Krishnakumar Balasubramanian
Orcid: 0000-0001-5271-9314Affiliations:
- University of California, Davis, Graduate Group in Applied Mathematics, CA, USA
- Georgia Institute of Technology, Atlanta, GA, USA (PhD 2014)
According to our database1,
Krishnakumar Balasubramanian
authored at least 55 papers
between 2010 and 2024.
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Bibliography
2024
IEEE Trans. Inf. Theory, January, 2024
Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance Estimation.
CoRR, 2024
2023
Zeroth-order algorithms for nonconvex-strongly-concave minimax problems with improved complexities.
J. Glob. Optim., November, 2023
Stochastic Zeroth-Order Functional Constrained Optimization: Oracle Complexity and Applications.
INFORMS J. Optim., July, 2023
Math. Oper. Res., May, 2023
From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression.
CoRR, 2023
Online covariance estimation for stochastic gradient descent under Markovian sampling.
CoRR, 2023
CoRR, 2023
Gaussian random field approximation via Stein's method with applications to wide random neural networks.
CoRR, 2023
Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions.
CoRR, 2023
Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space.
CoRR, 2023
CoRR, 2023
A one-sample decentralized proximal algorithm for non-convex stochastic composite optimization.
Proceedings of the Uncertainty in Artificial Intelligence, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
2022
Fractal Gaussian Networks: A Sparse Random Graph Model Based on Gaussian Multiplicative Chaos.
IEEE Trans. Inf. Theory, 2022
Stochastic Multilevel Composition Optimization Algorithms with Level-Independent Convergence Rates.
SIAM J. Optim., 2022
Improved complexities for stochastic conditional gradient methods under interpolation-like conditions.
Oper. Res. Lett., 2022
J. Mach. Learn. Res., 2022
Zeroth-Order Nonconvex Stochastic Optimization: Handling Constraints, High Dimensionality, and Saddle Points.
Found. Comput. Math., 2022
Decentralized Stochastic Bilevel Optimization with Improved Per-Iteration Complexity.
CoRR, 2022
Projection-free Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data.
CoRR, 2022
A Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022
2021
J. Mach. Learn. Res., 2021
J. Mach. Learn. Res., 2021
Statistical Inference for Polyak-Ruppert Averaged Zeroth-order Stochastic Gradient Algorithm.
CoRR, 2021
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
2020
Stochastic Multi-level Composition Optimization Algorithms with Level-Independent Convergence Rates.
CoRR, 2020
CoRR, 2020
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
2019
CoRR, 2019
CoRR, 2019
Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT.
Proceedings of the Conference on Learning Theory, 2019
2018
Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
2017
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017
High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation.
Proceedings of the 34th International Conference on Machine Learning, 2017
2016
Artif. Intell., 2016
2014
2013
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013
2012
Proceedings of the 29th International Conference on Machine Learning, 2012
2011
J. Mach. Learn. Res., 2011
2010
Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels.
J. Mach. Learn. Res., 2010
Unsupervised Supervised Learning II: Training Margin Based Classifiers without Labels
CoRR, 2010
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010
Proceedings of the COLING 2010, 2010