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08/24/2023
Federated Learning of Causal Effects from Incomplete Observational Data
Decentralized and incomplete data sources are prevalent in real-world ap...
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01/01/2023
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects
We propose a new causal inference framework to learn causal effects from...
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05/31/2021
Adaptive Multi-Source Causal Inference
Data scarcity is a tremendous challenge in causal effect estimation. In ...
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05/31/2021
Federated Estimation of Causal Effects from Observational Data
Many modern applications collect data that comes in federated spirit, wi...
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11/27/2020
Randomized Transferable Machine
Feature-based transfer is one of the most effective methodologies for tr...
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08/08/2020
Hierarchial Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection
This work is inspired by recent advances in hierarchical reinforcement l...
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05/06/2020
Subdomain Adaptation with Manifolds Discrepancy Alignment
Reducing domain divergence is a key step in transfer learning problems. ...
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04/24/2020
A Causal Modeling Framework with Stochastic Confounders
This work aims to extend the current causal inference framework to incor...
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12/03/2018
Knowledge-driven generative subspaces for modeling multi-view dependencies in medical data
Early detection of Alzheimer's disease (AD) and identification of potent...
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03/20/2013
Representation Requirements for Supporting Decision Model Formulation
This paper outlines a methodology for analyzing the representational sup...
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03/13/2013
Representing Context-Sensitive Knowledge in a Network Formalism: A Preliminary Report
Automated decision making is often complicated by the complexity of the ...
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01/16/2013