Results 11 to 20 of about 400,613 (25)

A Tutorial on Probabilistic Latent Semantic Analysis [PDF]

arXiv, 2012
In this tutorial, I will discuss the details about how Probabilistic Latent Semantic Analysis (PLSA) is formalized and how different learning algorithms are proposed to learn the model.
arxiv  

Mathematical Perspective of Machine Learning [PDF]

arXiv, 2020
We take a closer look at some theoretical challenges of Machine Learning as a function approximation, gradient descent as the default optimization algorithm, limitations of fixed length and width networks and a different approach to RNNs from a mathematical perspective.
arxiv  

Ten-year Survival Prediction for Breast Cancer Patients [PDF]

arXiv, 2019
This report assesses different machine learning approaches to 10-year survival prediction of breast cancer patients.
arxiv  

Probabilistic Machine Learning for Healthcare [PDF]

arXiv, 2020
Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare.
arxiv  

Distributed Multitask Learning [PDF]

arXiv, 2015
We consider the problem of distributed multi-task learning, where each machine learns a separate, but related, task. Specifically, each machine learns a linear predictor in high-dimensional space,where all tasks share the same small support. We present a communication-efficient estimator based on the debiased lasso and show that it is comparable with ...
arxiv  

Distributed Stochastic Multi-Task Learning with Graph Regularization [PDF]

arXiv, 2018
We propose methods for distributed graph-based multi-task learning that are based on weighted averaging of messages from other machines. Uniform averaging or diminishing stepsize in these methods would yield consensus (single task) learning. We show how simply skewing the averaging weights or controlling the stepsize allows learning different, but ...
arxiv  

An Optimal Control View of Adversarial Machine Learning [PDF]

arXiv, 2018
I describe an optimal control view of adversarial machine learning, where the dynamical system is the machine learner, the input are adversarial actions, and the control costs are defined by the adversary's goals to do harm and be hard to detect. This view encompasses many types of adversarial machine learning, including test-item attacks, training ...
arxiv  

AutoCompete: A Framework for Machine Learning Competition [PDF]

arXiv, 2015
In this paper, we propose AutoCompete, a highly automated machine learning framework for tackling machine learning competitions. This framework has been learned by us, validated and improved over a period of more than two years by participating in online machine learning competitions.
arxiv  

Does data interpolation contradict statistical optimality? [PDF]

arXiv, 2018
We show that learning methods interpolating the training data can achieve optimal rates for the problems of nonparametric regression and prediction with square loss.
arxiv  

k-MLE, k-Bregman, k-VARs: Theory, Convergence, Computation [PDF]

arXiv
We develop hard clustering based on likelihood rather than distance and prove convergence. We also provide simulations and real data examples.
arxiv