Krishnakumar Balasubramanian
27 papers · 2010–2025 · 7 conferences · across top CS/AI conferences
Achievements
Jump to papers ↓+9 more ↓ Show less ↑
π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (13) π§ Keyword Pioneer π£ Hot Topic Early Bird π Conference Polyglot (7)
π
Interdisciplinary Bridge
π
Conference Polyglot
(7)
π
Academic Marathon
(15)
π
Keyword Champion
(2)
ποΈ
Keyword Collector
(133)
π
Century Club
(27)
π₯
Unstoppable
(9)
π
Trend Setter
π
Conference Pioneer
Conferences
NIPS (11)
JMLR (8)
AISTATS (2)
COLT (2)
ICML (2)
COLING (1)
UAI (1)
Top co-authors
Research topics
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 Steinβs 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