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Found 4 papers, 0 papers with code
Asymptotically Efficient Online Learning for Censored Regression Models Under Non-I.I.D Data
The asymptotically efficient online learning problem is investigated for stochastic censored regression models, which arise from various fields of learning and statistics but up to now still lacks comprehensive theoretical studies on the efficiency of the learning algorithms.
Adaptive Identification with Guaranteed Performance Under Saturated-Observation and Non-Persistent Excitation
This paper investigates the adaptive identification and prediction problems for stochastic dynamical systems with saturated observations, which arise from various fields in engineering and social systems, but up to now still lack comprehensive theoretical studies including performance guarantees needed in practical applications.
A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning
In this paper we explore self-supervised learning to reduce labeling burdens in computational pathology.
Identification and Adaptation with Binary-Valued Observations under Non-Persistent Excitation Condition
By using both the stochastic Lyapunov function and martingale estimation methods, we establish the strong consistency of the estimation algorithm and provide the convergence rate, under a signal condition which is considerably weaker than the traditional PE condition and coincides with the weakest possible excitation known for the classical least square algorithm of stochastic regression models.
