2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-9.3
Paper Title A Dynamical Systems Perspective on Online Bayesian Nonparametric Estimators with Adaptive Hyperparameters
Authors Alec Koppel, Amrit Singh Bedi, US Army Research Laboratory, United States; Vikram Krishnamurthy, Cornell University, United States
SessionMLSP-9: Learning Theory for Neural Networks
LocationGather.Town
Session Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-LEAR] Learning theory and algorithms
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract This paper presents and analyzes constant step size stochastic gradient algorithms in reproducing kernel Hilbert Space (RKHS), which encapsulates various adaptive nonlinear interpolation schemes. The hyperparameters of the function iterates are updated via a distribution that depends on the estimates generated by the algorithm. Using stochastic averaging theory, we show that the estimates generated by the algorithm converge weakly to an algebraically constrained ordinary differential equation. We illustrate this proposed algorithm in an online multi-class classification problem. Specifically, the proposed RKHS-valued stochastic gradient algorithm operating in concert with a Gaussian kernel whose bandwidth stably evolves during training ,performs comparably to when the bandwidth is set according to oracle knowledge of its optimal value.