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A new perspective on machine learning

HRUSHIKESH MHASKAR

Claremont Graduate University

January 28, 2019 • 12:00 AM

Scientific Machine Learning

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Deep Neural Network, Finite Element and Multigrid

Jinchao Xu

Pennsylvania State University

January 28, 2019 • 12:00 AM

Scientific Machine Learning

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Which ReLU Net Architectures Give Rise to Exploding and Vanishing Gradients...

Boris Hanin

Texas A&M University

January 28, 2019 • 12:00 AM

Scientific Machine Learning

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Machine learning and multi-scale modeling

Weinan E

Princeton University

January 28, 2019 • 12:00 AM

Scientific Machine Learning

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Using incomplete electronic health record data to inform decision making in...

Giorgos Bakoyannis

Indiana University

January 17, 2019 • 12:00 AM

Models and Machine Learning for Causal Inference and Decision Making in Health Research

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Causal Inference, Uncertainty, and Health Policy Decision Making

Sharon-Lise Normand

Harvard Medical School

January 17, 2019 • 12:00 AM

Models and Machine Learning for Causal Inference and Decision Making in Health Research

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Studentized Sensitivity Analysis in Paired Observational Studies

Colin Fogarty

MIT

January 17, 2019 • 12:00 AM

Models and Machine Learning for Causal Inference and Decision Making in Health Research

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Learning to Personalize from Observational Data Under Unobserved Confoundin...

Nathan Kallus

Cornell University

January 17, 2019 • 12:00 AM

Models and Machine Learning for Causal Inference and Decision Making in Health Research

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Personalizing Causal Pathway Effects

Ilya Shpitser

Johns Hopkins

January 16, 2019 • 12:00 AM

Models and Machine Learning for Causal Inference and Decision Making in Health Research

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Extending causal inferences from a randomized trial to a target population

Issa Dahabreh

Brown University

January 16, 2019 • 12:00 AM

Models and Machine Learning for Causal Inference and Decision Making in Health Research

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Estimating Effects in Clinical Trials Under Unobserved Conditions

Alisa Stephens-Shields

University of Pennsylvania

January 16, 2019 • 12:00 AM

Models and Machine Learning for Causal Inference and Decision Making in Health Research

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Subgroup Identification using Covariate Adjusted Interaction Trees

Jon Steingrimsson

Brown University

January 16, 2019 • 12:00 AM

Models and Machine Learning for Causal Inference and Decision Making in Health Research

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Video Archive

A new perspective on machine learning

Deep Neural Network, Finite Element and Multigrid

Which ReLU Net Architectures Give Rise to Exploding and Vanishing Gradients...

Machine learning and multi-scale modeling

Using incomplete electronic health record data to inform decision making in...

Causal Inference, Uncertainty, and Health Policy Decision Making

Studentized Sensitivity Analysis in Paired Observational Studies

Learning to Personalize from Observational Data Under Unobserved Confoundin...

Personalizing Causal Pathway Effects

Extending causal inferences from a randomized trial to a target population

Estimating Effects in Clinical Trials Under Unobserved Conditions

Subgroup Identification using Covariate Adjusted Interaction Trees

Video Archive

A new perspective on machine learning

Deep Neural Network, Finite Element and Multigrid

Which ReLU Net Architectures Give Rise to Exploding and Vanishing Gradients...

Machine learning and multi-scale modeling

Using incomplete electronic health record data to inform decision making in...

Causal Inference, Uncertainty, and Health Policy Decision Making

Studentized Sensitivity Analysis in Paired Observational Studies

Learning to Personalize from Observational Data Under Unobserved Confoundin...

Personalizing Causal Pathway Effects

Extending causal inferences from a randomized trial to a target population

Estimating Effects in Clinical Trials Under Unobserved Conditions

Subgroup Identification using Covariate Adjusted Interaction Trees