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Ainesh Bakshi
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2020 – today
- 2024
- [c27]Ainesh Bakshi, Pravesh K. Kothari, Goutham Rajendran, Madhur Tulsiani, Aravindan Vijayaraghavan:
Efficient Certificates of Anti-Concentration Beyond Gaussians. FOCS 2024: 970-987 - [c26]Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang:
High-Temperature Gibbs States are Unentangled and Efficiently Preparable. FOCS 2024: 1027-1036 - [c25]Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang:
Structure Learning of Hamiltonians from Real-Time Evolution. FOCS 2024: 1037-1050 - [c24]Ainesh Bakshi, Ewin Tang:
An Improved Classical Singular Value Transformation for Quantum Machine Learning. SODA 2024: 2398-2453 - [c23]Ainesh Bakshi
, Allen Liu
Learning Quantum Hamiltonians at Any Temperature in Polynomial Time. STOC 2024: 1470-1477 - [i24]Alex Conway, Ainesh Bakshi, Arghya Bhattacharya, Rory Bennett, Yizheng Jiao, Eric Knorr, Yang Zhan, Michael A. Bender, William Jannen, Rob Johnson, Bradley C. Kuszmaul, Donald E. Porter, Jun Yuan, Martin Farach-Colton:
File System Aging. CoRR abs/2401.08858 (2024) - [i23]Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang:
High-Temperature Gibbs States are Unentangled and Efficiently Preparable. CoRR abs/2403.16850 (2024) - [i22]Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang:
Structure learning of Hamiltonians from real-time evolution. CoRR abs/2405.00082 (2024) - [i21]Ainesh Bakshi, Pravesh Kothari, Goutham Rajendran, Madhur Tulsiani, Aravindan Vijayaraghavan:
Efficient Certificates of Anti-Concentration Beyond Gaussians. CoRR abs/2405.15084 (2024) - 2023
- [c22]Ainesh Bakshi, Shyam Narayanan:
Krylov Methods are (nearly) Optimal for Low-Rank Approximation. FOCS 2023: 2093-2101 - [c21]Ainesh Bakshi, Piotr Indyk, Praneeth Kacham, Sandeep Silwal, Samson Zhou:
Subquadratic Algorithms for Kernel Matrices via Kernel Density Estimation. ICLR 2023 - [c20]Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau:
Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems. ICML 2023: 1549-1563 - [c19]Ainesh Bakshi, Piotr Indyk, Rajesh Jayaram, Sandeep Silwal, Erik Waingarten:
Near-Linear Time Algorithm for the Chamfer Distance. NeurIPS 2023 - [c18]Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau:
A New Approach to Learning Linear Dynamical Systems. STOC 2023: 335-348 - [i20]Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau:
A New Approach to Learning Linear Dynamical Systems. CoRR abs/2301.09519 (2023) - [i19]Ainesh Bakshi, Ewin Tang:
An Improved Classical Singular Value Transformation for Quantum Machine Learning. CoRR abs/2303.01492 (2023) - [i18]Ainesh Bakshi, Shyam Narayanan:
Krylov Methods are (nearly) Optimal for Low-Rank Approximation. CoRR abs/2304.03191 (2023) - [i17]Ainesh Bakshi, Piotr Indyk, Rajesh Jayaram, Sandeep Silwal, Erik Waingarten:
A Near-Linear Time Algorithm for the Chamfer Distance. CoRR abs/2307.03043 (2023) - [i16]Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau:
Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems. CoRR abs/2307.06538 (2023) - [i15]Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang:
Learning quantum Hamiltonians at any temperature in polynomial time. CoRR abs/2310.02243 (2023) - [i14]Ainesh Bakshi, Vincent Cohen-Addad, Samuel B. Hopkins, Rajesh Jayaram, Silvio Lattanzi:
A quasi-polynomial time algorithm for Multi-Dimensional Scaling via LP hierarchies. CoRR abs/2311.17840 (2023) - 2022
- [c17]Ainesh Bakshi, Kenneth L. Clarkson, David P. Woodruff:
Low-rank approximation with 1/ε1/3 matrix-vector products. STOC 2022: 1130-1143 - [c16]Ainesh Bakshi, Ilias Diakonikolas, He Jia, Daniel M. Kane, Pravesh K. Kothari, Santosh S. Vempala:
Robustly learning mixtures of k arbitrary Gaussians. STOC 2022: 1234-1247 - [i13]Ainesh Bakshi, Kenneth L. Clarkson, David P. Woodruff:
Low-Rank Approximation with 1/ε1/3 Matrix-Vector Products. CoRR abs/2202.05120 (2022) - [i12]Ainesh Bakshi, Piotr Indyk, Praneeth Kacham, Sandeep Silwal, Samson Zhou:
Sub-quadratic Algorithms for Kernel Matrices via Kernel Density Estimation. CoRR abs/2212.00642 (2022) - 2021
- [c15]Ainesh Bakshi, Chiranjib Bhattacharyya, Ravi Kannan, David P. Woodruff, Samson Zhou:
