2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
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 | |
| Click here to download the manuscript | |
| Click here to view the Virtual Presentation | |
| 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 | |
| Click here to download the manuscript | |
| Click here to view the Virtual Presentation | |
| 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 | |
| Click here to download the manuscript | |
| Click here to view the Virtual Presentation | |
| Daesung Kim; KAIST | |
| Hye Won Chung; KAIST | |
| L.3.4: Reliable Distributed Clustering with Redundant Data Assignment | |
| Click here to download the manuscript | |
| Click here to view the Virtual Presentation | |
| 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
