← Authors

Krishnakumar Balasubramanian

27 papers · 2010–2025 · 7 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (13) 🌈 Renaissance Researcher (5) 🧭 Keyword Pioneer 🐝 Cross-Pollinator (9) 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (7) πŸƒ Academic Marathon (15) πŸ† Keyword Champion (2) πŸ—ƒοΈ Keyword Collector (133) πŸ’Ž Century Club (27) πŸ”₯ Unstoppable (9) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer

Conferences

NIPS (11) JMLR (8) AISTATS (2) COLT (2) ICML (2) COLING (1) UAI (1)

Top co-authors

Saeed Ghadimi (6) Guy Lebanon (6) Abhishek Roy (6) Murat A Erdogdu (4) Ye He (3) Xuxing Chen (3) Murat A. Erdogdu (3) Tesi Xiao (3) Pinar Donmez (3) Han Liu (2)

Research topics

Statistics (2) Probability (1)

Keywords

stochastic optimization (7) stochastic gradient descent (3) non-convex optimization (3) convergence rate (3) unlabeled datum (3) heavy-tailed sampling (2) transfer learning (2) semi-supervised learning (2) unsupervised learning (2) asymptotic normality (2) oracle complexity (2) nonconvex optimization (2) margin-based classification (2) confidence interval (2) conditional gradient (2) high-dimensional statistics (2) stochastic gradient (2) high-dimensional regression (2) zeroth-order optimization (2) logistic regression (1)

Papers

Online Covariance Estimation in Nonsmooth Stochastic Approximation COLT 2025
Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data NIPS 2024
Mean-Square Analysis of Discretized ItΓ΄ Diffusions for Heavy-tailed Sampling JMLR 2024
Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions JMLR 2024
A Separation in Heavy-Tailed Sampling: Gaussian vs. Stable Oracles for Proximal Samplers NIPS 2024
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent NIPS 2023
A one-sample decentralized proximal algorithm for non-convex stochastic composite optimization UAI 2023
Stochastic Zeroth-Order Optimization under Nonstationarity and Nonconvexity JMLR 2022
A Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization NIPS 2022
Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data NIPS 2022
Topologically penalized regression on manifolds JMLR 2022
On Empirical Risk Minimization with Dependent and Heavy-Tailed Data NIPS 2021
On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests JMLR 2021
Nonparametric Modeling of Higher-Order Interactions via Hypergraphons JMLR 2021
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias NIPS 2021
Escaping Saddle-Point Faster under Interpolation-like Conditions NIPS 2020
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method NIPS 2020
Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT COLT 2019
Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates NIPS 2018
Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein’s Lemma NIPS 2017
High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation ICML 2017
Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations ICML 2013
Ultrahigh Dimensional Feature Screening via RKHS Embeddings AISTATS 2013
Unsupervised Supervised Learning II: Margin-Based Classification Without Labels JMLR 2011
Unsupervised Supervised Learning II: Margin-Based Classification without Labels AISTATS 2011
Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels JMLR 2010
Dimensionality Reduction for Text using Domain Knowledge COLING 2010