meka:
New Graph Decompositions and Combinatorial Boolean Matrix Multiplication Algorithms. CoRR abs/2311.09095 (2023) - [i66]Amir Abboud, Nick Fischer, Zander Kelley, Shachar Lovett, Raghu Meka:
New Graph Decompositions and Combinatorial Boolean Matrix Multiplication Algorithms. Electron. Colloquium Comput. Complex. TR23 (2023) - [i65]Zander Kelley, Shachar Lovett, Raghu Meka:
Explicit separations between randomized and deterministic Number-on-Forehead communication. Electron. Colloquium Comput. Complex. TR23 (2023) - 2022
- [j16]Pravesh K. Kothari, Raghu Meka, Prasad Raghavendra:
Approximating Rectangles by Juntas and Weakly Exponential Lower Bounds for LP Relaxations of CSPs. SIAM J. Comput. 51(2): 17-305 (2022) - [c54]Zander Kelley, Raghu Meka:
Random Restrictions and PRGs for PTFs in Gaussian Space. CCC 2022: 21:1-21:24 - [c53]Nikhil Bansal, Haotian Jiang, Raghu Meka, Sahil Singla, Makrand Sinha:
Smoothed Analysis of the Komlós Conjecture. ICALP 2022: 14:1-14:12 - [c52]Sitan Chen, Jerry Li, Yuanzhi Li, Raghu Meka:
Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs. ICLR 2022 - [c51]Nikhil Bansal, Haotian Jiang, Raghu Meka, Sahil Singla, Makrand Sinha:
Prefix Discrepancy, Smoothed Analysis, and Combinatorial Vector Balancing. ITCS 2022: 13:1-13:22 - [c50]Shachar Lovett, Raghu Meka, Ian Mertz, Toniann Pitassi, Jiapeng Zhang:
Lifting with Sunflowers. ITCS 2022: 104:1-104:24 - [c49]Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka:
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. NeurIPS 2022 - [c48]Nishanth Dikkala, Sankeerth Rao Karingula, Raghu Meka, Jelani Nelson, Rina Panigrahy, Xin Wang:
Sketching based Representations for Robust Image Classification with Provable Guarantees. NeurIPS 2022 - [c47]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Lower Bounds on Randomly Preconditioned Lasso via Robust Sparse Designs. NeurIPS 2022 - [i64]Sitan Chen, Jerry Li, Yuanzhi Li, Raghu Meka:
Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs. CoRR abs/2201.07206 (2022) - [i63]Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka:
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. CoRR abs/2202.05258 (2022) - [i62]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Distributional Hardness Against Preconditioned Lasso via Erasure-Robust Designs. CoRR abs/2203.02824 (2022) - [i61]Nikhil Bansal, Haotian Jiang, Raghu Meka, Sahil Singla, Makrand Sinha:
Smoothed Analysis of the Komlós Conjecture. CoRR abs/2204.11427 (2022) - [i60]Nikhil Bansal, Haotian Jiang, Raghu Meka:
Resolving Matrix Spencer Conjecture Up to Poly-logarithmic Rank. CoRR abs/2208.11286 (2022) - [i59]Peter Ivanov, Raghu Meka, Emanuele Viola:
Efficient resilient functions. Electron. Colloquium Comput. Complex. TR22 (2022) - 2021
- [c46]Dean Doron, Raghu Meka, Omer Reingold, Avishay Tal, Salil P. Vadhan:
Pseudorandom Generators for Read-Once Monotone Branching Programs. APPROX-RANDOM 2021: 58:1-58:21 - [c45]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
On the Power of Preconditioning in Sparse Linear Regression. FOCS 2021: 550-561 - [c44]Sitan Chen, Adam R. Klivans, Raghu Meka:
Learning Deep ReLU Networks Is Fixed-Parameter Tractable. FOCS 2021: 696-707 - [c43]Sitan Chen, Adam R. Klivans, Raghu Meka:
Efficiently Learning One Hidden Layer ReLU Networks From Queries. NeurIPS 2021: 24087-24098 - [c42]Nikhil Bansal, Haotian Jiang, Raghu Meka, Sahil Singla, Makrand Sinha:
