Search Results for author: Gregory F. Cooper

Found 19 papers, 0 papers with code

Online Transfer Learning for RSV Case Detection

no code implementations3 Feb 2024 Yiming Sun, Yuhe Gao, Runxue Bao, Gregory F. Cooper, Jessi Espino, Harry Hochheiser, Marian G. Michaels, John M. Aronis, Ye Ye

To address this challenge, we introduce Predictive Volume-Adaptive Weighting (PVAW), a novel online multi-source transfer learning method.

Transfer Learning

The m-connecting imset and factorization for ADMG models

no code implementations18 Jul 2022 Bryan Andrews, Gregory F. Cooper, Thomas S. Richardson, Peter Spirtes

The m-connecting imset and factorization criterion provide two new statistical tools for learning and inference with ADMG models.

Learning Latent Causal Structures with a Redundant Input Neural Network

no code implementations29 Mar 2020 Jonathan D. Young, Bryan Andrews, Gregory F. Cooper, Xinghua Lu

We developed a deep learning model, which we call a redundant input neural network (RINN), with a modified architecture and a regularized objective function to find causal relationships between input, hidden, and output variables.

Causal Discovery

Obtaining Accurate Probabilistic Causal Inference by Post-Processing Calibration

no code implementations22 Dec 2017 Fattaneh Jabbari, Mahdi Pakdaman Naeini, Gregory F. Cooper

In this paper, we introduce a novel framework to derive calibrated probabilities of causal relationships from observational data.

Causal Inference

Binary Classifier Calibration using an Ensemble of Near Isotonic Regression Models

no code implementations16 Nov 2015 Mahdi Pakdaman Naeini, Gregory F. Cooper

The method can be considered as an extension of BBQ, a recently proposed calibration method, as well as the commonly used calibration method based on isotonic regression.

Binary Classification Classifier calibration +2

Counting Markov Blanket Structures

no code implementations9 Jul 2014 Shyam Visweswaran, Gregory F. Cooper

Learning Markov blanket (MB) structures has proven useful in performing feature selection, learning Bayesian networks (BNs), and discovering causal relationships.

feature selection

Binary Classifier Calibration: Non-parametric approach

no code implementations14 Jan 2014 Mahdi Pakdaman Naeini, Gregory F. Cooper, Milos Hauskrecht

We prove three theorems showing that using a simple histogram binning post-processing method, it is possible to make a classifier be well calibrated while retaining its discrimination capability.

Classifier calibration Decision Making +1

Binary Classifier Calibration: Bayesian Non-Parametric Approach

no code implementations13 Jan 2014 Mahdi Pakdaman Naeini, Gregory F. Cooper, Milos Hauskrecht

A set of probabilistic predictions is well calibrated if the events that are predicted to occur with probability p do in fact occur about p fraction of the time.

BIG-bench Machine Learning Binary Classification +1

KNET: Integrating Hypermedia and Bayesian Modeling

no code implementations27 Mar 2013 R. Martin Chavez, Gregory F. Cooper

KNET is a general-purpose shell for constructing expert systems based on belief networks and decision networks.

Management Retrieval

An Algorithm for Computing Probabilistic Propositions

no code implementations27 Mar 2013 Gregory F. Cooper

A method for computing probabilistic propositions is presented.

A Combination of Cutset Conditioning with Clique-Tree Propagation in the Pathfinder System

no code implementations27 Mar 2013 Jaap Suermondt, Gregory F. Cooper, David Heckerman

Cutset conditioning and clique-tree propagation are two popular methods for performing exact probabilistic inference in Bayesian belief networks.

Pathfinder

Bounded Conditioning: Flexible Inference for Decisions under Scarce Resources

no code implementations27 Mar 2013 Eric J. Horvitz, Jaap Suermondt, Gregory F. Cooper

We introduce a graceful approach to probabilistic inference called bounded conditioning.

A Method for Using Belief Networks as Influence Diagrams

no code implementations27 Mar 2013 Gregory F. Cooper

This paper demonstrates a method for using belief-network algorithms to solve influence diagram problems.

Stochastic Simulation of Bayesian Belief Networks

no code implementations27 Mar 2013 Homer L. Chin, Gregory F. Cooper

This paper examines Bayesian belief network inference using simulation as a method for computing the posterior probabilities of network variables.

Updating Probabilities in Multiply-Connected Belief Networks

no code implementations27 Mar 2013 Jaap Suermondt, Gregory F. Cooper

This paper focuses on probability updates in multiply-connected belief networks.

A Randomized Approximation Algorithm of Logic Sampling

no code implementations27 Mar 2013 R. Martin Chavez, Gregory F. Cooper

In recent years, researchers in decision analysis and artificial intelligence (AI) have used Bayesian belief networks to build models of expert opinion.

Kutato: An Entropy-Driven System for Construction of Probabilistic Expert Systems from Databases

no code implementations27 Mar 2013 Edward H. Herskovits, Gregory F. Cooper

Kutato is a system that takes as input a database of cases and produces a belief network that captures many of the dependence relations represented by those data.

An Empirical Evaluation of a Randomized Algorithm for Probabilistic Inference

no code implementations27 Mar 2013 R. Martin Chavez, Gregory F. Cooper

Using standard methods drawn from the theory of computational complexity, workers in the field have shown that the problem of probabilistic inference in belief networks is difficult and almost certainly intractable.

Medical Diagnosis

An Empirical Analysis of Likelihood-Weighting Simulation on a Large, Multiply-Connected Belief Network

no code implementations27 Mar 2013 Michael Shwe, Gregory F. Cooper

We analyzed the convergence properties of likelihood- weighting algorithms on a two-level, multiply connected, belief-network representation of the QMR knowledge base of internal medicine.

Cannot find the paper you are looking for? You can Submit a new open access paper.