Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Gauraha, Niharika"'
Autor:
Gauraha, Niharika
High-dimensional state trajectories of state-space models pose challenges for Bayesian inference. Particle Gibbs (PG) methods have been widely used to sample from the posterior of a state space model. Basically, particle Gibbs is a Particle Markov Ch
Externí odkaz:
http://arxiv.org/abs/2007.15862
Conformal Prediction is a framework that produces prediction intervals based on the output from a machine learning algorithm. In this paper we explore the case when training data is made up of multiple parts available in different sources that cannot
Externí odkaz:
http://arxiv.org/abs/1908.05571
Autor:
Gauraha, Niharika, Chaturvedi, Akshay
To estimate the conditional probability functions based on the direct problem setting, V-matrix based method was proposed. We construct V-matrix based constrained quadratic programming problems for which the inequality constraints are inconsistent. I
Externí odkaz:
http://arxiv.org/abs/1809.01706
Conformal Prediction is a machine learning methodology that produces valid prediction regions under mild conditions. In this paper, we explore the application of making predictions over multiple data sources of different sizes without disclosing data
Externí odkaz:
http://arxiv.org/abs/1806.04000
Autor:
Gauraha, Niharika, Spjuth, Ola
The conformalClassification package implements Transductive Conformal Prediction (TCP) and Inductive Conformal Prediction (ICP) for classification problems. Conformal Prediction (CP) is a framework that complements the predictions of machine learning
Externí odkaz:
http://arxiv.org/abs/1804.05494
This paper explores conformal prediction in the learning under privileged information (LUPI) paradigm. We use the SVM+ realization of LUPI in an inductive conformal predictor, and apply it to the MNIST benchmark dataset and three datasets in drug dis
Externí odkaz:
http://arxiv.org/abs/1803.11136
Autor:
Gauraha, Niharika
We consider the problem of model selection and estimation in sparse high dimensional linear regression models with strongly correlated variables. First, we study the theoretical properties of the dual Lasso solution, and we show that joint considerat
Externí odkaz:
http://arxiv.org/abs/1703.06602
Autor:
Gauraha, Niharika
We study various constraints and conditions on the true coefficient vector and on the design matrix to establish non-asymptotic oracle inequalities for the prediction error, estimation accuracy and variable selection for the Lasso estimator in high d
Externí odkaz:
http://arxiv.org/abs/1603.06177
Autor:
Gauraha, Niharika
We present a comprehensive study of graphical log-linear models for contingency tables. High dimensional contingency tables arise in many areas such as computational biology, collection of survey and census data and others. Analysis of contingency ta
Externí odkaz:
http://arxiv.org/abs/1603.04122
Autor:
Gauraha, Niharika, Parui, Swapan K.
In this paper, we introduce Adaptive Cluster Lasso(ACL) method for variable selection in high dimensional sparse regression models with strongly correlated variables. To handle correlated variables, the concept of clustering or grouping variables and
Externí odkaz:
http://arxiv.org/abs/1603.03724