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Krishnakumar Balasubramanian
Krishnakumar Balasubramanian
Other namesKrishna Balasubramanian
Verified email at ucdavis.edu - Homepage
Title
Cited by
Cited by
Year
The Landmark Selection Method for Multiple Output Prediction
K Balasubramanian, G Lebanon
Proc. of the 29th International Conference on Machine Learning (ICML), 2012
1222012
Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates
K Balasubramanian, S Ghadimi
Advances in Neural Information Processing Systems (NeurIPS), 2018
1122018
Zeroth-order Nonconvex Stochastic Optimization: Handling Constraints, High-Dimensionality and Saddle-Points
K Balasubramanian, S Ghadimi
Foundations of Computational Mathematics, 2022
1092022
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
792010
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
732022
Ultrahigh Dimensional Feature Screening via RKHS Embeddings
K Balasubramanian, BK Sriperumbudur, G Lebanon
International Conference on Artificial Intelligence and Statistics (AISTATS), 2013
582013
High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation
HL Zhuoran Yang, Krishnakumar Balasubramanian
International Conference on Machine Learning, 2017
552017
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
492021
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
47*2022
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
472021
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
43*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
432019
Smooth sparse coding via marginal regression for learning sparse representations
K Balasubramanian, K Yu, G Lebanon
International Conference on Machine Learning, 289-297, 2013
422013
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
39*2017
Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations
K Balasubramanian, K Yu, G Lebanon
Artificial Intelligence, 2016
392016
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
372020
Stochastic Zeroth-order Riemannian Derivative Estimation and Optimization
J Li, K Balasubramanian, S Ma
Mathematics of Operations Research, 2022
34*2022
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
33*2011
Improved discretization analysis for underdamped Langevin Monte Carlo
S Zhang, S Chewi, M Li, K Balasubramanian, MA Erdogdu
The Thirty Sixth Annual Conference on Learning Theory, 36-71, 2023
302023
Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space
MZ Diao, K Balasubramanian, S Chewi, A Salim
International Conference on Machine Learning, 7960-7991, 2023
292023
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