← Authors

Garvesh Raskutti

18 papers · 2008–2025 · 4 conferences · across top CS/AI conferences

Achievements

πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (4) πŸƒ Academic Marathon (17) 🐝 Cross-Pollinator (13) 🌈 Renaissance Researcher (6) πŸ—ΊοΈ Taxonomy Completionist (12) 🌟 Keyword Trendsetter Combo (5) πŸ† Keyword Champion πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (94) πŸ”₯ Unstoppable (5) πŸ’Ž Century Club (18)

Conferences

JMLR (10) NIPS (4) AISTATS (2) ICML (2)

Top co-authors

Martin J. Wainwright (5) Bin Yu (5) Lili Zheng (3) Rebecca Willett (3) Ming Yuan (2) Benjamin Mark (2) Gunwoong Park (2) Hao Chen (2) Raed AL Kontar (2) Ran Dai (1)

Keywords

graphical model (3) kernel methods (3) reproducing kernel hilbert space (2) randomized sketching (2) hyperparameter estimation (2) convex optimization (2) maximum likelihood estimation (2) statistical analysis (2) structure learning (2) least square (2) high-dimensional statistics (2) maximum likelihood (2) gaussian process (2) minimax optimization (1) graph structure (1) covariance estimation (1) sparse estimation (1) non-convex optimization (1) stochastic gradient descent (1) convergence analysis (1)

Papers

Reliable and Scalable Variable Importance Estimation via Warm-start and Early Stopping AISTATS 2025
Lazy Estimation of Variable Importance for Large Neural Networks ICML 2022
Gaussian Process Parameter Estimation Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits JMLR 2022
A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration JMLR 2021
Context-dependent Networks in Multivariate Time Series: Models, Methods, and Risk Bounds in High Dimensions JMLR 2021
Convex and Non-Convex Approaches for Statistical Inference with Class-Conditional Noisy Labels JMLR 2020
Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes NIPS 2020
Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression JMLR 2019
Estimating Network Structure from Incomplete Event Data AISTATS 2019
Learning Quadratic Variance Function (QVF) DAG Models via OverDispersion Scoring (ODS) JMLR 2018
A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares JMLR 2016
Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring NIPS 2015
Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares ICML 2015
Early Stopping and Non-parametric Regression: An Optimal Data-dependent Stopping Rule JMLR 2014
Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming JMLR 2012
Restricted Eigenvalue Properties for Correlated Gaussian Designs JMLR 2010
Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness NIPS 2009
Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \boldmath$\ell_1$-regularized MLE NIPS 2008