Krishnakumar Balasubramanian

research

∙ 08/03/2023

Online covariance estimation for stochastic gradient descent under Markovian sampling

We study the online overlapping batch-means covariance estimator for Sto...

0 Abhishek Roy, et al. ∙

research

∙ 07/11/2023

Stochastic Nested Compositional Bi-level Optimization for Robust Feature Learning

We develop and analyze stochastic approximation algorithms for solving n...

0 Xuxing Chen, et al. ∙

research

∙ 06/28/2023

Gaussian random field approximation via Stein's method with applications to wide random neural networks

We derive upper bounds on the Wasserstein distance (W_1), with respect t...

0 Krishnakumar Balasubramanian, et al. ∙

research

∙ 06/21/2023

Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions

Stochastic Bilevel optimization usually involves minimizing an upper-lev...

0 Xuxing Chen, et al. ∙

research

∙ 05/23/2023

Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent

Stein Variational Gradient Descent (SVGD) is a nonparametric particle-ba...

0 Tianle Liu, et al. ∙

research

∙ 04/10/2023

Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space

Variational inference (VI) seeks to approximate a target distribution π ...

7 Michael Diao, et al. ∙

research

∙ 04/03/2023

High-dimensional scaling limits and fluctuations of online least-squares SGD with smooth covariance

We derive high-dimensional scaling limits and fluctuations for the onlin...

0 Krishnakumar Balasubramanian, et al. ∙

research

∙ 03/07/2023

Towards a Complete Analysis of Langevin Monte Carlo: Beyond Poincaré Inequality

Langevin diffusions are rapidly convergent under appropriate functional ...

0 Alireza Mousavi Hosseini, et al. ∙

research

∙ 03/01/2023

Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling

We analyze the complexity of sampling from a class of heavy-tailed distr...

0 Ye He, et al. ∙

research

∙ 02/20/2023

A One-Sample Decentralized Proximal Algorithm for Non-Convex Stochastic Composite Optimization

We focus on decentralized stochastic non-convex optimization, where n ag...

0 Tesi Xiao, et al. ∙

research

∙ 02/20/2023

High-dimensional Central Limit Theorems for Linear Functionals of Online Least-Squares SGD

Stochastic gradient descent (SGD) has emerged as the quintessential meth...

0 Bhavya Agrawalla, et al. ∙

research

∙ 02/16/2023

Improved Discretization Analysis for Underdamped Langevin Monte Carlo

Underdamped Langevin Monte Carlo (ULMC) is an algorithm used to sample f...

0 Matthew Zhang, et al. ∙

research

∙ 11/15/2022

Regularized Stein Variational Gradient Flow

The Stein Variational Gradient Descent (SVGD) algorithm is an determinis...

0 Ye He, et al. ∙

research

∙ 10/23/2022

Decentralized Stochastic Bilevel Optimization with Improved Per-Iteration Complexity

Bilevel optimization recently has received tremendous attention due to i...

1 Xuxing Chen, et al. ∙

research

∙ 10/19/2022

A Flexible Approach for Normal Approximation of Geometric and Topological Statistics

We derive normal approximation results for a class of stabilizing functi...

0 Zhaoyang Shi, et al. ∙

research

∙ 06/22/2022

Projection-free Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data

We study a projection-free conditional gradient-type algorithm for const...

0 Abhishek Roy, et al. ∙

research

∙ 06/08/2022

Unified RKHS Methodology and Analysis for Functional Linear and Single-Index Models

Functional linear and single-index models are core regression methods in...

0 Krishnakumar Balasubramanian, et al. ∙

research

∙ 02/23/2022

Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance

We study stochastic convex optimization under infinite noise variance. S...

0 Nuri Mert Vural, et al. ∙

research

∙ 02/10/2022

Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity Guarantees for Langevin Monte Carlo

For the task of sampling from a density π∝exp(-V) on ℝ^d, where V is pos...

6 Krishnakumar Balasubramanian, et al. ∙

research

∙ 02/09/2022

A Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization

We propose a projection-free conditional gradient-type algorithm for smo...

0 Tesi Xiao, et al. ∙

research

∙ 01/20/2022

Heavy-tailed Sampling via Transformed Unadjusted Langevin Algorithm

We analyze the oracle complexity of sampling from polynomially decaying ...

0 Ye He, et al. ∙

research

∙ 10/26/2021

Topologically penalized regression on manifolds

We study a regression problem on a compact manifold M. In order to take ...

0 Olympio Hacquard, et al. ∙

research

∙ 09/06/2021

On Empirical Risk Minimization with Dependent and Heavy-Tailed Data

In this work, we establish risk bounds for the Empirical Risk Minimizati...

