Garvesh Raskutti

62 papers A* 3A 2Journal 46Unranked 11
YearRankTypeTitle / Venue / Authors
2026 J jnl
A Theoretical Framework for LLM Fine-tuning Using Early Stopping for Non-random Initialization.
CoRR
Zexuan Sun, Garvesh Raskutti
2025 J jnl
Comparing Model-agnostic Feature Selection Methods through Relative Efficiency.
CoRR
Chenghui Zheng, Garvesh Raskutti
2025 A conf
Reliable and Scalable Variable Importance Estimation via Warm-start and Early Stopping.
AISTATS
Zexuan Sun, Garvesh Raskutti
2024 J jnl
Reliable and scalable variable importance estimation via warm-start and early stopping.
CoRR
Zexuan Sun, Garvesh Raskutti
2023 J jnl
Fast, Distribution-free Predictive Inference for Neural Networks with Coverage Guarantees.
CoRR
Yue Gao, Garvesh Raskutti, Rebecca Willett
2022 J jnl
Gaussian Process Parameter Estimation Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits.
J. Mach. Learn. Res.
Hao Chen, Lili Zheng, Raed Al Kontar, Garvesh Raskutti
2022 A* conf
Lazy Estimation of Variable Importance for Large Neural Networks.
ICML
Yue Gao, Abby Stevens, Garvesh Raskutti, Rebecca Willett
2022 J jnl
Lazy Estimation of Variable Importance for Large Neural Networks.
CoRR
Yue Gao, Abby Stevens, Rebecca Willett, Garvesh Raskutti
2021 J jnl
A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration.
J. Mach. Learn. Res.
Yuetian Luo, Garvesh Raskutti, Ming Yuan, Anru R. Zhang
2021 J jnl
Context-dependent Networks in Multivariate Time Series: Models, Methods, and Risk Bounds in High Dimensions.
J. Mach. Learn. Res.
Lili Zheng, Garvesh Raskutti, Rebecca Willett, Benjamin Mark
2021 J jnl
Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits.
CoRR
Hao Chen, Lili Zheng, Raed Al Kontar, Garvesh Raskutti
2021 J jnl
Improved Prediction and Network Estimation Using the Monotone Single Index Multi-variate Autoregressive Model.
CoRR
Yue Gao, Garvesh Raskutti
2021 J jnl
Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach.
IEEE Trans. Pattern Anal. Mach. Intell.
Raed Kontar, Garvesh Raskutti, Shiyu Zhou
2021 — conf
Prediction in the Presence of Response-Dependent Missing Labels.
SSP
Hyebin Song, Garvesh Raskutti, Rebecca Willett
2021 J jnl
Prediction in the presence of response-dependent missing labels.
CoRR
Hyebin Song, Garvesh Raskutti, Rebecca Willett
2021 J jnl
The Internet of Federated Things (IoFT).
IEEE Access
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
2021 J jnl
The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning.
CoRR
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
2020 J jnl
A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration.
CoRR
Yuetian Luo, Garvesh Raskutti, Ming Yuan, Anru R. Zhang
2020 J jnl
Context-dependent self-exciting point processes: models, methods, and risk bounds in high dimensions.
CoRR
Lili Zheng, Garvesh Raskutti, Rebecca Willett, Benjamin Mark
2020 J jnl
Convex and Non-Convex Approaches for Statistical Inference with Class-Conditional Noisy Labels.
J. Mach. Learn. Res.
Hyebin Song, Ran Dai, Garvesh Raskutti, Rina Foygel Barber
2020 J jnl
Graph-Based Regularization for Regression Problems with Alignment and Highly Correlated Designs.
SIAM J. Math. Data Sci.
Yuan Li, Benjamin Mark, Garvesh Raskutti, Rebecca Willett, Hyebin Song, David Neiman
2020 J jnl
ISLET: Fast and Optimal Low-Rank Tensor Regression via Importance Sketching.
SIAM J. Math. Data Sci.
Anru R. Zhang, Yuetian Luo, Garvesh Raskutti, Ming Yuan
2020 A* conf
Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes.
NeurIPS
Hao Chen, Lili Zheng, Raed Al Kontar, Garvesh Raskutti
2019 A conf
Estimating Network Structure from Incomplete Event Data.
AISTATS
Benjamin Mark, Garvesh Raskutti, Rebecca Willett
2019 J jnl
ISLET: Fast and Optimal Low-rank Tensor Regression via Importance Sketching.
CoRR
Anru Zhang, Yuetian Luo, Garvesh Raskutti, Ming Yuan
2019 J jnl
Learning High-Dimensional Generalized Linear Autoregressive Models.
IEEE Trans. Inf. Theory
Eric C. Hall, Garvesh Raskutti, Rebecca M. Willett
2019 J jnl
Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach.
CoRR
Raed Kontar, Garvesh Raskutti, Shiyu Zhou
2019 J jnl
Network Estimation From Point Process Data.
IEEE Trans. Inf. Theory
Benjamin Mark, Garvesh Raskutti, Rebecca Willett
2019 J jnl
Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression.
J. Mach. Learn. Res.
Han Chen, Garvesh Raskutti, Ming Yuan
2019 J jnl
Non-Parametric Sparse Additive Auto-Regressive Network Models.
IEEE Trans. Inf. Theory
Hao Henry Zhou, Garvesh Raskutti
2018 J jnl
Estimating Network Structure from Incomplete Event Data.
