Krishnakumar Balasubramanian
27 papers · 2010–2025 · 7 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (35) 🏃 Academic Marathon · Full 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird
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Keyword Pioneer
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Keyword Collector
(136)
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Trend Setter
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Century Club · Bronze
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Unstoppable · Bronze
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Conference Pioneer
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Research topics
Machine Learning
(26)
Optimization & Theory · Machine Learning
(21)
Mathematics & Optimization
(16)
Optimization
(10)
Core Methods
(8)
Mathematics
(8)
Learning Types
(6)
Probability
(4)
Statistics
(2)
Artificial Intelligence
(1)
Deep Learning
(1)
Reinforcement Learning
(1)
Bayesian & Probabilistic
(1)
Methods
(1)
Optimization & Theory · Deep Learning
(1)
Keywords
stochastic optimization
(7)
non-convex optimization
(5)
convergence rate
(4)
stochastic gradient descent
(3)
unlabeled data
(3)
asymptotic normality
(3)
heavy-tailed sampling
(2)
semi-supervised learning
(2)
high-dimensional statistics
(2)
unsupervised learning
(2)
zeroth-order optimization
(2)
transfer learning
(2)
conditional gradient
(2)
margin-based classification
(2)
stochastic gradient
(2)
high-dimensional regression
(2)
confidence interval
(2)
oracle complexity
(2)
sparse representation
(1)
logistic regression
(1)
Papers
Online Covariance Estimation in Nonsmooth Stochastic Approximation
COLT 2025
A Separation in Heavy-Tailed Sampling: Gaussian vs. Stable Oracles for Proximal Samplers
NeurIPS 2024
Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling
JMLR 2024
Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions
JMLR 2024
Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data
NeurIPS 2024
A one-sample decentralized proximal algorithm for non-convex stochastic composite optimization
UAI 2023
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent
NeurIPS 2023
A Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization
NeurIPS 2022
Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data
NeurIPS 2022
Stochastic Zeroth-Order Optimization under Nonstationarity and Nonconvexity
JMLR 2022
Topologically penalized regression on manifolds
JMLR 2022
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias
NeurIPS 2021
Nonparametric Modeling of Higher-Order Interactions via Hypergraphons
JMLR 2021
On Empirical Risk Minimization with Dependent and Heavy-Tailed Data
NeurIPS 2021
On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests
JMLR 2021
Escaping Saddle-Point Faster under Interpolation-like Conditions
NeurIPS 2020
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method
NeurIPS 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
NeurIPS 2018
Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein’s Lemma
NeurIPS 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
AISTATS 2011
Unsupervised Supervised Learning II: Margin-Based Classification Without Labels
JMLR 2011
Dimensionality Reduction for Text using Domain Knowledge
COLING 2010
Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels
JMLR 2010