0 Abhishek Roy, et al. ∙

research

∙ 05/18/2021

Nonparametric Modeling of Higher-Order Interactions via Hypergraphons

We study statistical and algorithmic aspects of using hypergraphons, tha...

0 Krishnakumar Balasubramanian, et al. ∙

research

∙ 02/10/2021

Statistical Inference for Polyak-Ruppert Averaged Zeroth-order Stochastic Gradient Algorithm

As machine learning models are deployed in critical applications, it bec...

0 Yanhao Jin, et al. ∙

research

∙ 11/06/2020

On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method

The randomized midpoint method, proposed by [SL19], has emerged as an op...

0 Ye He, et al. ∙

research

∙ 09/28/2020

Escaping Saddle-Points Faster under Interpolation-like Conditions

In this paper, we show that under over-parametrization several standard ...

0 Abhishek Roy, et al. ∙

research

∙ 08/24/2020

Stochastic Multi-level Composition Optimization Algorithms with Level-Independent Convergence Rates

In this paper, we study smooth stochastic multi-level composition optimi...

0 Krishnakumar Balasubramanian, et al. ∙

research

∙ 08/07/2020

Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos

We propose a novel stochastic network model, called Fractal Gaussian Net...

0 Subhroshekhar Ghosh, et al. ∙

research

∙ 06/15/2020

Improved Complexities for Stochastic Conditional Gradient Methods under Interpolation-like Conditions

We analyze stochastic conditional gradient type methods for constrained ...

0 Tesi Xiao, et al. ∙

research

∙ 06/14/2020

An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias

Structured non-convex learning problems, for which critical points have ...

0 Lu Yu, et al. ∙

research

∙ 03/25/2020

Zeroth-order Optimization on Riemannian Manifolds

We propose and analyze zeroth-order algorithms for optimization over Rie...

17 Jiaxiang Li, et al. ∙

research

∙ 01/22/2020

Zeroth-Order Algorithms for Nonconvex Minimax Problems with Improved Complexities

In this paper, we study zeroth-order algorithms for minimax optimization...

0 Zhongruo Wang, et al. ∙

research

∙ 12/03/2019

Online and Bandit Algorithms for Nonstationary Stochastic Saddle-Point Optimization

Saddle-point optimization problems are an important class of optimizatio...

0 Abhishek Roy, et al. ∙

research

∙ 07/31/2019

Multi-Point Bandit Algorithms for Nonstationary Online Nonconvex Optimization

Bandit algorithms have been predominantly analyzed in the convex setting...

0 Abhishek Roy, et al. ∙

research

∙ 04/03/2019

Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT

We provide non-asymptotic convergence rates of the Polyak-Ruppert averag...

0 Andreas Anastasiou, et al. ∙

research

∙ 02/04/2019

Non-asymptotic Results for Langevin Monte Carlo: Coordinate-wise and Black-box Sampling

Euler-Maruyama and Ozaki discretization of a continuous time diffusion p...

0 Lingqing Shen, et al. ∙

research

∙ 09/17/2018

Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates

In this paper, we propose and analyze zeroth-order stochastic approximat...

0 Krishnakumar Balasubramanian, et al. ∙

research

∙ 07/17/2018

Tensor Methods for Additive Index Models under Discordance and Heterogeneity

Motivated by the sampling problems and heterogeneity issues common in hi...

0 Krishnakumar Balasubramanian, et al. ∙

research

∙ 09/26/2017

On Stein's Identity and Near-Optimal Estimation in High-dimensional Index Models

We consider estimating the parametric components of semi-parametric mult...

0 Zhuoran Yang, et al. ∙

research

∙ 09/24/2017

On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests

The reproducing kernel Hilbert space (RKHS) embedding of distributions o...

0 Krishnakumar Balasubramanian, et al. ∙

research

∙ 09/26/2013

High-dimensional Joint Sparsity Random Effects Model for Multi-task Learning

Joint sparsity regularization in multi-task learning has attracted much ...

0 Krishnakumar Balasubramanian, et al. ∙

research

∙ 10/03/2012

Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations

We propose and analyze a novel framework for learning sparse representat...

0 Krishnakumar Balasubramanian, et al. ∙

research

∙ 06/27/2012

The Landmark Selection Method for Multiple Output Prediction

Conditional modeling x → y is a central problem in machine learning. A s...

0 Krishnakumar Balasubramanian, et al. ∙

Krishnakumar Balasubramanian

Featured Co-authors