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Garvesh Raskutti

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

Jun 11, 2023
Yue Gao, Garvesh Raskutti, Rebecca Willet

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Lazy Estimation of Variable Importance for Large Neural Networks

Jul 19, 2022
Yue Gao, Abby Stevens, Rebecca Willet, Garvesh Raskutti

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

Nov 19, 2021
Hao Chen, Lili Zheng, Raed Al Kontar, Garvesh Raskutti

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

Nov 09, 2021
Raed Kontar, Naichen Shi, Xubo Yue, Seokhyun Chung, Eunshin Byon, Mosharaf Chowdhury, Judy Jin, Wissam Kontar, Neda Masoud, Maher Noueihed, Chinedum E. Okwudire, Garvesh Raskutti, Romesh Saigal, Karandeep Singh, Zhisheng Ye

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

Jun 29, 2021
Yue Gao, Garvesh Raskutti

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Prediction in the presence of response-dependent missing labels

Mar 25, 2021
Hyebin Song, Garvesh Raskutti, Rebecca Willett

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A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration

Aug 06, 2020
Yuetian Luo, Garvesh Raskutti, Ming Yuan, Anru R. Zhang

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

Mar 16, 2020
Lili Zheng, Garvesh Raskutti, Rebecca Willett, Benjamin Mark

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

Nov 09, 2019
Anru Zhang, Yuetian Luo, Garvesh Raskutti, Ming Yuan

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

Mar 31, 2019
Raed Kontar, Garvesh Raskutti, Shiyu Zhou

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