| The Landmark Selection Method for Multiple Output Prediction K Balasubramanian, G Lebanon Proc. of the 29th International Conference on Machine Learning (ICML), 2012 | 120 | 2012 |
| Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates K Balasubramanian, S Ghadimi Advances in Neural Information Processing Systems (NeurIPS), 2018 | 104 | 2018 |
| Zeroth-order Nonconvex Stochastic Optimization: Handling Constraints, High-Dimensionality and Saddle-Points K Balasubramanian, S Ghadimi Foundations of Computational Mathematics, 2022 | 93 | 2022 |
| Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels. P Donmez, G Lebanon, K Balasubramanian Journal of Machine Learning Research 11 (4), 2010 | 79 | 2010 |
| Ultrahigh Dimensional Feature Screening via RKHS Embeddings K Balasubramanian, BK Sriperumbudur, G Lebanon International Conference on Artificial Intelligence and Statistics (AISTATS), 2013 | 53 | 2013 |
| Towards a theory of non-log-concave sampling: first-order stationarity guarantees for Langevin Monte Carlo K Balasubramanian, S Chewi, MA Erdogdu, A Salim, S Zhang Conference on Learning Theory, 2896-2923, 2022 | 52 | 2022 |
| High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation HL Zhuoran Yang, Krishnakumar Balasubramanian International Conference on Machine Learning, 2017 | 52 | 2017 |
| Smooth sparse coding via marginal regression for learning sparse representations K Balasubramanian, K Yu, G Lebanon International Conference on Machine Learning, 289-297, 2013 | 43 | 2013 |
| Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations K Balasubramanian, K Yu, G Lebanon Artificial Intelligence, 2016 | 40 | 2016 |
| Zeroth-order algorithms for nonconvex–strongly-concave minimax problems with improved complexities Z Wang, K Balasubramanian, S Ma, M Razaviyayn Journal of Global Optimization (to appear), 2022 | 39* | 2022 |
| An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias L Yu, K Balasubramanian, S Volgushev, MA Erdogdu 35th Conference on Neural Information Processing Systems (NeurIPS), 2021 | 39 | 2021 |
| Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT A Anastasiou, K Balasubramanian, M Erdogdu Conference on Learning Theory, 2019 | 36 | 2019 |
| Learning Non-Gaussian Multi-Index Model via Second-Order Stein’s Method Z Yang, K Balasubramanian, Z Wang, H Liu Advances in Neural Information Processing Systems, 6099-6108, 2017 | 36* | 2017 |
| On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests. K Balasubramanian, T Li, M Yuan Journal of Machine Learning Research 22, 1:1-1:45, 2021 | 34* | 2021 |
| Stochastic Multi-level Composition Optimization Algorithms with Level-Independent Convergence Rates K Balasubramanian, S Ghadimi, A Nguyen SIAM Journal on Optimization (to appear); arXiv preprint arXiv:2008.10526, 2021 | 32 | 2021 |
| On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method Y He, K Balasubramanian, MA Erdogdu Advances in Neural Information Processing Systems 33, 2020 | 32 | 2020 |
| Unsupervised Supervised Learning II: Training Margin Based Classifiers without Labels. K Balasubramanian, P Donmez, G Lebanon Journal of Machine Learning Research 12, 1-30, 2011 | 29* | 2011 |
| Stochastic Zeroth-order Riemannian Derivative Estimation and Optimization J Li, K Balasubramanian, S Ma Mathematics of Operations Research, 2022 | 28* | 2022 |
| Dimensionality reduction for text using domain knowledge Y Mao, K Balasubramanian, G Lebanon Coling 2010: Posters, 801-809, 2010 | 28 | 2010 |
| Online and Bandit Algorithms for Nonstationary Stochastic Saddle-Point Optimization A Roy, Y Chen, K Balasubramanian, P Mohapatra arXiv preprint arXiv:1912.01698, 2019 | 24 | 2019 |
