E.3: Estimation Theory | |
Session Type: Lecture | |
Track: Detection and Estimation | |
Virtual Session: View on Virtual Platform | |
Session Chair: Ioannis Kontoyiannis, University of Cambridge | |
E.3.1: A General Derivative Identity for the Conditional Mean Estimator in Gaussian Noise and Some Applications | |
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Alex Dytso; Princeton University | |
H. Vincent Poor; Princeton University | |
Shlomo Shamai (Shitz); Princeton University | |
E.3.2: On the Sample Complexity of Estimating Small Singular Modes | |
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Xiangxiang Xu; Tsinghua University | |
Weida Wang; Tsinghua-Berkeley Shenzhen Institute | |
Shao-Lun Huang; Tsinghua-Berkeley Shenzhen Institute | |
E.3.3: On the Randomized Babai Point | |
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Xiao-Wen Chang; McGill University | |
Zhilong Chen; McGill University | |
Yingzi Xu; McGill University | |
E.3.4: When does the Tukey Median work? | |
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Banghua Zhu; University of California, Berkeley | |
Jiantao Jiao; University of California, Berkeley | |
Jacob Steinhardt; University of California, Berkeley | |
E.3.5: Missing Mass of Markov Chains | |
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Prafulla Chandra; Indian Institute of Technology Madras | |
Andrew Thangaraj; Indian Institute of Technology Madras | |
Nived Rajaraman; University of California Berkeley | |
E.3.6: Linear Models are Most Favorable among Generalized Linear Models | |
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Kuan-Yun Lee; UC Berkeley | |
Thomas A. Courtade; UC Berkeley | |
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia