Autonomous Robot Navigation: Path Planning on a Detail-Preserving Reduced-Complexity Representation of 3D Point Clouds. - Rohit Sant; Ninad Kulkarni; Ainesh Bakshi; Salil Kapur; Kratarth Goel (ICVS, 2013)

Non Dominated Sorting Genetic Algorithm for Chance Constrained Supplier Selection Model with Volume Discounts. - Remica Aggarwal; Ainesh Bakshi (ACIIDS (2), 2014)

A novel feature selection and extraction technique for classification. - Kratarth Goel; Raunaq Vohra; Ainesh Bakshi (SMC, 2014)

File Systems Fated for Senescence? Nonsense, Says Science! - Alexander Conway 001; Ainesh Bakshi; Yizheng Jiao; William Jannen; Yang Zhan 001; Jun Yuan 006; Michael A. Bender; Rob Johnson 001; Bradley C. Kuszmaul; Donald E. Porter; Martin Farach-Colton (FAST, 2017)

How to Fragment Your File System. - Alex Conway 001; Ainesh Bakshi; Yizheng Jiao; Yang Zhan 001; Michael A. Bender; William Jannen; Rob Johnson 001; Bradley C. Kuszmaul; Donald E. Porter; Jun Yuan 006; Martin Farach-Colton (login Usenix Mag., 2017)

Sublinear Time Low-Rank Approximation of Distance Matrices. - Ainesh Bakshi; David P. Woodruff (NeurIPS, 2018)

Learning Two Layer Rectified Neural Networks in Polynomial Time. - Ainesh Bakshi; Rajesh Jayaram; David P. Woodruff (COLT, 2019)

Robust Communication-Optimal Distributed Clustering Algorithms. - Pranjal Awasthi; Ainesh Bakshi; Maria-Florina Balcan; Colin White; David P. Woodruff (ICALP, 2019)

Weighted Maximum Independent Set of Geometric Objects in Turnstile Streams. - Ainesh Bakshi; Nadiia Chepurko; David P. Woodruff (APPROX-RANDOM, 2020)

Testing Positive Semi-Definiteness via Random Submatrices. - Ainesh Bakshi; Nadiia Chepurko; Rajesh Jayaram (FOCS, 2020)

Robust and Sample Optimal Algorithms for PSD Low Rank Approximation. - Ainesh Bakshi; Nadiia Chepurko; David P. Woodruff (FOCS, 2020)

Outlier-Robust Clustering of Gaussians and Other Non-Spherical Mixtures. - Ainesh Bakshi; Ilias Diakonikolas; Samuel B. Hopkins 001; Daniel Kane 001; Sushrut Karmalkar; Pravesh K. Kothari (FOCS, 2020)

List-Decodable Subspace Recovery via Sum-of-Squares. - Ainesh Bakshi; Pravesh Kothari (CoRR, 2020)

Outlier-Robust Clustering of Non-Spherical Mixtures. - Ainesh Bakshi; Pravesh Kothari (CoRR, 2020)

Robustly Learning Mixtures of k Arbitrary Gaussians. - Ainesh Bakshi; Ilias Diakonikolas; He Jia; Daniel M. Kane; Pravesh K. Kothari; Santosh S. Vempala (CoRR, 2020)

Learning a Latent Simplex in Input Sparsity Time. - Ainesh Bakshi; Chiranjib Bhattacharyya; Ravi Kannan; David P. Woodruff; Samson Zhou (ICLR, 2021)

List-Decodable Subspace Recovery: Dimension Independent Error in Polynomial Time. - Ainesh Bakshi; Pravesh K. Kothari (SODA, 2021)

Robust linear regression: optimal rates in polynomial time. - Ainesh Bakshi; Adarsh Prasad (STOC, 2021)

Low-rank approximation with 1/ε1/3 matrix-vector products. - Ainesh Bakshi; Kenneth L. Clarkson; David P. Woodruff (STOC, 2022)

Robustly learning mixtures of k arbitrary Gaussians. - Ainesh Bakshi; Ilias Diakonikolas; He Jia; Daniel M. Kane; Pravesh K. Kothari; Santosh S. Vempala (STOC, 2022)