CoRR
Benjamin Mark, Garvesh Raskutti, Rebecca Willett
2018 — conf
Graph-Based Regularization for Regression Problems with Highly-Correlated Designs.
GlobalSIP
Yuan Li, Benjamin Mark, Garvesh Raskutti, Rebecca Willett
2018 J jnl
Graph-based regularization for regression problems with highly-correlated designs.
CoRR
Yuan Li, Garvesh Raskutti, Rebecca Willett
2018 J jnl
Minimax Optimal Convex Methods for Poisson Inverse Problems Under ℓ
IEEE Trans. Inf. Theory
Yuan Li, Garvesh Raskutti
2018 J jnl
Network Estimation from Point Process Data.
CoRR
Benjamin Mark, Garvesh Raskutti, Rebecca Willett
2017 J jnl
Image inpainting using reproducing kernel Hilbert space and Heaviside functions.
J. Comput. Appl. Math.
Si Wang, Weihong Guo, Ting-Zhu Huang, Garvesh Raskutti
2017 J jnl
Learning Quadratic Variance Function (QVF) DAG Models via OverDispersion Scoring (ODS).
J. Mach. Learn. Res.
Gunwoong Park, Garvesh Raskutti
2017 J jnl
Learning Quadratic Variance Function (QVF) DAG models via OverDispersion Scoring (ODS).
CoRR
Gunwoong Park, Garvesh Raskutti
2017 — conf
Network estimation via poisson autoregressive models.
CAMSAP
Benjamin Mark, Garvesh Raskutti, Rebecca Willett
2016 J jnl
A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares.
J. Mach. Learn. Res.
Garvesh Raskutti, Michael W. Mahoney
2016 J jnl
Identifiability assumptions for directed graphical models with feedback.
CoRR
Gunwoong Park, Garvesh Raskutti
2016 J jnl
Inference of High-dimensional Autoregressive Generalized Linear Models.
CoRR
Eric C. Hall, Garvesh Raskutti, Rebecca Willett
2016 — conf
Inferring high-dimensional poisson autoregressive models.
SSP
Eric C. Hall, Garvesh Raskutti, Rebecca Willett
2015 — conf
Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring.
NIPS
Gunwoong Park, Garvesh Raskutti
2015 J jnl
Minimax Optimal Rates for Poisson Inverse Problems With Physical Constraints.
IEEE Trans. Inf. Theory
Xin Jiang Hunt, Garvesh Raskutti, Rebecca Willett
2015 A* conf
Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares.
ICML
Garvesh Raskutti, Michael W. Mahoney
2015 — conf
The Information Geometry of Mirror Descent.
GSI
Garvesh Raskutti, Sayan Mukherjee
2015 J jnl
The Information Geometry of Mirror Descent.
IEEE Trans. Inf. Theory
Garvesh Raskutti, Sayan Mukherjee
2014 J jnl
Early stopping and non-parametric regression: an optimal data-dependent stopping rule.
J. Mach. Learn. Res.
Garvesh Raskutti, Martin J. Wainwright, Bin Yu
2013 J jnl
Learning directed acyclic graphs based on sparsest permutations.
CoRR
Garvesh Raskutti, Caroline Uhler
2013 J jnl
The Information Geometry of Mirror Descent.
CoRR
Garvesh Raskutti, Sayan Mukherjee
2012 J jnl
Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming.
J. Mach. Learn. Res.
Garvesh Raskutti, Martin J. Wainwright, Bin Yu
2011 — conf
Early stopping for non-parametric regression: An optimal data-dependent stopping rule.
Allerton
Garvesh Raskutti, Martin J. Wainwright, Bin Yu
2011 J jnl
Minimax Rates of Estimation for High-Dimensional Linear Regression Over
IEEE Trans. Inf. Theory
Garvesh Raskutti, Martin J. Wainwright, Bin Yu
2010 J jnl
Minimax-optimal rates for sparse additive models over kernel classes via convex programming.
CoRR
Garvesh Raskutti, Martin J. Wainwright, Bin Yu
2010 J jnl
Restricted Eigenvalue Properties for Correlated Gaussian Designs.
J. Mach. Learn. Res.
Garvesh Raskutti, Martin J. Wainwright, Bin Yu
2009 — conf
Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness.
NIPS
Garvesh Raskutti, Martin J. Wainwright, Bin Yu
2009 — conf
Minimax rates of convergence for high-dimensional regression under ℓq-ball sparsity.
Allerton
Garvesh Raskutti, Martin J. Wainwright, Bin Yu
2009 J jnl
Minimax rates of estimation for high-dimensional linear regression over $\ell_q$-balls.
CoRR
Garvesh Raskutti, Martin J. Wainwright, Bin Yu
2008 — conf
Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of l
NIPS
Pradeep Ravikumar, Garvesh Raskutti, Martin J. Wainwright, Bin Yu
2007 — conf
Blocking Probability Estimation for Trunk Reservation Networks.
ICC
Garvesh Raskutti, Andrew Zalesky, Eric Wing Ming Wong, Moshe Zukerman
2007 J jnl
Enhanced Blocking Probability Evaluation Method for Circuit-Switched Trunk Reservation Networks.
IEEE Commun. Lett.
Garvesh Raskutti, Andrew Zalesky, Eric W. M. Wong, Moshe Zukerman