Technical Program

L.9: Learning Methods and Networks

Session Type: Lecture
Track: Statistics and Learning Theory
Virtual Session: View on Virtual Platform
Session Chair: Venugopal Veeravalli, University of Illinois at Urbana-Champaign
 
L.9.1: CodNN – Robust Neural Networks From Coded Classification
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         Netanel Raviv; Washington University in Saint Louis
         Siddharth Jain; California Institute of Technology
         Pulakesh Upadhyaya; Texas A&M University
         Jehoshua Bruck; California Institute of Technology
         Anxiao Jiang; Texas A&M University
 
L.9.2: Functional Error Correction for Reliable Neural Networks
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         Kunping Huang; Texas A&M University
         Paul H. Siegel; University of California, San Diego
         Anxiao Jiang; Texas A&M University
 
L.9.3: On the alpha-loss Landscape in the Logistic Model
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         Tyler Sypherd; Arizona State University
         Mario Diaz; Universidad Nacional Autónoma de México
         Lalitha Sankar; Arizona State University
         Gautam Dasarathy; Arizona State University
 
L.9.4: Max-affine Regression with Universal Parameter Estimation for Small-ball Designs
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         Avishek Ghosh; University of California, Berkeley
         Ashwin Pananjady; University of California, Berkeley
         Kannan Ramchandran; University of California, Berkeley
         Aditya Guntuboyina; University of California, Berkeley
 
L.9.5: Model Projection: Theory and Applications to Fair Machine Learning
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         Wael Alghamdi; Harvard University
         Shahab Asoodeh; Harvard University
         Hao Wang; Harvard University
         Flavio P. Calmon; Harvard University
         Dennis Wei; IBM Research
         Karthikeyan Natesan Ramamurthy; IBM Research
 

Plan Ahead

IEEE ISIT 2021

2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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