Online Discrepancy Minimization for Stochastic Arrivals. SODA 2021: 2842-2861 - [i58]Zander Kelley, Raghu Meka:
Random restrictions and PRGs for PTFs in Gaussian Space. CoRR abs/2103.14134 (2021) - [i57]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
On the Power of Preconditioning in Sparse Linear Regression. CoRR abs/2106.09207 (2021) - [i56]Sitan Chen, Adam R. Klivans, Raghu Meka:
Efficiently Learning Any One Hidden Layer ReLU Network From Queries. CoRR abs/2111.04727 (2021) - [i55]Nikhil Bansal, Haotian Jiang, Raghu Meka, Sahil Singla, Makrand Sinha:
Prefix Discrepancy, Smoothed Analysis, and Combinatorial Vector Balancing. CoRR abs/2111.07049 (2021) - [i54]Dean Doron, Raghu Meka, Omer Reingold, Avishay Tal, Salil P. Vadhan:
Monotone Branching Programs: Pseudorandomness and Circuit Complexity. Electron. Colloquium Comput. Complex. TR21 (2021) - [i53]Zander Kelley, Raghu Meka:
Random restrictions and PRGs for PTFs in Gaussian Space. Electron. Colloquium Comput. Complex. TR21 (2021) - 2020
- [j15]Nikhil Bansal, Raghu Meka:
On the discrepancy of random low degree set systems. Random Struct. Algorithms 57(3): 695-705 (2020) - [c41]Sitan Chen, Raghu Meka:
Learning Polynomials in Few Relevant Dimensions. COLT 2020: 1161-1227 - [c40]Paxton Turner, Raghu Meka, Philippe Rigollet:
Balancing Gaussian vectors in high dimension. COLT 2020: 3455-3486 - [c39]Eshan Chattopadhyay, Jesse Goodman, Vipul Goyal, Ashutosh Kumar, Xin Li, Raghu Meka, David Zuckerman:
Extractors and Secret Sharing Against Bounded Collusion Protocols. FOCS 2020: 1226-1242 - [c38]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Ankur Moitra:
Learning Some Popular Gaussian Graphical Models without Condition Number Bounds. NeurIPS 2020 - [e1]Jaroslaw Byrka, Raghu Meka:
Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, APPROX/RANDOM 2020, August 17-19, 2020, Virtual Conference. LIPIcs 176, Schloss Dagstuhl - Leibniz-Zentrum für Informatik 2020, ISBN 978-3-95977-164-1 [contents] - [i52]Sitan Chen, Raghu Meka:
Learning Polynomials of Few Relevant Dimensions. CoRR abs/2004.13748 (2020) - [i51]Nikhil Bansal, Haotian Jiang, Raghu Meka, Sahil Singla, Makrand Sinha:
Online Discrepancy Minimization for Stochastic Arrivals. CoRR abs/2007.10622 (2020) - [i50]Sitan Chen, Adam R. Klivans, Raghu Meka:
Learning Deep ReLU Networks Is Fixed-Parameter Tractable. CoRR abs/2009.13512 (2020) - [i49]Ashutosh Kumar, Raghu Meka, David Zuckerman:
Bounded Collusion Protocols, Cylinder-Intersection Extractors and Leakage-Resilient Secret Sharing. Electron. Colloquium Comput. Complex. TR20 (2020) - [i48]Shachar Lovett, Raghu Meka, Jiapeng Zhang:
Improved lifting theorems via robust sunflowers. Electron. Colloquium Comput. Complex. TR20 (2020) - [i47]Ashutosh Kumar, Raghu Meka, David Zuckerman:
Bounded Collusion Protocols, Cylinder-Intersection Extractors and Leakage-Resilient Secret Sharing. IACR Cryptol. ePrint Arch. 2020: 473 (2020)
2010 – 2019
- 2019
- [j14]Russell Impagliazzo, Raghu Meka, David Zuckerman:
Pseudorandomness from Shrinkage. J. ACM 66(2): 11:1-11:16 (2019) - [c37]Ashutosh Kumar, Raghu Meka, Amit Sahai:
Leakage-Resilient Secret Sharing Against Colluding Parties. FOCS 2019: 636-660 - [c36]Nikhil Bansal, Raghu Meka:
On the discrepancy of random low degree set systems. SODA 2019: 2557-2564 - [c35]Raghu Meka, Omer Reingold, Avishay Tal:
Pseudorandom generators for width-3 branching programs. STOC 2019: 626-637 - [i46]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Ankur Moitra:
Learning Some Popular Gaussian Graphical Models without Condition Number Bounds. CoRR abs/1905.01282 (2019) - [i45]Raghu Meka, Philippe Rigollet, Paxton Turner:
Balancing Gaussian vectors in high dimension. CoRR abs/1910.13972 (2019) - [i44]Arnab Bhattacharyya, Philips George John, Suprovat Ghoshal, Raghu Meka:
Average Bias and Polynomial Sources. Electron. Colloquium Comput. Complex. TR19 (2019) - 2018
- [j13]Parikshit Gopalan, Daniel M. Kane, Raghu Meka:
Pseudorandomness via the Discrete Fourier Transform. SIAM J. Comput. 47(6): 2451-2487 (2018) - [c34]Adam R. Klivans, Pravesh K. Kothari, Raghu Meka:
Efficient Algorithms for Outlier-Robust Regression. COLT 2018: 1420-1430 - [c33]Surbhi Goel, Adam R. Klivans, Raghu Meka:
Learning One Convolutional Layer with Overlapping Patches. ICML 2018: 1778-1786 - [i43]Surbhi Goel, Adam R. Klivans, Raghu Meka:
Learning One Convolutional Layer with Overlapping Patches. CoRR abs/1802.02547 (2018) - [i42]Adam R. Klivans, Pravesh K. Kothari, Raghu Meka:
Efficient Algorithms for Outlier-Robust Regression. CoRR abs/1803.03241 (2018) - [i41]Raghu Meka, Omer Reingold, Avishay Tal:
Pseudorandom Generators for Width-3 Branching Programs. CoRR abs/1806.04256 (2018) - [i40]Nikhil Bansal, Raghu Meka:
On the discrepancy of random low degree set systems. CoRR abs/1810.03374 (2018) - [i39]Ashutosh Kumar, Raghu Meka, Amit Sahai:
Leakage-Resilient Secret Sharing. Electron. Colloquium Comput. Complex. TR18 (2018) - [i38]Raghu Meka, Omer Reingold, Avishay Tal:
Pseudorandom Generators for Width-3 Branching Programs. Electron. Colloquium Comput. Complex. TR18 (2018) - [i37]Ashutosh Kumar, Raghu Meka, Amit Sahai:
Leakage-Resilient Secret Sharing. IACR Cryptol. ePrint Arch. 2018: 1138 (2018) - 2017
- [j12]Clément L. Canonne, Venkatesan Guruswami, Raghu Meka, Madhu Sudan:
Communication With Imperfectly Shared Randomness. IEEE Trans. Inf. Theory 63(10): 6799-6818 (2017) - [c32]Adam R. Klivans, Raghu Meka:
Learning Graphical Models Using Multiplicative Weights. FOCS 2017: 343-354 - [c31]Raghu Meka:
Explicit Resilient Functions Matching Ajtai-Linial. SODA 2017: 1132-1148 - [c30]Pravesh K. Kothari, Raghu Meka, Prasad Raghavendra:
Approximating rectangles by juntas and weakly-exponential lower bounds for LP relaxations of CSPs. STOC 2017: 590-603 - [i36]Adam R. Klivans, Raghu Meka:
Learning Graphical Models Using Multiplicative Weights. CoRR abs/1706.06274 (2017) - 2016
- [j11]Moritz Hardt, Yuval Ishai, Raghu Meka, Virginia Vassilevska Williams:
Special Section on the Fifty-Fourth Annual IEEE Symposium on Foundations of Computer Science (FOCS 2013). SIAM J. Comput. 45(3): 881 (2016) - [j10]Mika Göös, Shachar Lovett, Raghu Meka, Thomas Watson, David Zuckerman:
Rectangles Are Nonnegative Juntas. SIAM J. Comput. 45(5): 1835-1869 (2016) - [j9]Raghu Meka, Oanh Nguyen, Van Vu:
Anti-concentration for Polynomials of Independent Random Variables. Theory Comput. 12(1): 1-17 (2016) - [i35]Pravesh Kothari, Raghu Meka, Prasad Raghavendra:
Approximating Rectangles by Juntas and Weakly-Exponential Lower Bounds for LP Relaxations of CSPs. CoRR abs/1610.02704 (2016) - 2015
- [j8]Boaz Barak, Parikshit Gopalan, Johan Håstad, Raghu Meka, Prasad Raghavendra, David Steurer:
Making the Long Code Shorter. SIAM J. Comput. 44(5): 1287-1324 (2015) - [j7]Shachar Lovett, Raghu Meka:
Constructive Discrepancy Minimization by Walking on the Edges. SIAM J. Comput. 44(5): 1573-1582 (2015) - [c29]Parikshit Gopalan, Daniel M. Kane, Raghu Meka:
Pseudorandomness via the Discrete Fourier Transform. FOCS 2015: 903-922 - [c28]Clément Louis Canonne, Venkatesan Guruswami, Raghu Meka, Madhu Sudan:
Communication with Imperfectly Shared Randomness. ITCS 2015: 257-262 - [c27]Raghu Meka, Aaron Potechin, Avi Wigderson:
Sum-of-squares Lower Bounds for Planted Clique. STOC 2015: 87-96 - [c26]Pravesh K. Kothari, Raghu Meka:
Almost Optimal Pseudorandom Generators for Spherical Caps: Extended Abstract. STOC 2015: 247-256 - [c25]Mika Göös, Shachar Lovett, Raghu Meka, Thomas Watson, David Zuckerman:
Rectangles Are Nonnegative Juntas. STOC 2015: 257-266 - [i34]Raghu Meka, Aaron Potechin, Avi Wigderson:
Sum-of-squares lower bounds for planted clique. CoRR abs/1503.06447 (2015) - [i33]Parikshit Gopalan, Daniel M. Kane, Raghu Meka:
Pseudorandomness via the discrete Fourier transform. CoRR abs/1506.04350 (2015) - [i32]Raghu Meka:
Explicit resilient functions matching Ajtai-Linial. CoRR abs/1509.00092 (2015) - [i31]Raghu Meka:
Explicit resilient functions matching Ajtai-Linial. Electron. Colloquium Comput. Complex. TR15 (2015) - 2014
- [j6]Prahladh Harsha, Adam R. Klivans, Raghu Meka:
Bounding the Sensitivity of Polynomial Threshold Functions. Theory Comput. 10: 1-26 (2014) - [c24]Raghu Meka, Omer Reingold, Yuan Zhou:
Deterministic Coupon Collection and Better Strong Dispersers. APPROX-RANDOM 2014: 872-884 - [c23]Elad Hazan, Zohar Shay Karnin, Raghu Meka:
Volumetric Spanners: an Efficient Exploration Basis for Learning. COLT 2014: 408-422 - [c22]Moritz Hardt, Raghu Meka, Prasad Raghavendra, Benjamin Weitz:
Computational Limits for Matrix Completion. COLT 2014: 703-725 - [c21]Raghu Meka, Omer Reingold, Guy N. Rothblum, Ron D. Rothblum:
Fast Pseudorandomness for Independence and Load Balancing - (Extended Abstract). ICALP (1) 2014: 859-870 - [i30]Moritz Hardt, Raghu Meka, Prasad Raghavendra, Benjamin Weitz:
Computational Limits for Matrix Completion. CoRR abs/1402.2331 (2014) - [i29]Clément L. Canonne, Venkatesan Guruswami, Raghu Meka, Madhu Sudan:
Communication with Imperfectly Shared Randomness. CoRR abs/1411.3603 (2014) - [i28]Parikshit Gopalan, Daniel M. Kane, Raghu Meka:
Pseudorandomness for concentration bounds and signed majorities. CoRR abs/1411.4584 (2014) - [i27]Pravesh Kothari, Raghu Meka:
Almost Optimal Pseudorandom Generators for Spherical Caps. CoRR abs/1411.6299 (2014) - [i26]Clément L. Canonne, Venkatesan Guruswami, Raghu Meka, Madhu Sudan:
Communication with Imperfectly Shared Randomness. Electron. Colloquium Comput. Complex. TR14 (2014) - [i25]Mika Göös, Shachar Lovett, Raghu Meka, Thomas Watson, David Zuckerman:
Rectangles Are Nonnegative Juntas. Electron. Colloquium Comput. Complex. TR14 (2014) - 2013
- [j5]Parikshit Gopalan, Raghu Meka, Omer Reingold:
DNF sparsification and a faster deterministic counting algorithm. Comput. Complex. 22(2): 275-310 (2013) - [j4]Parikshit Gopalan, Raghu Meka, Omer Reingold, David Zuckerman:
Pseudorandom Generators for Combinatorial Shapes. SIAM J. Comput. 42(3): 1051-1076 (2013) - [j3]Raghu Meka, David Zuckerman:
Pseudorandom Generators for Polynomial Threshold Functions. SIAM J. Comput. 42(3): 1275-1301 (2013) - [c20]Daniel M. Kane, Adam R. Klivans, Raghu Meka:
