Zobrazeno 1 - 10
of 172
pro vyhledávání: '"Raskutti, Garvesh"'
This paper introduces a novel, computationally-efficient algorithm for predictive inference (PI) that requires no distributional assumptions on the data and can be computed faster than existing bootstrap-type methods for neural networks. Specifically
Externí odkaz:
http://arxiv.org/abs/2306.06582
Autor:
Zheng, Lili, Raskutti, Garvesh
Classification with positive and unlabeled (PU) data frequently arises in bioinformatics, clinical data, and ecological studies, where collecting negative samples can be prohibitively expensive. While prior works on PU data focus on binary classifica
Externí odkaz:
http://arxiv.org/abs/2304.09305
As opaque predictive models increasingly impact many areas of modern life, interest in quantifying the importance of a given input variable for making a specific prediction has grown. Recently, there has been a proliferation of model-agnostic methods
Externí odkaz:
http://arxiv.org/abs/2207.09097
Publikováno v:
Journal of Machine learning Research (JMLR), 2022
Stochastic gradient descent (SGD) and its variants have established themselves as the go-to algorithms for large-scale machine learning problems with independent samples due to their generalization performance and intrinsic computational advantage. H
Externí odkaz:
http://arxiv.org/abs/2111.10461
Autor:
Kontar, Raed, Shi, Naichen, Yue, Xubo, Chung, Seokhyun, Byon, Eunshin, Chowdhury, Mosharaf, Jin, Judy, Kontar, Wissam, Masoud, Neda, Noueihed, Maher, Okwudire, Chinedum E., Raskutti, Garvesh, Saigal, Romesh, Singh, Karandeep, Ye, Zhisheng
Publikováno v:
IEEE Access, 2021
The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the cloud will be substituted by the crowd where model training is brought to the edge, allowing IoT devices to collaboratively extract kno
Externí odkaz:
http://arxiv.org/abs/2111.05326
Autor:
Gao, Yue, Raskutti, Garvesh
Network estimation from multi-variate point process or time series data is a problem of fundamental importance. Prior work has focused on parametric approaches that require a known parametric model, which makes estimation procedures less robust to mo
Externí odkaz:
http://arxiv.org/abs/2106.14630
We consider a high-dimensional monotone single index model (hdSIM), which is a semiparametric extension of a high-dimensional generalize linear model (hdGLM), where the link function is unknown, but constrained with monotone and non-decreasing shape.
Externí odkaz:
http://arxiv.org/abs/2105.07587
In a variety of settings, limitations of sensing technologies or other sampling mechanisms result in missing labels, where the likelihood of a missing label in the training set is an unknown function of the data. For example, satellites used to detec
Externí odkaz:
http://arxiv.org/abs/2103.13555
In this paper, we develop novel perturbation bounds for the high-order orthogonal iteration (HOOI) [DLDMV00b]. Under mild regularity conditions, we establish blockwise tensor perturbation bounds for HOOI with guarantees for both tensor reconstruction
Externí odkaz:
http://arxiv.org/abs/2008.02437
Context-dependent self-exciting point processes: models, methods, and risk bounds in high dimensions
High-dimensional autoregressive point processes model how current events trigger or inhibit future events, such as activity by one member of a social network can affect the future activity of his or her neighbors. While past work has focused on estim
Externí odkaz:
http://arxiv.org/abs/2003.07429