Low-Rank Approximation with 1/ε1/3 Matrix-Vector Products. - Ainesh Bakshi; Kenneth L. Clarkson; David P. Woodruff (CoRR, 2022)

Sub-quadratic Algorithms for Kernel Matrices via Kernel Density Estimation. - Ainesh Bakshi; Piotr Indyk; Praneeth Kacham; Sandeep Silwal; Samson Zhou (CoRR, 2022)

Krylov Methods are (nearly) Optimal for Low-Rank Approximation. - Ainesh Bakshi; Shyam Narayanan (FOCS, 2023)

Subquadratic Algorithms for Kernel Matrices via Kernel Density Estimation. - Ainesh Bakshi; Piotr Indyk; Praneeth Kacham; Sandeep Silwal; Samson Zhou (ICLR, 2023)

Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems. - Ainesh Bakshi; Allen Liu; Ankur Moitra; Morris Yau (ICML, 2023)

Near-Linear Time Algorithm for the Chamfer Distance. - Ainesh Bakshi; Piotr Indyk; Rajesh Jayaram; Sandeep Silwal; Erik Waingarten (NeurIPS, 2023)

A New Approach to Learning Linear Dynamical Systems. - Ainesh Bakshi; Allen Liu; Ankur Moitra; Morris Yau (STOC, 2023)

A quasi-polynomial time algorithm for Multi-Dimensional Scaling via LP hierarchies. - Ainesh Bakshi; Vincent Cohen-Addad; Samuel B. Hopkins 001; Rajesh Jayaram; Silvio Lattanzi (CoRR, 2023)

Efficient Certificates of Anti-Concentration Beyond Gaussians. - Ainesh Bakshi; Pravesh K. Kothari; Goutham Rajendran; Madhur Tulsiani; Aravindan Vijayaraghavan (FOCS, 2024)

High-Temperature Gibbs States are Unentangled and Efficiently Preparable. - Ainesh Bakshi; Allen Liu; Ankur Moitra; Ewin Tang (FOCS, 2024)

Structure Learning of Hamiltonians from Real-Time Evolution. - Ainesh Bakshi; Allen Liu; Ankur Moitra; Ewin Tang (FOCS, 2024)

An Improved Classical Singular Value Transformation for Quantum Machine Learning. - Ainesh Bakshi; Ewin Tang (SODA, 2024)

Learning Quantum Hamiltonians at Any Temperature in Polynomial Time. - Ainesh Bakshi; Allen Liu; Ankur Moitra; Ewin Tang (STOC, 2024)

File System Aging. - Alex Conway 001; Ainesh Bakshi; Arghya Bhattacharya; Rory Bennett; Yizheng Jiao; Eric Knorr; Yang Zhan 001; Michael A. Bender; William Jannen; Rob Johnson 001; Bradley C. Kuszmaul; Donald E. Porter; et al. (CoRR, 2024)

Metric Embeddings Beyond Bi-Lipschitz Distortion via Sherali-Adams. - Ainesh Bakshi; Vincent Cohen-Addad; Rajesh Jayaram; Sam Hopkins 001; Silvio Lattanzi (COLT, 2025)

Sample-Optimal Private Regression in Polynomial Time. - Prashanti Anderson; Ainesh Bakshi; Mahbod Majid; Stefan Tiegel (STOC, 2025)

Learning the Closest Product State. - Ainesh Bakshi; John Bostanci; William Kretschmer; Zeph Landau; Jerry Li 001; Allen Liu; Ryan O'Donnell; Ewin Tang (STOC, 2025)

Additive Approximation Schemes for Low-Dimensional Embeddings. - Prashanti Anderson; Ainesh Bakshi; Samuel B. Hopkins 001 (CoRR, 2025)

A Dobrushin condition for quantum Markov chains: Rapid mixing and conditional mutual information at high temperature. - Ainesh Bakshi; Allen Liu; Ankur Moitra; Ewin Tang (CoRR, 2025)