2 citations · 7 across the 12 of their papers we have counts for
13 papers
MergeLLL: A Hierarchical Divide-and-Conquer Framework for LLL-Based Lattice Reduction
Niharika Gauraha
Lattice basis reduction algorithms have various applications in computational number theory and lattice-based cryptography, but their complexity increases rapidly with the dimensio…
A Note on Particle Gibbs Method and its Extensions and Variants
Niharika Gauraha
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…
Combining Prediction Intervals on Multi-Source Non-Disclosed Regression Datasets
Ola Spjuth, Robin Carrión Brännström, Lars Carlsson +1
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 i…
A Limitation of V-Matrix based Methods
Niharika Gauraha, Akshay Chaturvedi
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 program…
Aggregating Predictions on Multiple Non-disclosed Datasets using Conformal Prediction
Ola Spjuth, Lars Carlsson, Niharika Gauraha
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…
conformalClassification: A Conformal Prediction R Package for Classification
Niharika Gauraha, Ola Spjuth
The conformalClassification package implements Transductive Conformal Prediction (TCP) and Inductive Conformal Prediction (ICP) for classification problems. Conformal Prediction (C…
Conformal Prediction in Learning Under Privileged Information Paradigm with Applications in Drug Discovery
Niharika Gauraha, Lars Carlsson, Ola Spjuth
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…
Dual Lasso Selector
Niharika Gauraha
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 pr…
Graphical Log-linear Models: Fundamental Concepts and Applications
Niharika Gauraha
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, colle…
Mutual Conditional Independence and its Applications to Inference in Markov Networks
Niharika Gauraha
The fundamental concepts underlying in Markov networks are the conditional independence and the set of rules called Markov properties that translates conditional independence const…
Efficient Clustering of Correlated Variables and Variable Selection in High-Dimensional Linear Models
Niharika Gauraha, Swapan K. Parui
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 cor…
Model Selection for Graphical Log-linear Models: A Forward Model Selection Algorithm based on Mutual Conditional Independence
Niharika Gauraha
Model selection and learning the structure of graphical models from the data sample constitutes an important field of probabilistic graphical model research, as in most of the situ…
Constraints and Conditions: the Lasso Oracle-inequalities
Niharika Gauraha
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, estima…