A proliferation of emerging data science applications require efficient extraction of information from complex data. The unprecedented scale of relevant features, however, often overwhelms the volume of available samples, which dramatically complicates statistical inference and decision making.
Upcoming Events
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Events Archive
Breaking the Sample Size Barrier in Statistical Inference and Reinforcement Learning
Speaker(s):
Yuting Wei
Carnegie Mellon University
HEE Seminar- Taylor Faucett-UCI-Physics Learning from Machines Learning
Location:
Zoom
Speaker(s):
Taylor Faucett
UCI
Physics Learning from Machines Learning
Using Code Ocean in the Sciences and Engineering: Bringing computational reproducibility to your research collaborations
Conditional Sampling with Monotone GANs: Modifying Generative Models to Solve Inverse Problems
Location:
https://www.oneworldml.org/home
Speaker(s):
Department of Computing and Mathematical Sciences, Caltech
Tags:
Featured Event
Metamaterials Design and Manufacturing: Perspectives From Biology and Artificial Intelligence
Deep Learning: It’s Not All About Recognizing Cats and Dogs
Speaker(s):
Carole-Jean Wu
Arizona State University
Analysis of Stochastic Gradient Descent in Continuous Time
Introduction to the Machine Learning Libraries
Speaker(s):
Jonathan Halverson
Princeton University
Intro to Data Analysis using Python
Speaker(s):
Oscar Torres-Reyna
Princeton University Library