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Garvesh 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
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
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
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
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
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
Non-Parametric Sparse Additive Auto-Regressive Network Models. IEEE Trans. Inf. Theory 65(3): 1473-1492 (2019) - [j13]Eric C. Hall
Learning High-Dimensional Generalized Linear Autoregressive Models. IEEE Trans. Inf. Theory 65(4): 2401-2422 (2019) - [j12]Benjamin Mark
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
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
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
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
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
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
Enhanced Blocking Probability Evaluation Method for Circuit-Switched Trunk Reservation Networks. IEEE Commun. Lett. 11(6): 543-545 (2007) - [c1]Garvesh Raskutti, Andrew Zalesky
Blocking Probability Estimation for Trunk Reservation Networks. ICC 2007: 223-228
Coauthor Index
aka: Rebecca M. Willett
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