Garvesh Raskutti

According to our database, Garvesh Raskutti authored at least 47 papers between 2007 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Fast, Distribution-free Predictive Inference for Neural Networks with Coverage Guarantees.

Yue Gao, Garvesh Raskutti, Rebecca Willett

CoRR, 2023

2022

Gaussian Process Parameter Estimation Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits.

Hao Chen, Lili Zheng, Raed Al Kontar, Garvesh Raskutti

J. Mach. Learn. Res., 2022

Lazy Estimation of Variable Importance for Large Neural Networks.

Yue Gao, Abby Stevens, Garvesh Raskutti, Rebecca Willett

Proceedings of the International Conference on Machine Learning, 2022

2021

Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach.

Raed Kontar, Garvesh Raskutti, Shiyu Zhou

IEEE Trans. Pattern Anal. Mach. Intell., 2021

Context-dependent Networks in Multivariate Time Series: Models, Methods, and Risk Bounds in High Dimensions.

Lili Zheng, Garvesh Raskutti, Rebecca Willett, Benjamin Mark

J. Mach. Learn. Res., 2021

A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration.

Yuetian Luo, Garvesh Raskutti, Ming Yuan, Anru R. Zhang

J. Mach. Learn. Res., 2021

Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits.

Hao Chen, Lili Zheng, Raed Al Kontar, Garvesh Raskutti

CoRR, 2021

The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning.

Raed Kontar, Naichen Shi, Xubo Yue, Seokhyun Chung, Eunshin Byon, Mosharaf Chowdhury, Judy Jin, Wissam Kontar, Neda Masoud, Maher Nouiehed, Chinedum Emmanuel Okwudire, Garvesh Raskutti, Romesh Saigal, Karandeep Singh, Zhisheng Ye

CoRR, 2021

Improved Prediction and Network Estimation Using the Monotone Single Index Multi-variate Autoregressive Model.

Yue Gao, Garvesh Raskutti

CoRR, 2021

The Internet of Federated Things (IoFT).

Raed Kontar, Naichen Shi, Xubo Yue, Seokhyun Chung, Eunshin Byon, Mosharaf Chowdhury, Jionghua Jin, Wissam Kontar, Neda Masoud, Maher Nouiehed, Chinedum Emmanuel Okwudire, Garvesh Raskutti, Romesh Saigal, Karandeep Singh, Zhi-Sheng Ye

IEEE Access, 2021

Prediction in the Presence of Response-Dependent Missing Labels.

Hyebin Song, Garvesh Raskutti, Rebecca Willett

Proceedings of the IEEE Statistical Signal Processing Workshop, 2021

2020

ISLET: Fast and Optimal Low-Rank Tensor Regression via Importance Sketching.

Anru R. Zhang, Yuetian Luo, Garvesh Raskutti, Ming Yuan

SIAM J. Math. Data Sci., 2020

Graph-Based Regularization for Regression Problems with Alignment and Highly Correlated Designs.

Yuan Li, Benjamin Mark, Garvesh Raskutti, Rebecca Willett, Hyebin Song, David Neiman

SIAM J. Math. Data Sci., 2020

Convex and Non-Convex Approaches for Statistical Inference with Class-Conditional Noisy Labels.

Hyebin Song, Ran Dai, Garvesh Raskutti, Rina Foygel Barber

J. Mach. Learn. Res., 2020

Context-dependent self-exciting point processes: models, methods, and risk bounds in high dimensions.

Lili Zheng, Garvesh Raskutti, Rebecca Willett, Benjamin Mark

CoRR, 2020

Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes.

Hao Chen, Lili Zheng, Raed Al Kontar, Garvesh Raskutti

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Non-Parametric Sparse Additive Auto-Regressive Network Models.

Hao Henry Zhou, Garvesh Raskutti

IEEE Trans. Inf. Theory, 2019

Network Estimation From Point Process Data.

Benjamin Mark, Garvesh Raskutti, Rebecca Willett

IEEE Trans. Inf. Theory, 2019

Learning High-Dimensional Generalized Linear Autoregressive Models.

Eric C. Hall, Garvesh Raskutti, Rebecca M. Willett

IEEE Trans. Inf. Theory, 2019

Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression.

Han Chen, Garvesh Raskutti, Ming Yuan

J. Mach. Learn. Res., 2019

Estimating Network Structure from Incomplete Event Data.

Benjamin Mark, Garvesh Raskutti, Rebecca Willett

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Minimax Optimal Convex Methods for Poisson Inverse Problems Under ℓ_q-Ball Sparsity.

Yuan Li, Garvesh Raskutti

IEEE Trans. Inf. Theory, 2018

Graph-based regularization for regression problems with highly-correlated designs.

Yuan Li, Garvesh Raskutti, Rebecca Willett

CoRR, 2018

Graph-Based Regularization for Regression Problems with Highly-Correlated Designs.

Yuan Li, Benjamin Mark, Garvesh Raskutti, Rebecca Willett

Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing, 2018

2017

Learning Quadratic Variance Function (QVF) DAG Models via OverDispersion Scoring (ODS).

Gunwoong Park, Garvesh Raskutti

J. Mach. Learn. Res., 2017

Image inpainting using reproducing kernel Hilbert space and Heaviside functions.

Si Wang, Weihong Guo, Ting-Zhu Huang, Garvesh Raskutti

J. Comput. Appl. Math., 2017

Network estimation via poisson autoregressive models.

Benjamin Mark, Garvesh Raskutti, Rebecca Willett

Proceedings of the 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2017

2016

A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares.

Garvesh Raskutti, Michael W. Mahoney

J. Mach. Learn. Res., 2016

Identifiability assumptions for directed graphical models with feedback.

Gunwoong Park, Garvesh Raskutti

CoRR, 2016

Inference of High-dimensional Autoregressive Generalized Linear Models.

Eric C. Hall, Garvesh Raskutti, Rebecca Willett

CoRR, 2016

Inferring high-dimensional poisson autoregressive models.

Eric C. Hall, Garvesh Raskutti, Rebecca Willett

Proceedings of the IEEE Statistical Signal Processing Workshop, 2016

2015

The Information Geometry of Mirror Descent.

Garvesh Raskutti, Sayan Mukherjee

IEEE Trans. Inf. Theory, 2015

Minimax Optimal Rates for Poisson Inverse Problems With Physical Constraints.

Xin Jiang Hunt, Garvesh Raskutti, Rebecca Willett

IEEE Trans. Inf. Theory, 2015

Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring.

Gunwoong Park, Garvesh Raskutti

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares.

Garvesh Raskutti, Michael W. Mahoney

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Early stopping and non-parametric regression: an optimal data-dependent stopping rule.

Garvesh Raskutti, Martin J. Wainwright, Bin Yu

J. Mach. Learn. Res., 2014

2013

Learning directed acyclic graphs based on sparsest permutations.

Garvesh Raskutti, Caroline Uhler

CoRR, 2013

2012

Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming.

Garvesh Raskutti, Martin J. Wainwright, Bin Yu

J. Mach. Learn. Res., 2012

2011

Minimax Rates of Estimation for High-Dimensional Linear Regression Over _q -Balls.

Garvesh Raskutti, Martin J. Wainwright, Bin Yu

IEEE Trans. Inf. Theory, 2011

Early stopping for non-parametric regression: An optimal data-dependent stopping rule.

Garvesh Raskutti, Martin J. Wainwright, Bin Yu

Proceedings of the 49th Annual Allerton Conference on Communication, 2011

2010

Restricted Eigenvalue Properties for Correlated Gaussian Designs.

Garvesh Raskutti, Martin J. Wainwright, Bin Yu

J. Mach. Learn. Res., 2010

2009

Minimax rates of estimation for high-dimensional linear regression over $\ell_q$-balls.

Garvesh Raskutti, Martin J. Wainwright, Bin Yu

CoRR, 2009

Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness.

Garvesh Raskutti, Martin J. Wainwright, Bin Yu

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Minimax rates of convergence for high-dimensional regression under ℓq-ball sparsity.

Garvesh Raskutti, Martin J. Wainwright, Bin Yu

Proceedings of the 47th Annual Allerton Conference on Communication, 2009

2008

Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of l₁-regularized MLE.

Pradeep Ravikumar, Garvesh Raskutti, Martin J. Wainwright, Bin Yu

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

2007

Enhanced Blocking Probability Evaluation Method for Circuit-Switched Trunk Reservation Networks.

Garvesh Raskutti, Andrew Zalesky, Eric W. M. Wong, Moshe Zukerman

IEEE Commun. Lett., 2007

Blocking Probability Estimation for Trunk Reservation Networks.

Garvesh Raskutti, Andrew Zalesky, Eric Wing Ming Wong, Moshe Zukerman

Proceedings of IEEE International Conference on Communications, 2007