L.3: Distributed Learning Performance Analysis | |
Session Type: Lecture | |
Track: Statistics and Learning Theory | |
Virtual Session: View on Virtual Platform | |
Session Chair: Lav Varshney, University of Illinois, Urbana-Champaign | |
L.3.1: Communication Efficient Distributed Approximate Newton Method | |
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Avishek Ghosh; University of California, Berkeley | |
Raj Kumar Maity; University of Massachusetts, Amherst | |
Arya Mazumdar; University of Massachusetts, Amherst | |
Kannan Ramachandran; University of California, Berkeley | |
L.3.2: Communication Efficient and Byzantine Tolerant Distributed Learning | |
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Avishek Ghosh; University of California, Berkeley | |
Raj Kumar Maity; University of Massachusetts, Amherst | |
Swanand Kadhe; University of California, Berkeley | |
Arya Mazumdar; University of Massachusetts, Amherst | |
Kannan Ramchandran; University of California, Berkeley | |
L.3.3: Crowdsourced Classification with XOR Queries: An Algorithm with Optimal Sample Complexity | |
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Daesung Kim; KAIST | |
Hye Won Chung; KAIST | |
L.3.4: Reliable Distributed Clustering with Redundant Data Assignment | |
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Venkata Gandikota; University of Massachusetts Amherst | |
Arya Mazumdar; University of Massachusetts Amherst | |
Ankit Singh Rawat; Google Research | |
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia