Kristjan Greenewald

Research Staff Member

My interests lie broadly in applying information theory and statistics to both practical and theoretical machine learning problems. Areas of interest include learning theory, causal inference, online learning and decision making, and robust ML. I received my PhD from the University of Michigan in 2017 focusing on signal processing and machine learning, and was a postdoctoral research fellow at the Harvard University Statistics department (2017) before joining IBM Research in 2018.

Top Work

Causal inference is expensive. Here’s an algorithm for fixing that.

Causal inference is expensive. Here’s an algorithm for fixing that.

Causal Inference

Publications with the MIT-IBM Watson AI Lab

SPAHM: Parameter matching for model fusion
SPAHM: Parameter matching for model fusion
 
Causal inference is expensive. Here’s an algorithm for fixing that.
Causal inference is expensive. Here’s an algorithm for fixing that.
 
Bayesian Nonparametric Federated Learning of Neural Networks
Bayesian Nonparametric Federated Learning of Neural Networks
 
Action Centered Contextual Bandits
Action Centered Contextual Bandits
 
Ensemble Estimation of Information Divergence
Ensemble Estimation of Information Divergence