Learning Halfspaces Under Log-Concave Densities: Polynomial Approximations and Moment Matching. COLT 2013: 522-545 - [c19]Daniel M. Kane, Raghu Meka:
A PRG for lipschitz functions of polynomials with applications to sparsest cut. STOC 2013: 1-10 - [i24]Adam R. Klivans, Raghu Meka:
Moment-Matching Polynomials. CoRR abs/1301.0820 (2013) - [i23]Elad Hazan, Zohar Shay Karnin, Raghu Meka:
Volumetric Spanners and their Applications to Machine Learning. CoRR abs/1312.6214 (2013) - [i22]Adam R. Klivans, Raghu Meka:
Moment-Matching Polynomials. Electron. Colloquium Comput. Complex. TR13 (2013) - [i21]Raghu Meka, Avi Wigderson:
Association schemes, non-commutative polynomial concentration, and sum-of-squares lower bounds for planted clique. Electron. Colloquium Comput. Complex. TR13 (2013) - 2012
- [j2]Prahladh Harsha, Adam R. Klivans, Raghu Meka:
An invariance principle for polytopes. J. ACM 59(6): 29:1-29:25 (2012) - [c18]Parikshit Gopalan, Raghu Meka, Omer Reingold:
DNF Sparsification and a Faster Deterministic Counting Algorithm. CCC 2012: 126-135 - [c17]Shachar Lovett, Raghu Meka:
Constructive Discrepancy Minimization by Walking on the Edges. FOCS 2012: 61-67 - [c16]Russell Impagliazzo, Raghu Meka, David Zuckerman:
Pseudorandomness from Shrinkage. FOCS 2012: 111-119 - [c15]Parikshit Gopalan, Raghu Meka, Omer Reingold, Luca Trevisan, Salil P. Vadhan:
Better Pseudorandom Generators from Milder Pseudorandom Restrictions. FOCS 2012: 120-129 - [c14]Raghu Meka:
A PTAS for Computing the Supremum of Gaussian Processes. FOCS 2012: 217-222 - [c13]Boaz Barak, Parikshit Gopalan, Johan Håstad, Raghu Meka, Prasad Raghavendra, David Steurer:
Making the Long Code Shorter. FOCS 2012: 370-379 - [c12]Parikshit Gopalan, Adam R. Klivans, Raghu Meka:
Learning Functions of Halfspaces using Prefix Covers. COLT 2012: 15.1-15.10 - [i20]Raghu Meka:
A PTAS for Computing the Supremum of Gaussian Processes. CoRR abs/1202.4970 (2012) - [i19]Shachar Lovett, Raghu Meka:
Constructive Discrepancy Minimization by Walking on The Edges. CoRR abs/1203.5747 (2012) - [i18]Parikshit Gopalan, Raghu Meka, Omer Reingold:
DNF Sparsification and a Faster Deterministic Counting Algorithm. CoRR abs/1205.3534 (2012) - [i17]Parikshit Gopalan, Raghu Meka, Omer Reingold, Luca Trevisan, Salil P. Vadhan:
Better Pseudorandom Generators from Milder Pseudorandom Restrictions. CoRR abs/1210.0049 (2012) - [i16]Daniel M. Kane, Raghu Meka:
A PRG for Lipschitz Functions of Polynomials with Applications to Sparsest Cut. CoRR abs/1211.1109 (2012) - [i15]Parikshit Gopalan, Raghu Meka, Omer Reingold:
DNF Sparsification and a Faster Deterministic Counting. Electron. Colloquium Comput. Complex. TR12 (2012) - [i14]Parikshit Gopalan, Raghu Meka, Omer Reingold, Luca Trevisan, Salil P. Vadhan:
Better pseudorandom generators from milder pseudorandom restrictions. Electron. Colloquium Comput. Complex. TR12 (2012) - [i13]Russell Impagliazzo, Raghu Meka, David Zuckerman:
Pseudorandomness from Shrinkage. Electron. Colloquium Comput. Complex. TR12 (2012) - 2011
- [c11]Daniel Kane, Raghu Meka, Jelani Nelson:
Almost Optimal Explicit Johnson-Lindenstrauss Families. APPROX-RANDOM 2011: 628-639 - [c10]Parikshit Gopalan, Adam R. Klivans, Raghu Meka, Daniel Stefankovic, Santosh S. Vempala, Eric Vigoda:
An FPTAS for #Knapsack and Related Counting Problems. FOCS 2011: 817-826 - [c9]Parikshit Gopalan, Raghu Meka, Omer Reingold, David Zuckerman:
