TA6a6.5

Intrinsic Slepian-Bangs Type Formula for Parameters on LGs with Unknown Measurement Noise Variance

Samy LABSIR, IPSA, France; Alexandre Renaux, University of Paris Saclay, France; Jordi Vilà-Valls, Eric Chaumette, ISAE-SUPAERO, France
Session:
TA6a6: Estimation and Inference Poster
Track:
Adaptive Systems, Machine Learning, Data Analytics
Location:
Fred Farr
Presentation Time:
Tue, 31 Oct, 08:15 - 09:55 PT (UTC -8)
Session Chair:
Visa Koivunen, Aalto Unversity
Presentation
Discussion
Resources
No resources available.
Session TA6a6
TA6a6.1: Power Grid Faults Classification via Low-Rank Tensor Modeling
Matthew Repasky, Yao Xie, Georgia Institute of Technology, United States; Yichen Zhang, University of Texas at Arlington, United States; Feng Qiu, Argonne National Laboratory, United States
TA6a6.2: Bias, Variance, and Threshold Level of the Least Squares Pitch Estimator with Windowed Data
Jonas Lindenberger, Silicon Austria Labs GmbH, Austria; Stefan Schuster, voestalpine Stahl GmbH, Austria; Oliver Lang, Johannes Kepler University, Austria; Alexander Haberl, voestalpine Stahl GmbH, Austria; Clemens Staudinger, K1-MET GmbH, Austria; Theresa Roland, voestalpine Metal Forming GmbH, Austria; Mario Huemer, Johannes Kepler University, Austria
TA6a6.3: Stochastic Geometry Analysis of Localizability in Vision-Based Geolocation Systems
Haozhou Hu, Harpreet S. Dhillon, R. Michael Buehrer, Virginia Tech, United States
TA6a6.4: Optimal Multi-Stream Quickest Detection with False Discovery Rate control
Topi Halme, Visa Koivunen, Aalto University, Finland
TA6a6.5: Intrinsic Slepian-Bangs Type Formula for Parameters on LGs with Unknown Measurement Noise Variance
Samy LABSIR, IPSA, France; Alexandre Renaux, University of Paris Saclay, France; Jordi Vilà-Valls, Eric Chaumette, ISAE-SUPAERO, France
TA6a6.6: Local Convergence of Gradient Descent-Ascent for Training Generative Adversarial Networks
Evan Becker, University of California Los Angeles, United States; Parthe Pandit, University of California San Diego, United States; Alyson Fletcher, University of California Los Angeles, United States; Sundeep Rangan, New York University, United States
TA6a6.7: Bethe Free Energy and Extrinsics in Approximate Message Passing
Zilu Zhao, Dirk Slock, EURECOM, France
TA6a6.8: Causal Structural Learning from Time Series: A Convex Optimization Approach
Song Wei, Yao Xie, Georgia Institute of Technology, United States
TA6a6.9: Deep Expectation-Consistent Approximation for Phase Retrieval
Saurav K. Shastri, Rizwan Ahmad, Philip Schniter, The Ohio State University, United States
TA6a6.10: Identifying Direct Causes using Intervened Target Variable
Kang Du, Yu Xiang, University of Utah, United States; Ilya Soloveychik, Hebrew University of Jerusalem, Israel
TA6a6.11: Streaming Low-Rank Matrix Data Assimilation and Change Identification
Henry Yuchi, Matthew Repasky, Terry Ma, Yao Xie, Georgia Institute of Technology, United States
TA6a6.12: Poisson Multi-Bernoulli Filtering With Amplitude Information
Thomas Kropfreiter, University of California San Diego, United States; Jason L. Williams, Data61, Commonwealth Scientific and Industrial Research Organisation, Australia; Florian Meyer, University of California San Diego, United States
TA6a6.13: Global One-Bit Phase Retrieval via Sample Abundance—Including an Application to STFT Measurements
Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian, University of Illinois Chicago, United States
TA6a6.14: Error Probability Bounds for Invariant Causal Prediction via Multiple Access Channels
Austin Goddard, Yu Xiang, University of Utah, United States; Ilya Soloveychik, Hebrew University of Jerusalem, Israel
Contacts