Blocking Probability Estimation for Trunk Reservation Networks. - Garvesh Raskutti; Andrew Zalesky; Eric Wing Ming Wong; Moshe Zukerman (ICC, 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)

Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of l1-regularized MLE. - Pradeep Ravikumar; Garvesh Raskutti; Martin J. Wainwright; Bin Yu 001 (NIPS, 2008)

Minimax rates of convergence for high-dimensional regression under ℓq-ball sparsity. - Garvesh Raskutti; Martin J. Wainwright; Bin Yu 001 (Allerton, 2009)

Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness. - Garvesh Raskutti; Martin J. Wainwright; Bin Yu 001 (NIPS, 2009)

Minimax rates of estimation for high-dimensional linear regression over $ ell_q$-balls. - Garvesh Raskutti; Martin J. Wainwright; Bin Yu 001 (CoRR, 2009)

Restricted Eigenvalue Properties for Correlated Gaussian Designs. - Garvesh Raskutti; Martin J. Wainwright; Bin Yu 001 (J. Mach. Learn. Res., 2010)

Early stopping for non-parametric regression: An optimal data-dependent stopping rule. - Garvesh Raskutti; Martin J. Wainwright; Bin Yu 001 (Allerton, 2011)

Minimax Rates of Estimation for High-Dimensional Linear Regression Over q -Balls. - Garvesh Raskutti; Martin J. Wainwright; Bin Yu 001 (IEEE Trans. Inf. Theory, 2011)

Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming. - Garvesh Raskutti; Martin J. Wainwright; Bin Yu 001 (J. Mach. Learn. Res., 2012)

Learning directed acyclic graphs based on sparsest permutations. - Garvesh Raskutti; Caroline Uhler (CoRR, 2013)

Early stopping and non-parametric regression: an optimal data-dependent stopping rule. - Garvesh Raskutti; Martin J. Wainwright; Bin Yu 001 (J. Mach. Learn. Res., 2014)

Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares. - Garvesh Raskutti; Michael W. Mahoney (ICML, 2015)

Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring. - Gunwoong Park; Garvesh Raskutti (NIPS, 2015)

Minimax Optimal Rates for Poisson Inverse Problems With Physical Constraints. - Xin Jiang Hunt; Garvesh Raskutti; Rebecca Willett (IEEE Trans. Inf. Theory, 2015)

The Information Geometry of Mirror Descent. - Garvesh Raskutti; Sayan Mukherjee 001 (IEEE Trans. Inf. Theory, 2015)

Inferring high-dimensional poisson autoregressive models. - Eric C. Hall; Garvesh Raskutti; Rebecca Willett (SSP, 2016)

Inference of High-dimensional Autoregressive Generalized Linear Models. - Eric C. Hall; Garvesh Raskutti; Rebecca Willett (CoRR, 2016)

Identifiability assumptions for directed graphical models with feedback. - Gunwoong Park; Garvesh Raskutti (CoRR, 2016)

A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares. - Garvesh Raskutti; Michael W. Mahoney (J. Mach. Learn. Res., 2016)

Network estimation via poisson autoregressive models. - Benjamin Mark; Garvesh Raskutti; Rebecca Willett (CAMSAP, 2017)

Image inpainting using reproducing kernel Hilbert space and Heaviside functions. - Si Wang; Weihong Guo 002; Ting-Zhu Huang; Garvesh Raskutti (J. Comput. Appl. Math., 2017)

Learning Quadratic Variance Function (QVF) DAG Models via OverDispersion Scoring (ODS). - Gunwoong Park; Garvesh Raskutti (J. Mach. Learn. Res., 2017)

Graph-Based Regularization for Regression Problems with Highly-Correlated Designs. - Yuan Li 006; Benjamin Mark; Garvesh Raskutti; Rebecca Willett (GlobalSIP, 2018)

Graph-based regularization for regression problems with highly-correlated designs. - Yuan Li 006; Garvesh Raskutti; Rebecca Willett (CoRR, 2018)

Minimax Optimal Convex Methods for Poisson Inverse Problems Under ℓq-Ball Sparsity. - Yuan Li 006; Garvesh Raskutti (IEEE Trans. Inf. Theory, 2018)

Estimating Network Structure from Incomplete Event Data. - Benjamin Mark; Garvesh Raskutti; Rebecca Willett (AISTATS, 2019)

Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression. - Han Chen; Garvesh Raskutti; Ming Yuan 001 (J. Mach. Learn. Res., 2019)

Learning High-Dimensional Generalized Linear Autoregressive Models. - Eric C. Hall; Garvesh Raskutti; Rebecca M. Willett (IEEE Trans. Inf. Theory, 2019)

Network Estimation From Point Process Data. - Benjamin Mark; Garvesh Raskutti; Rebecca Willett (IEEE Trans. Inf. Theory, 2019)

Non-Parametric Sparse Additive Auto-Regressive Network Models. - Hao Henry Zhou; Garvesh Raskutti (IEEE Trans. Inf. Theory, 2019)

Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes. - Hao Chen 067; Lili Zheng; Raed Al Kontar; Garvesh Raskutti (NeurIPS, 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)

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)

Graph-Based Regularization for Regression Problems with Alignment and Highly Correlated Designs. - Yuan Li 006; Benjamin Mark; Garvesh Raskutti; Rebecca Willett; Hyebin Song; David Neiman (SIAM J. Math. Data Sci., 2020)

ISLET: Fast and Optimal Low-Rank Tensor Regression via Importance Sketching. - Anru R. Zhang; Yuetian Luo; Garvesh Raskutti; Ming Yuan 001 (SIAM J. Math. Data Sci., 2020)

Prediction in the Presence of Response-Dependent Missing Labels. - Hyebin Song; Garvesh Raskutti; Rebecca Willett (SSP, 2021)

The Internet of Federated Things (IoFT). - Raed Kontar; Naichen Shi; Xubo Yue; Seokhyun Chung; Eunshin Byon; Mosharaf Chowdhury; Jionghua Jin 001; Wissam Kontar; Neda Masoud; Maher Nouiehed; Chinedum Emmanuel Okwudire; Garvesh Raskutti; et al. (IEEE Access, 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): 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; et al. (CoRR, 2021)

Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits. - Hao Chen 067; Lili Zheng; Raed Al Kontar; Garvesh Raskutti (CoRR, 2021)

A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration. - Yuetian Luo; Garvesh Raskutti; Ming Yuan 001; Anru R. Zhang (J. Mach. Learn. Res., 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)

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)

Lazy Estimation of Variable Importance for Large Neural Networks. - Yue Gao; Abby Stevens; Garvesh Raskutti; Rebecca Willett (ICML, 2022)

Gaussian Process Parameter Estimation Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits. - Hao Chen 067; Lili Zheng; Raed Al Kontar; Garvesh Raskutti (J. Mach. Learn. Res., 2022)

Fast, Distribution-free Predictive Inference for Neural Networks with Coverage Guarantees. - Yue Gao; Garvesh Raskutti; Rebecca Willett (CoRR, 2023)

Reliable and Scalable Variable Importance Estimation via Warm-start and Early Stopping. - Zexuan Sun; Garvesh Raskutti (AISTATS, 2025)