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ORCIDGarvesh Raskutti
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2020 – today
- 2023
[i20]Yue Gao, Garvesh Raskutti, Rebecca Willett:
Fast, Distribution-free Predictive Inference for Neural Networks with Coverage Guarantees. CoRR abs/2306.06582 (2023)- 2022
- [j23]Hao Chen, Lili Zheng, Raed Al Kontar, Garvesh Raskutti:
Gaussian Process Parameter Estimation Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits. J. Mach. Learn. Res. 23: 227:1-227:59 (2022) - [c15]Yue Gao
, Abby Stevens, Garvesh Raskutti, Rebecca Willett:
Lazy Estimation of Variable Importance for Large Neural Networks. ICML 2022: 7122-7143 - [i19]Yue Gao, Abby Stevens, Rebecca Willett, Garvesh Raskutti:
Lazy Estimation of Variable Importance for Large Neural Networks. CoRR abs/2207.09097 (2022) - 2021
- [j22]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:
The Internet of Federated Things (IoFT). IEEE Access 9: 156071-156113 (2021) - [j21]Yuetian Luo, Garvesh Raskutti, Ming Yuan, Anru R. Zhang:
A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration. J. Mach. Learn. Res. 22: 179:1-179:48 (2021) - [j20]Lili Zheng, Garvesh Raskutti, Rebecca Willett, Benjamin Mark:
Context-dependent Networks in Multivariate Time Series: Models, Methods, and Risk Bounds in High Dimensions. J. Mach. Learn. Res. 22: 216:1-216:88 (2021) - [j19]Raed Kontar, Garvesh Raskutti, Shiyu Zhou:
Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach. IEEE Trans. Pattern Anal. Mach. Intell. 43(10): 3508-3522 (2021) - [c14]Hyebin Song, Garvesh Raskutti, Rebecca Willett:
Prediction in the Presence of Response-Dependent Missing Labels. SSP 2021: 451-455 - [i18]Hyebin Song, Garvesh Raskutti, Rebecca Willett:
Prediction in the presence of response-dependent missing labels. CoRR abs/2103.13555 (2021) - [i17]Yue Gao, Garvesh Raskutti:
Improved Prediction and Network Estimation Using the Monotone Single Index Multi-variate Autoregressive Model. CoRR abs/2106.14630 (2021) - [i16]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:
The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning. CoRR abs/2111.05326 (2021) - [i15]Hao Chen, Lili Zheng, Raed Al Kontar, Garvesh Raskutti:
Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits. CoRR abs/2111.10461 (2021) - 2020
- [j18]Hyebin Song, Ran Dai, Garvesh Raskutti, Rina Foygel Barber:
Convex and Non-Convex Approaches for Statistical Inference with Class-Conditional Noisy Labels. J. Mach. Learn. Res. 21: 168:1-168:58 (2020) - [j17]Anru R. Zhang, Yuetian Luo, Garvesh Raskutti, Ming Yuan:
ISLET: Fast and Optimal Low-Rank Tensor Regression via Importance Sketching. SIAM J. Math. Data Sci. 2(2): 444-479 (2020) - [j16]Yuan Li, Benjamin Mark, Garvesh Raskutti, Rebecca Willett, Hyebin Song, David Neiman:
Graph-Based Regularization for Regression Problems with Alignment and Highly Correlated Designs. SIAM J. Math. Data Sci. 2(2): 480-504 (2020) - [c13]Hao Chen, Lili Zheng, Raed Al Kontar, Garvesh Raskutti:
Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes. NeurIPS 2020 - [i14]Lili Zheng, Garvesh Raskutti, Rebecca Willett, Benjamin Mark:
Context-dependent self-exciting point processes: models, methods, and risk bounds in high dimensions. CoRR abs/2003.07429 (2020) - [i13]Yuetian Luo, Garvesh Raskutti, Ming Yuan, Anru R. Zhang:
A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration. CoRR abs/2008.02437 (2020)
2010 – 2019
- 2019
- [j15]Han Chen, Garvesh Raskutti, Ming Yuan:
Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression. J. Mach. Learn. Res. 20: 5:1-5:37 (2019) - [j14]Hao Henry Zhou, Garvesh Raskutti:
Non-Parametric Sparse Additive Auto-Regressive Network Models. IEEE Trans. Inf. Theory 65(3): 1473-1492 (2019) - [j13]Eric C. Hall, Garvesh Raskutti, Rebecca M. Willett:
Learning High-Dimensional Generalized Linear Autoregressive Models. IEEE Trans. Inf. Theory 65(4): 2401-2422 (2019) - [j12]Benjamin Mark, Garvesh Raskutti, Rebecca Willett:
Network Estimation From Point Process Data. IEEE Trans. Inf. Theory 65(5): 2953-2975 (2019) - [c12]Benjamin Mark, Garvesh Raskutti, Rebecca Willett:
Estimating Network Structure from Incomplete Event Data. AISTATS 2019: 2535-2544 - [i12]Raed Kontar, Garvesh Raskutti, Shiyu Zhou:
Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach. CoRR abs/1901.11512 (2019) - [i11]Anru Zhang, Yuetian Luo, Garvesh Raskutti, Ming Yuan:
ISLET: Fast and Optimal Low-rank Tensor Regression via Importance Sketching. CoRR abs/1911.03804 (2019) - 2018
- [j11]Yuan Li, Garvesh Raskutti:
Minimax Optimal Convex Methods for Poisson Inverse Problems Under ℓq-Ball Sparsity. IEEE Trans. Inf. Theory 64(8): 5498-5512 (2018) - [c11]Yuan Li, Benjamin Mark, Garvesh Raskutti, Rebecca Willett:
Graph-Based Regularization for Regression Problems with Highly-Correlated Designs. GlobalSIP 2018: 740-742 - [i10]Benjamin Mark, Garvesh Raskutti, Rebecca Willett:
Network Estimation from Point Process Data. CoRR abs/1802.04838 (2018) - [i9]Yuan Li, Garvesh Raskutti, Rebecca Willett:
Graph-based regularization for regression problems with highly-correlated designs. CoRR abs/1803.07658 (2018) - [i8]Benjamin Mark, Garvesh Raskutti, Rebecca Willett:
Estimating Network Structure from Incomplete Event Data. CoRR abs/1811.02979 (2018) - 2017
- [j10]Si Wang, Weihong Guo, Ting-Zhu Huang, Garvesh Raskutti:
Image inpainting using reproducing kernel Hilbert space and Heaviside functions. J. Comput. Appl. Math. 311: 551-564 (2017) - [j9]Gunwoong Park, Garvesh Raskutti:
Learning Quadratic Variance Function (QVF) DAG Models via OverDispersion Scoring (ODS). J. Mach. Learn. Res. 18: 224:1-224:44 (2017) - [c10]Benjamin Mark, Garvesh Raskutti, Rebecca Willett:
Network estimation via poisson autoregressive models. CAMSAP 2017: 1-5 - [i7]Gunwoong Park, Garvesh Raskutti:
Learning Quadratic Variance Function (QVF) DAG models via OverDispersion Scoring (ODS). CoRR abs/1704.08783 (2017) - 2016
- [j8]Garvesh Raskutti, Michael W. Mahoney:
A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares. J. Mach. Learn. Res. 17: 214:1-214:31 (2016) - [c9]Eric C. Hall, Garvesh Raskutti, Rebecca Willett:
Inferring high-dimensional poisson autoregressive models. SSP 2016: 1-5 - [i6]Gunwoong Park, Garvesh Raskutti:
Identifiability assumptions for directed graphical models with feedback. CoRR abs/1602.04418 (2016) - [i5]Eric C. Hall, Garvesh Raskutti, Rebecca Willett:
Inference of High-dimensional Autoregressive Generalized Linear Models. CoRR abs/1605.02693 (2016) - 2015
- [j7]Garvesh Raskutti, Sayan Mukherjee:
The Information Geometry of Mirror Descent. IEEE Trans. Inf. Theory 61(3): 1451-1457 (2015) - [j6]Xin Jiang Hunt, Garvesh Raskutti, Rebecca Willett:
Minimax Optimal Rates for Poisson Inverse Problems With Physical Constraints. IEEE Trans. Inf. Theory 61(8): 4458-4474 (2015) - [c8]Garvesh Raskutti, Sayan Mukherjee:
The Information Geometry of Mirror Descent. GSI 2015: 359-368 - [c7]Garvesh Raskutti, Michael W. Mahoney:
Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares. ICML 2015: 617-625 - [c6]Gunwoong Park, Garvesh Raskutti:
Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring. NIPS 2015: 631-639 - 2014
- [j5]Garvesh Raskutti, Martin J. Wainwright, Bin Yu:
Early stopping and non-parametric regression: an optimal data-dependent stopping rule. J. Mach. Learn. Res. 15(1): 335-366 (2014) - 2013
- [i4]Garvesh Raskutti, Caroline Uhler:
Learning directed acyclic graphs based on sparsest permutations. CoRR abs/1307.0366 (2013) - [i3]Garvesh Raskutti, Sayan Mukherjee:
The Information Geometry of Mirror Descent. CoRR abs/1310.7780 (2013) - 2012
- [j4]Garvesh Raskutti, Martin J. Wainwright, Bin Yu:
Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming. J. Mach. Learn. Res. 13: 389-427 (2012) - 2011
- [j3]Garvesh Raskutti, Martin J. Wainwright, Bin Yu:
Minimax Rates of Estimation for High-Dimensional Linear Regression Over q -Balls. IEEE Trans. Inf. Theory 57(10): 6976-6994 (2011) - [c5]Garvesh Raskutti, Martin J. Wainwright, Bin Yu:
Early stopping for non-parametric regression: An optimal data-dependent stopping rule. Allerton 2011: 1318-1325 - 2010
- [j2]Garvesh Raskutti, Martin J. Wainwright, Bin Yu:
Restricted Eigenvalue Properties for Correlated Gaussian Designs. J. Mach. Learn. Res. 11: 2241-2259 (2010) - [i2]Garvesh Raskutti, Martin J. Wainwright, Bin Yu:
Minimax-optimal rates for sparse additive models over kernel classes via convex programming. CoRR abs/1008.3654 (2010)
2000 – 2009
- 2009
- [c4]Garvesh Raskutti, Martin J. Wainwright, Bin Yu:
Minimax rates of convergence for high-dimensional regression under ℓq-ball sparsity. Allerton 2009: 251-257 - [c3]Garvesh Raskutti, Martin J. Wainwright, Bin Yu:
Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness. NIPS 2009: 1563-1570 - [i1]Garvesh Raskutti, Martin J. Wainwright, Bin Yu:
Minimax rates of estimation for high-dimensional linear regression over $\ell_q$-balls. CoRR abs/0910.2042 (2009) - 2008
- [c2]Pradeep Ravikumar, Garvesh Raskutti, Martin J. Wainwright, Bin Yu:
Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of l1-regularized MLE. NIPS 2008: 1329-1336 - 2007
- [j1]Garvesh Raskutti, Andrew Zalesky, Eric W. M. Wong, Moshe Zukerman:
Enhanced Blocking Probability Evaluation Method for Circuit-Switched Trunk Reservation Networks. IEEE Commun. Lett. 11(6): 543-545 (2007) - [c1]Garvesh Raskutti, Andrew Zalesky, Eric Wing Ming Wong, Moshe Zukerman:
Blocking Probability Estimation for Trunk Reservation Networks. ICC 2007: 223-228
Coauthor Index
aka: Rebecca M. Willett
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