Pseudorandom generators for combinatorial shapes. STOC 2011: 253-262 - [i12]Boaz Barak, Parikshit Gopalan, Johan Håstad, Raghu Meka, Prasad Raghavendra, David Steurer:
Making the long code shorter, with applications to the Unique Games Conjecture. CoRR abs/1111.0405 (2011) - [i11]Boaz Barak, Parikshit Gopalan, Johan Håstad, Raghu Meka, Prasad Raghavendra, David Steurer:
Making the long code shorter, with applications to the Unique Games Conjecture. Electron. Colloquium Comput. Complex. TR11 (2011) - 2010
- [c8]Prateek Jain, Raghu Meka, Inderjit S. Dhillon:
Guaranteed Rank Minimization via Singular Value Projection. NIPS 2010: 937-945 - [c7]Raghu Meka, David Zuckerman:
Pseudorandom generators for polynomial threshold functions. STOC 2010: 427-436 - [c6]Ilias Diakonikolas, Prahladh Harsha, Adam R. Klivans, Raghu Meka, Prasad Raghavendra, Rocco A. Servedio, Li-Yang Tan:
Bounding the average sensitivity and noise sensitivity of polynomial threshold functions. STOC 2010: 533-542 - [c5]Prahladh Harsha, Adam R. Klivans, Raghu Meka:
An invariance principle for polytopes. STOC 2010: 543-552 - [i10]Parikshit Gopalan, Adam R. Klivans, Raghu Meka:
Polynomial-Time Approximation Schemes for Knapsack and Related Counting Problems using Branching Programs. CoRR abs/1008.3187 (2010) - [i9]Raghu Meka:
Almost Optimal Explicit Johnson-Lindenstrauss Transformations. CoRR abs/1011.6397 (2010) - [i8]Parikshit Gopalan, Adam R. Klivans, Raghu Meka:
Polynomial-Time Approximation Schemes for Knapsack and Related Counting Problems using Branching Programs. Electron. Colloquium Comput. Complex. TR10 (2010) - [i7]Parikshit Gopalan, Raghu Meka, Omer Reingold, David Zuckerman:
Pseudorandom Generators for Combinatorial Shapes. Electron. Colloquium Comput. Complex. TR10 (2010) - [i6]Daniel Kane, Raghu Meka, Jelani Nelson:
Almost Optimal Explicit Johnson-Lindenstrauss Transformations. Electron. Colloquium Comput. Complex. TR10 (2010)
2000 – 2009
- 2009
- [c4]Raghu Meka, David Zuckerman:
Small-Bias Spaces for Group Products. APPROX-RANDOM 2009: 658-672 - [c3]Raghu Meka, Prateek Jain, Inderjit S. Dhillon:
Matrix Completion from Power-Law Distributed Samples. NIPS 2009: 1258-1266 - [i5]Prahladh Harsha, Adam R. Klivans, Raghu Meka:
Bounding the Sensitivity of Polynomial Threshold Functions. CoRR abs/0909.5175 (2009) - [i4]Raghu Meka, Prateek Jain, Inderjit S. Dhillon:
Guaranteed Rank Minimization via Singular Value Projection. CoRR abs/0909.5457 (2009) - [i3]Raghu Meka, David Zuckerman:
Pseudorandom Generators for Polynomial Threshold Functions. CoRR abs/0910.4122 (2009) - [i2]Prahladh Harsha, Adam R. Klivans, Raghu Meka:
An Invariance Principle for Polytopes. CoRR abs/0912.4884 (2009) - [i1]Prahladh Harsha, Adam R. Klivans, Raghu Meka:
An Invariance Principle for Polytopes. Electron. Colloquium Comput. Complex. TR09 (2009) - 2008
- [j1]Prateek Jain, Raghu Meka, Inderjit S. Dhillon:
Simultaneous Unsupervised Learning of Disparate Clusterings. Stat. Anal. Data Min. 1(3): 195-210 (2008) - [c2]Raghu Meka, Prateek Jain, Constantine Caramanis, Inderjit S. Dhillon:
Rank minimization via online learning. ICML 2008: 656-663 - [c1]Prateek Jain, Raghu Meka, Inderjit S. Dhillon:
Simultaneous Unsupervised Learning of Disparate Clusterings. SDM 2008: 858-869
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-01-09 12:57 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