Learning a Latent Simplex in Input Sparsity Time. ICLR 2021 - [c14]Ainesh Bakshi, Pravesh K. Kothari:
List-Decodable Subspace Recovery: Dimension Independent Error in Polynomial Time. SODA 2021: 1279-1297 - [c13]Ainesh Bakshi, Adarsh Prasad:
Robust linear regression: optimal rates in polynomial time. STOC 2021: 102-115 - [i11]Ainesh Bakshi, Chiranjib Bhattacharyya, Ravi Kannan, David P. Woodruff, Samson Zhou:
Learning a Latent Simplex in Input-Sparsity Time. CoRR abs/2105.08005 (2021) - 2020
- [c12]Ainesh Bakshi, Nadiia Chepurko, David P. Woodruff:
Weighted Maximum Independent Set of Geometric Objects in Turnstile Streams. APPROX-RANDOM 2020: 64:1-64:22 - [c11]Ainesh Bakshi, Ilias Diakonikolas, Samuel B. Hopkins, Daniel Kane, Sushrut Karmalkar, Pravesh K. Kothari:
Outlier-Robust Clustering of Gaussians and Other Non-Spherical Mixtures. FOCS 2020: 149-159 - [c10]Ainesh Bakshi, Nadiia Chepurko, David P. Woodruff:
Robust and Sample Optimal Algorithms for PSD Low Rank Approximation. FOCS 2020: 506-516 - [c9]Ainesh Bakshi, Nadiia Chepurko, Rajesh Jayaram:
Testing Positive Semi-Definiteness via Random Submatrices. FOCS 2020: 1191-1202 - [i10]Ainesh Bakshi, Pravesh Kothari:
List-Decodable Subspace Recovery via Sum-of-Squares. CoRR abs/2002.05139 (2020) - [i9]Ainesh Bakshi, Pravesh Kothari:
Outlier-Robust Clustering of Non-Spherical Mixtures. CoRR abs/2005.02970 (2020) - [i8]Ainesh Bakshi, Nadiia Chepurko, Rajesh Jayaram:
Testing Positive Semi-Definiteness via Random Submatrices. CoRR abs/2005.06441 (2020) - [i7]Ainesh Bakshi, Adarsh Prasad:
Robust Linear Regression: Optimal Rates in Polynomial Time. CoRR abs/2007.01394 (2020) - [i6]Ainesh Bakshi, Ilias Diakonikolas, He Jia, Daniel M. Kane, Pravesh K. Kothari, Santosh S. Vempala:
Robustly Learning Mixtures of k Arbitrary Gaussians. CoRR abs/2012.02119 (2020)
2010 – 2019
- 2019
- [c8]Ainesh Bakshi, Rajesh Jayaram, David P. Woodruff:
Learning Two Layer Rectified Neural Networks in Polynomial Time. COLT 2019: 195-268 - [c7]Pranjal Awasthi, Ainesh Bakshi, Maria-Florina Balcan, Colin White, David P. Woodruff:
Robust Communication-Optimal Distributed Clustering Algorithms. ICALP 2019: 18:1-18:16 - [i5]Ainesh Bakshi, Nadiia Chepurko, David P. Woodruff:
Weighted Maximum Independent Set of Geometric Objects in Turnstile Streams. CoRR abs/1902.10328 (2019) - [i4]Ainesh Bakshi, Nadiia Chepurko, David P. Woodruff:
Robust and Sample Optimal Algorithms for PSD Low-Rank Approximation. CoRR abs/1912.04177 (2019) - 2018
- [c6]Ainesh Bakshi, David P. Woodruff:
Sublinear Time Low-Rank Approximation of Distance Matrices. NeurIPS 2018: 3786-3796 - [i3]Ainesh Bakshi, David P. Woodruff:
Sublinear Time Low-Rank Approximation of Distance Matrices. CoRR abs/1809.06986 (2018) - [i2]Ainesh Bakshi, Rajesh Jayaram, David P. Woodruff:
Learning Two Layer Rectified Neural Networks in Polynomial Time. CoRR abs/1811.01885 (2018) - 2017
- [j1]Alex Conway, Ainesh Bakshi, Yizheng Jiao, Yang Zhan, Michael A. Bender, William Jannen, Rob Johnson, Bradley C. Kuszmaul, Donald E. Porter, Jun Yuan, Martin Farach-Colton:
How to Fragment Your File System. login Usenix Mag. 42(2) (2017) - [c5]Alexander Conway, Ainesh Bakshi, Yizheng Jiao, William Jannen, Yang Zhan, Jun Yuan, Michael A. Bender, Rob Johnson, Bradley C. Kuszmaul, Donald E. Porter, Martin Farach-Colton:
File Systems Fated for Senescence? Nonsense, Says Science! FAST 2017: 45-58 - 2014
- [c4]Remica Aggarwal, Ainesh Bakshi:
Non Dominated Sorting Genetic Algorithm for Chance Constrained Supplier Selection Model with Volume Discounts. ACIIDS (2) 2014: 465-474 - [c3]Kratarth Goel, Raunaq Vohra, Ainesh Bakshi:
A Novel Feature Selection and Extraction Technique for Classification. ICFHR 2014: 104-109 - [c2]Kratarth Goel, Raunaq Vohra, Ainesh Bakshi:
A novel feature selection and extraction technique for classification. SMC 2014: 4033-4034 - [i1]Kratarth Goel, Raunaq Vohra, Ainesh Bakshi:
A Novel Feature Selection and Extraction Technique for Classification. CoRR abs/1412.7934 (2014) - 2013
- [c1]Rohit Sant, Ninad Kulkarni, Ainesh Bakshi, Salil Kapur, Kratarth Goel:
Autonomous Robot Navigation: Path Planning on a Detail-Preserving Reduced-Complexity Representation of 3D Point Clouds. ICVS 2013: 173-182
Coauthor Index
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