Technical Program

L.4: Distribution Learning

Session Type: Lecture
Track: Statistics and Learning Theory
Virtual Session: View on Virtual Platform
Session Chair: Galen Reeves, Duke University
 
L.4.1: Analysis of K Nearest Neighbor KL Divergence Estimation for Continuous Distributions
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         Puning Zhao; University of California Davis
         Lifeng Lai; University of California Davis
 
L.4.2: Entropy property testing with finitely many errors
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         Changlong Wu; University of Hawaii at Manoa
         Narayana Santhanam; University of Hawaii at Manoa
 
L.4.3: On Learning Parametric Non-Smooth Continuous Distributions
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         Sudeep Kamath; PDT Partners
         Alon Orlitsky; University of California San Diego
         Venkatadheeraj Pichapati; Apple Inc.
         Ehsan Zobeidi; University of California San Diego
 
L.4.4: Latent Factor Analysis of Gaussian Distributions under Graphical Constraints
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         Md Mahmudul Hasan; Louisiana State University
         Shuangqing Wei; Louisiana State University
         Ali Moharrer; Louisiana State University
 
L.4.5: Learning Additive Noise Channels: Generalization Bounds and Algorithms
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         Nir Weinberger; Massachusetts Institute of Technology
 

Plan Ahead

IEEE ISIT 2021

2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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