Krishnakumar Balasubramanian

ORCID

Publication Activity (10 Years)

Years Active: 2003-2023
Publications (10 Years): 39

Top Venues

Georgia Institute of Technology, Atlanta, GA, USA (PHD)

Education History

Georgia Institute of Technology, Atlanta, GA, USA

PHD

Publications

Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, Natesh Pillai
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent. CoRR (2023)
Michael Diao, Krishnakumar Balasubramanian, Sinho Chewi, Adil Salim
Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space. CoRR (2023)
Tesi Xiao, Xuxing Chen, Krishnakumar Balasubramanian, Saeed Ghadimi
A One-Sample Decentralized Proximal Algorithm for Non-Convex Stochastic Composite Optimization. CoRR (2023)
Ye He, Tyler Farghly, Krishnakumar Balasubramanian, Murat A. Erdogdu
Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling. CoRR (2023)
Xuxing Chen, Minhui Huang, Shiqian Ma, Krishnakumar Balasubramanian
Decentralized Stochastic Bilevel Optimization with Improved Per-Iteration Complexity. CoRR (2022)
Nuri Mert Vural, Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu
Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance. COLT (2022)
Tesi Xiao, Krishnakumar Balasubramanian, Saeed Ghadimi
Improved complexities for stochastic conditional gradient methods under interpolation-like conditions. Oper. Res. Lett. 50 (2) (2022)
Nuri Mert Vural, Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu
Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance. CoRR (2022)
Subhroshekhar Ghosh, Krishnakumar Balasubramanian, Xiaochuan Yang
Fractal Gaussian Networks: A Sparse Random Graph Model Based on Gaussian Multiplicative Chaos. IEEE Trans. Inf. Theory 68 (5) (2022)
Ye He, Krishnakumar Balasubramanian, Bharath K. Sriperumbudur, Jianfeng Lu
Regularized Stein Variational Gradient Flow. CoRR (2022)
Krishnakumar Balasubramanian, Saeed Ghadimi, Anthony Nguyen
Stochastic Multilevel Composition Optimization Algorithms with Level-Independent Convergence Rates. SIAM J. Optim. 32 (2) (2022)
Krishnakumar Balasubramanian, Saeed Ghadimi
Zeroth-Order Nonconvex Stochastic Optimization: Handling Constraints, High Dimensionality, and Saddle Points. Found. Comput. Math. 22 (1) (2022)
Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi
Projection-free Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data. CoRR (2022)
Yanhao Jin, Tesi Xiao, Krishnakumar Balasubramanian
Statistical Inference for Polyak-Ruppert Averaged Zeroth-order Stochastic Gradient Algorithm. CoRR (2021)
Krishnakumar Balasubramanian
Nonparametric Modeling of Higher-Order Interactions via Hypergraphons. CoRR (2021)
Olympio Hacquard, Krishnakumar Balasubramanian, Gilles Blanchard, Wolfgang Polonik, Clément Levrard
Topologically penalized regression on manifolds. CoRR (2021)
Abhishek Roy, Krishnakumar Balasubramanian, Murat A. Erdogdu
On Empirical Risk Minimization with Dependent and Heavy-Tailed Data. NeurIPS (2021)
Krishnakumar Balasubramanian
Nonparametric Modeling of Higher-Order Interactions via Hypergraphons. J. Mach. Learn. Res. 22 (2021)
Krishnakumar Balasubramanian, Tong Li, Ming Yuan
On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests. J. Mach. Learn. Res. 22 (2021)
Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias. NeurIPS (2021)
Subhroshekhar Ghosh, Krishnakumar Balasubramanian, Xiaochuan Yang
Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos. ICML (2020)
Jiaxiang Li, Krishnakumar Balasubramanian, Shiqian Ma
Zeroth-order Optimization on Riemannian Manifolds. CoRR (2020)
Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias. CoRR (2020)
Subhroshekhar Ghosh, Krishnakumar Balasubramanian, Xiaochuan Yang
Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos. CoRR (2020)
Zhongruo Wang, Krishnakumar Balasubramanian, Shiqian Ma, Meisam Razaviyayn
Zeroth-Order Algorithms for Nonconvex Minimax Problems with Improved Complexities. CoRR (2020)
Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra
Escaping Saddle-Point Faster under Interpolation-like Conditions. NeurIPS (2020)
Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method. NeurIPS (2020)
Krishnakumar Balasubramanian, Saeed Ghadimi, Anthony Nguyen
Stochastic Multi-level Composition Optimization Algorithms with Level-Independent Convergence Rates. CoRR (2020)
Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra
Escaping Saddle-Points Faster under Interpolation-like Conditions. CoRR (2020)
Tesi Xiao, Krishnakumar Balasubramanian, Saeed Ghadimi
Improved Complexities for Stochastic Conditional Gradient Methods under Interpolation-like Conditions. CoRR (2020)
Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method. CoRR (2020)
Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra
Multi-Point Bandit Algorithms for Nonstationary Online Nonconvex Optimization. CoRR (2019)
Abhishek Roy, Yifang Chen, Krishnakumar Balasubramanian, Prasant Mohapatra
Online and Bandit Algorithms for Nonstationary Stochastic Saddle-Point Optimization. CoRR (2019)
Andreas Anastasiou, Krishnakumar Balasubramanian, Murat A. Erdogdu
Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT. COLT (2019)
