Garvesh Raskutti
18 papers · 2008–2025 · 4 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
Jump to papers ↓+8 more ↓ Show less ↑
πΊοΈ Taxonomy Completionist (35) π Academic Marathon Β· Full π Conference Polyglot Β· Bronze π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
π¬
Deep Specialist
(10)
π
Keyword Champion
π§
Keyword Pioneer
π
Keyword Trendsetter Combo
(4)
π₯
Unstoppable Β· Bronze
ποΈ
Keyword Collector
(98)
π
Trend Setter
π
Century Club Β· Bronze
Venues
Top co-authors
Research topics
Machine Learning
(16)
Optimization & Theory
(14)
Core Methods
(11)
Artificial Intelligence
(8)
Mathematics & Optimization
(8)
Optimization
(7)
Bayesian & Probabilistic Β· Artificial Intelligence
(5)
Core AI
(4)
Data Science & Analytics
(2)
Bayesian & Probabilistic Β· Machine Learning
(2)
Mathematics
(2)
Methods
(2)
Computer Science
(1)
Deep Learning
(1)
Foundations
(1)
Keywords
kernel methods
(4)
high-dimensional statistics
(3)
graphical model
(3)
maximum likelihood
(2)
randomized sketching
(2)
hyperparameter estimation
(2)
variable importance
(2)
nonparametric regression
(2)
structure learning
(2)
early stopping
(2)
gaussian process
(2)
least square
(2)
convex optimization
(2)
maximum likelihood estimation
(2)
sparse additive model
(2)
statistical analysis
(2)
reproducing kernel hilbert space
(2)
minimax optimization
(1)
graph structure
(1)
covariance estimation
(1)
Papers
Reliable and Scalable Variable Importance Estimation via Warm-start and Early Stopping
AISTATS 2025
Gaussian Process Parameter Estimation Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits
JMLR 2022
Lazy Estimation of Variable Importance for Large Neural Networks
ICML 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
NeurIPS 2020
Estimating Network Structure from Incomplete Event Data
AISTATS 2019
Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression
JMLR 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
NeurIPS 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
NeurIPS 2009
Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \boldmath$\ell_1$-regularized MLE
NeurIPS 2008