Russell Greiner

research

∙ 06/20/2023

Copula-Based Deep Survival Models for Dependent Censoring

A survival dataset describes a set of instances (e.g. patients) and prov...

0 Ali Hossein Gharari Foomani, et al. ∙

research

∙ 02/09/2023

Modeling and Forecasting COVID-19 Cases using Latent Subpopulations

Classical epidemiological models assume homogeneous populations. There h...

0 Roberto Vega, et al. ∙

research

∙ 01/14/2022

Domain-shift adaptation via linear transformations

A predictor, f_A : X → Y, learned with data from a source domain (A) mig...

6 Roberto Vega, et al. ∙

research

∙ 11/11/2021

Variational Auto-Encoder Architectures that Excel at Causal Inference

Estimating causal effects from observational data (at either an individu...

39 Negar Hassanpour, et al. ∙

research

∙ 06/03/2021

SIMLR: Machine Learning inside the SIR model for COVID-19 Forecasting

Accurate forecasts of the number of newly infected people during an epid...

49 Roberto Vega, et al. ∙

research

∙ 02/11/2021

Sample Efficient Learning of Image-Based Diagnostic Classifiers Using Probabilistic Labels

Deep learning approaches often require huge datasets to achieve good gen...

9 Roberto Vega, et al. ∙

research

∙ 10/24/2020

Shared Space Transfer Learning for analyzing multi-site fMRI data

Multi-voxel pattern analysis (MVPA) learns predictive models from task-b...

15 Muhammad Yousefnezhad, et al. ∙

research

∙ 12/19/2019

Reducing Selection Bias in Counterfactual Reasoning for Individual Treatment Effects Estimation

Counterfactual reasoning is an important paradigm applicable in many fie...

8 Zichen Zhang, et al. ∙

research

∙ 09/11/2019

Domain Aggregation Networks for Multi-Source Domain Adaptation

In many real-world applications, we want to exploit multiple source data...

15 Junfeng Wen, et al. ∙

research

∙ 06/25/2019

Simultaneous Prediction Intervals for Patient-Specific Survival Curves

Accurate models of patient survival probabilities provide important info...

2 Samuel Sokota, et al. ∙

research

∙ 03/29/2019

The Challenge of Predicting Meal-to-meal Blood Glucose Concentrations for Patients with Type I Diabetes

Patients with Type I Diabetes (T1D) must take insulin injections to prev...

0 Neil C. Borle, et al. ∙

research

∙ 03/25/2019

Gene Expression based Survival Prediction for Cancer Patients: A Topic Modeling Approach

Cancer is one of the leading cause of death, worldwide. Many believe tha...

0 Luke Kumar, et al. ∙

research

∙ 11/28/2018

Effective Ways to Build and Evaluate Individual Survival Distributions

An accurate model of a patient's individual survival distribution can he...

0 Humza Haider, et al. ∙

research

∙ 12/01/2017

Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks

Smart systems that can accurately diagnose patients with mental disorder...

0 Jumana Dakka, et al. ∙

research

∙ 01/01/2016

Stochastic Neural Networks with Monotonic Activation Functions

We propose a Laplace approximation that creates a stochastic unit from a...

0 Siamak Ravanbakhsh, et al. ∙

research

∙ 09/28/2015

Boolean Matrix Factorization and Noisy Completion via Message Passing

Boolean matrix factorization and Boolean matrix completion from noisy ob...

0 Siamak Ravanbakhsh, et al. ∙

research

∙ 09/25/2014

Revisiting Algebra and Complexity of Inference in Graphical Models

This paper studies the form and complexity of inference in graphical mod...

0 Siamak Ravanbakhsh, et al. ∙

research

∙ 09/04/2014

Accurate, fully-automated NMR spectral profiling for metabolomics

Many diseases cause significant changes to the concentrations of small m...

0 Siamak Ravanbakhsh, et al. ∙

research

∙ 06/04/2014

Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning

The cutting plane method is an augmentative constrained optimization pro...

0 Siamak Ravanbakhsh, et al. ∙

research

∙ 05/06/2014

Training Restricted Boltzmann Machine by Perturbation

A new approach to maximum likelihood learning of discrete graphical mode...

0 Siamak Ravanbakhsh, et al. ∙

research

∙ 01/26/2014

Perturbed Message Passing for Constraint Satisfaction Problems

We introduce an efficient message passing scheme for solving Constraint ...

0 Siamak Ravanbakhsh, et al. ∙

research

∙ 02/06/2013

Learning Bayesian Nets that Perform Well

A Bayesian net (BN) is more than a succinct way to encode a probabilisti...

0 Russell Greiner, et al. ∙

research

∙ 01/23/2013

Comparing Bayesian Network Classifiers

In this paper, we empirically evaluate algorithms for learning four type...

0 Jie Cheng, et al. ∙

research

∙ 01/10/2013

Bayesian Error-Bars for Belief Net Inference

A Bayesian Belief Network (BN) is a model of a joint distribution over a...

0 Tim Van Allen, et al. ∙

research

∙ 10/19/2012

Budgeted Learning of Naive-Bayes Classifiers

Frequently, acquiring training data has an associated cost. We consider ...

0 Daniel J. Lizotte, et al. ∙

research

∙ 07/11/2012

Active Model Selection

Classical learning assumes the learner is given a labeled data sample, f...

0 Omid Madani, et al. ∙

research

∙ 06/18/2012

A Generalized Loop Correction Method for Approximate Inference in Graphical Models

Belief Propagation (BP) is one of the most popular methods for inference...

0 Siamak Ravanbakhsh, et al. ∙

research

∙ 06/13/2012

Speeding Up Planning in Markov Decision Processes via Automatically Constructed Abstractions

In this paper, we consider planning in stochastic shortest path (SSP) pr...

0 Alejandro Isaza, et al. ∙

research

∙ 05/09/2012

Improved Mean and Variance Approximations for Belief Net Responses via Network Doubling

A Bayesian belief network models a joint distribution with an directed a...

0 Peter Hooper, et al. ∙

Russell Greiner

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