| 2026 | SODA | Additive Approximation Schemes for Low-Dimensional Embeddings. | Prashanti Anderson, Ainesh Bakshi, Samuel B. Hopkins |
| 2026 | STOC | A Dobrushin Condition for Quantum Markov Chains: Rapid Mixing and Conditional Mutual Information at High Temperature. | Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang |
| 2025 | COLT | Metric Embeddings Beyond Bi-Lipschitz Distortion via Sherali-Adams. | Ainesh Bakshi, Vincent Cohen-Addad, Rajesh Jayaram, Sam Hopkins, Silvio Lattanzi |
| 2025 | STOC | Sample-Optimal Private Regression in Polynomial Time. | Prashanti Anderson, Ainesh Bakshi, Mahbod Majid, Stefan Tiegel |
| 2025 | STOC | Learning the Closest Product State. | Ainesh Bakshi, John Bostanci, William Kretschmer, Zeph Landau, Jerry Li, Allen Liu, Ryan O'Donnell, Ewin Tang |
| 2024 | FOCS | Efficient Certificates of Anti-Concentration Beyond Gaussians. | Ainesh Bakshi, Pravesh K. Kothari, Goutham Rajendran, Madhur Tulsiani, Aravindan Vijayaraghavan |
| 2024 | FOCS | High-Temperature Gibbs States are Unentangled and Efficiently Preparable. | Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang |
| 2024 | FOCS | Structure Learning of Hamiltonians from Real-Time Evolution. | Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang |
| 2024 | SODA | An Improved Classical Singular Value Transformation for Quantum Machine Learning. | Ainesh Bakshi, Ewin Tang |
| 2024 | STOC | Learning Quantum Hamiltonians at Any Temperature in Polynomial Time. | Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang |
| 2023 | FOCS | Krylov Methods are (nearly) Optimal for Low-Rank Approximation. | Ainesh Bakshi, Shyam Narayanan |
| 2023 | ICLR | Subquadratic Algorithms for Kernel Matrices via Kernel Density Estimation. | Ainesh Bakshi, Piotr Indyk, Praneeth Kacham, Sandeep Silwal, Samson Zhou |
| 2023 | ICML | Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems. | Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau |
| 2023 | STOC | A New Approach to Learning Linear Dynamical Systems. | Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau |
| 2022 | STOC | Low-rank approximation with | Ainesh Bakshi, Kenneth L. Clarkson, David P. Woodruff |
| 2022 | STOC | Robustly learning mixtures of | Ainesh Bakshi, Ilias Diakonikolas, He Jia, Daniel M. Kane, Pravesh K. Kothari, Santosh S. Vempala |
| 2021 | ICLR | Learning a Latent Simplex in Input Sparsity Time. | Ainesh Bakshi, Chiranjib Bhattacharyya, Ravi Kannan, David P. Woodruff, Samson Zhou |
| 2021 | SODA | List-Decodable Subspace Recovery: Dimension Independent Error in Polynomial Time. | Ainesh Bakshi, Pravesh K. Kothari |
| 2021 | STOC | Robust linear regression: optimal rates in polynomial time. | Ainesh Bakshi, Adarsh Prasad |
| 2020 | FOCS | Testing Positive Semi-Definiteness via Random Submatrices. | Ainesh Bakshi, Nadiia Chepurko, Rajesh Jayaram |
| 2020 | FOCS | Robust and Sample Optimal Algorithms for PSD Low Rank Approximation. | Ainesh Bakshi, Nadiia Chepurko, David P. Woodruff |
| 2020 | FOCS | Outlier-Robust Clustering of Gaussians and Other Non-Spherical Mixtures. | Ainesh Bakshi, Ilias Diakonikolas, Samuel B. Hopkins, Daniel Kane, Sushrut Karmalkar, Pravesh K. Kothari |
| 2019 | COLT | Learning Two Layer Rectified Neural Networks in Polynomial Time. | Ainesh Bakshi, Rajesh Jayaram, David P. Woodruff |
| 2019 | ICALP | Robust Communication-Optimal Distributed Clustering Algorithms. | Pranjal Awasthi, Ainesh Bakshi, Maria-Florina Balcan, Colin White, David P. Woodruff |
| 2017 | FAST | File Systems Fated for Senescence? Nonsense, Says Science! | 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 |
| 2014 | ACIIDS | Non Dominated Sorting Genetic Algorithm for Chance Constrained Supplier Selection Model with Volume Discounts. | Remica Aggarwal, Ainesh Bakshi |
| 2014 | ICFHR | A Novel Feature Selection and Extraction Technique for Classification. | Kratarth Goel, Raunaq Vohra, Ainesh Bakshi |
| 2014 | SMC | A novel feature selection and extraction technique for classification. | Kratarth Goel, Raunaq Vohra, Ainesh Bakshi |
| 2013 | ICVS | 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 |