Krishnakumar Balasubramanian
Krishnakumar Balasubramanian
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The Landmark Selection Method for Multiple Output Prediction
K Balasubramanian, G Lebanon
Proc. of the 29th International Conference on Machine Learning (ICML), 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
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
Zeroth-order Nonconvex Stochastic Optimization: Handling Constraints, High-Dimensionality and Saddle-Points
K Balasubramanian, S Ghadimi
Foundations of Computational Mathematics, 2022
Smooth sparse coding via marginal regression for learning sparse representations
K Balasubramanian, K Yu, G Lebanon
International Conference on Machine Learning, 289-297, 2013
Ultrahigh Dimensional Feature Screening via RKHS Embeddings
K Balasubramanian, BK Sriperumbudur, G Lebanon
International Conference on Artificial Intelligence and Statistics (AISTATS), 2013
High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation
HL Zhuoran Yang, Krishnakumar Balasubramanian
International Conference on Machine Learning, 2017
Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations
K Balasubramanian, K Yu, G Lebanon
Artificial Intelligence, 2016
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
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
Dimensionality reduction for text using domain knowledge
Y Mao, K Balasubramanian, G Lebanon
Coling 2010: Posters, 801-809, 2010
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
Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT
A Anastasiou, K Balasubramanian, M Erdogdu
Conference on Learning Theory, 2019
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
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
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
Asymptotic analysis of generative semi-supervised learning
JV Dillon, K Balasubramanian, G Lebanon
International Conference on Machine Learning (ICML), 2010
On Stein's Identity and Near-Optimal Estimation in High-dimensional Index Models
Z Yang, K Balasubramanian, H Liu
arXiv preprint arXiv:1709.08795, 2017
Stochastic Zeroth-order Riemannian Derivative Estimation and Optimization
J Li, K Balasubramanian, S Ma
Mathematics of Operations Research, 2022
Online and Bandit Algorithms for Nonstationary Stochastic Saddle-Point Optimization
A Roy, Y Chen, K Balasubramanian, P Mohapatra
arXiv preprint arXiv:1912.01698, 2019
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