Krishnakumar Balasubramanian, Saeed Ghadimi
Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates. CoRR (2018)
Krishnakumar Balasubramanian, Saeed Ghadimi
Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates. NeurIPS (2018)
Zhuoran Yang, Krishnakumar Balasubramanian, Zhaoran Wang, Han Liu
Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein's Lemma. NIPS (2017)
Zhuoran Yang, Krishnakumar Balasubramanian, Han Liu
High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation. ICML (2017)
Krishnakumar Balasubramanian, Kai Yu, Guy Lebanon
Smooth sparse coding via marginal regression for learning sparse representations. Artif. Intell. 238 (2016)
Krishnakumar Balasubramanian, Kai Yu, Tong Zhang
High-dimensional Joint Sparsity Random Effects Model for Multi-task Learning. UAI (2013)
Krishnakumar Balasubramanian, Kai Yu, Guy Lebanon
Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations. ICML (3) (2013)
Krishnakumar Balasubramanian, Kai Yu, Tong Zhang
High-dimensional Joint Sparsity Random Effects Model for Multi-task Learning. CoRR (2013)
Krishnakumar Balasubramanian, Bharath K. Sriperumbudur, Guy Lebanon
Ultrahigh Dimensional Feature Screening via RKHS Embeddings. AISTATS (2013)
Krishnakumar Balasubramanian, Guy Lebanon
The Landmark Selection Method for Multiple Output Prediction. ICML (2012)
Krishnakumar Balasubramanian, Kai Yu, Guy Lebanon
Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations CoRR (2012)
Krishnakumar Balasubramanian, Pinar Donmez, Guy Lebanon
Unsupervised Supervised Learning II: Margin-Based Classification without Labels. AISTATS (2011)
Krishnakumar Balasubramanian, Pinar Donmez, Guy Lebanon
Unsupervised Supervised Learning II: Margin-Based Classification Without Labels. J. Mach. Learn. Res. 12 (2011)
Pinar Donmez, Krishnakumar Balasubramanian, Guy Lebanon
Unsupervised Supervised Learning II: Training Margin Based Classifiers without Labels CoRR (2010)
Yi Mao, Krishnakumar Balasubramanian, Guy Lebanon
Dimensionality Reduction for Text using Domain Knowledge. COLING (Posters) (2010)
Pinar Donmez, Guy Lebanon, Krishnakumar Balasubramanian
Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels. J. Mach. Learn. Res. 11 (2010)
Yi Mao, Krishnakumar Balasubramanian, Guy Lebanon
Linguistic Geometries for Unsupervised Dimensionality Reduction CoRR (2010)
Joshua V. Dillon, Krishnakumar Balasubramanian, Guy Lebanon
Asymptotic Analysis of Generative Semi-Supervised Learning. ICML (2010)
Joshua V. Dillon, Krishnakumar Balasubramanian, Guy Lebanon
Asymptotic Analysis of Generative Semi-Supervised Learning CoRR (2010)
Aniruddha S. Gokhale, Krishnakumar Balasubramanian, Arvind S. Krishna, Jaiganesh Balasubramanian, George Edwards, Gan Deng, Emre Turkay, Jeff Parsons, Douglas C. Schmidt
Model driven middleware: A new paradigm for developing distributed real-time and embedded systems. Sci. Comput. Program. 73 (1) (2008)
Krishnakumar Balasubramanian, Douglas C. Schmidt
Physical Assembly Mapper: A Model-Driven Optimization Tool for QoS-Enabled Component Middleware. IEEE Real-Time and Embedded Technology and Applications Symposium (2008)
Krishnakumar Balasubramanian, Douglas C. Schmidt, Zoltán Molnár, Ákos Lédeczi
Component-Based System Integration via (Meta)Model Composition. ECBS (2007)
Krishnakumar Balasubramanian, Jaiganesh Balasubramanian, Jeff Parsons, Aniruddha S. Gokhale, Douglas C. Schmidt
A Platform-Independent Component Modeling Language for Distributed Real-time and Embedded Systems. J. Comput. Syst. Sci. 73 (2) (2007)
Amogh Kavimandan, Krishnakumar Balasubramanian, Nishanth Shankaran, Aniruddha S. Gokhale, Douglas C. Schmidt
QUICKER: A Model-Driven QoS Mapping Tool for QoS-Enabled Component Middleware. ISORC (2007)
Krishnakumar Balasubramanian, Aniruddha S. Gokhale, Yuehua Lin, Jing Zhang, Jeff Gray
Weaving Deployment Aspects into Domain-specific Models. Int. J. Softw. Eng. Knowl. Eng. 16 (3) (2006)
Krishnakumar Balasubramanian, Aniruddha S. Gokhale, Gabor Karsai, Janos Sztipanovits, Sandeep Neema
Developing Applications Using Model-Driven Design Environments. Computer 39 (2) (2006)
Krishnakumar Balasubramanian, Arvind S. Krishna, Emre Turkay, Jaiganesh Balasubramanian, Jeff Parsons, Aniruddha S. Gokhale, Douglas C. Schmidt
Applying model-driven development to distributed real-time and embedded avionics systems. Int. J. Embed. Syst. 2 (3/4) (2006)
Krishnakumar Balasubramanian, Jaiganesh Balasubramanian, Jeff Parsons, Aniruddha S. Gokhale, Douglas C. Schmidt
A Platform-Independent Component Modeling Language for Distributed Real-Time and Embedded Systems. IEEE Real-Time and Embedded Technology and Applications Symposium (2005)
Aniruddha S. Gokhale, Krishnakumar Balasubramanian, Tao Lu
CoSMIC: addressing crosscutting deployment and configuration concerns of distributed real-time and embedded systems. OOPSLA Companion (2004)
Krishnakumar Balasubramanian, Nanbor Wang, Douglas C. Schmidt
Towards Composable Distributed Real-time and Embedded Software. WORDS (2003)

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Publication Activity (10 Years)

Top Topics

Top Venues

Krishnakumar Balasubramanian