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Suhas Diggavi
Also published under:S. N. Diggavi, Suhas N. Diggavi, S. Diggavi
Affiliation
Department of Electrical, Computer Engineering, University of California, Los Angeles, CA, USA
Topic
Gradient Descent,Lower Bound,Convergence Rate,Covariance Matrix,Federated Learning,Neural Network,Optimization Problem,Target Domain,Communication Cost,Federated Learning Algorithm,Heterogeneous Set,Inference Task,Internet Of Things Applications,Number Of Tests,Proof Of Theorem,Random Vector,Test Accuracy,Test Design,True Target,Acoustic Signals,Common Information,Commons Attribution,Communication Constraints,Communication Graph,Community Structure,Complex Gain,Conditional Independence,Convergence Results,Data Distribution,Data Generation,Deep Learning,Differential Privacy,Dimensional Model,Domain Shift,Edge Server,Efficient Communication,Entire Model,Error Probability,Excess Risk,False Negative,False Positive Rate,Foundation Model,Functional Class,Gaussian Random Vector,Interesting Scenario,Internet Of Things Devices,Latent Representation,Latent Space,Learning Models,Learning Problem,
Biography
Suhas Diggavi joined UCLA as professor of electrical engineering in 2010. His research interests include wireless network information theory, wireless networking systems, network data compression, and network algorithms. Diggavi received a PhD in electrical engineering from Stanford University. He is a Fellow of the IEEE. Contact him at [email protected].