Zobrazeno 1 - 10
of 526
pro vyhledávání: '"Zhang, Ji‐Feng"'
This paper addresses the optimal state estimation problem for dynamic systems while preserving private information against an adversary. To dominate the adversary's estimation accuracy about private information in the mean square error (MSE) sense, t
Externí odkaz:
http://arxiv.org/abs/2410.08756
The paper investigates the distributed estimation problem under low bit rate communications. Based on the signal-comparison (SC) consensus protocol under binary-valued communications, a new consensus+innovations type distributed estimation algorithm
Externí odkaz:
http://arxiv.org/abs/2405.18694
This paper proposes a new distributed nonconvex stochastic optimization algorithm that can achieve privacy protection, communication efficiency and convergence simultaneously. Specifically, each node adds general privacy noises to its local state to
Externí odkaz:
http://arxiv.org/abs/2403.18254
This paper proposes a consensus-based distributed nonlinear filter with kernel mean embedding (KME). This fills with gap of posterior density approximation with KME for distributed nonlinear dynamic systems. To approximate the posterior density, the
Externí odkaz:
http://arxiv.org/abs/2312.01928
Autor:
Wang, Jimin, Zhang, Ji-Feng
Differentially private distributed stochastic optimization has become a hot topic due to the urgent need of privacy protection in distributed stochastic optimization. In this paper, two-time scale stochastic approximation-type algorithms for differen
Externí odkaz:
http://arxiv.org/abs/2310.11892
This paper is concerned with the optimal identification problem of dynamical systems in which only quantized output observations are available under the assumption of fixed thresholds and bounded persistent excitations. Based on a time-varying projec
Externí odkaz:
http://arxiv.org/abs/2309.04984
In this paper, we solve an open problem and obtain a general maximum principle for a stochastic optimal control problem where the control domain is an arbitrary non-empty set and all the coefficients (especially the diffusion term and the terminal co
Externí odkaz:
http://arxiv.org/abs/2302.03339
This paper investigates the differentially private bipartite consensus algorithm over signed networks. The proposed algorithm protects each agent's sensitive information by adding noise with time-varying variances to the cooperative-competitive inter
Externí odkaz:
http://arxiv.org/abs/2212.11479
This paper studies the control-oriented identification problem of set-valued moving average systems with uniform persistent excitations and observation noises. A stochastic approximation-based (SA-based) algorithm without projections or truncations i
Externí odkaz:
http://arxiv.org/abs/2212.01777
Probabilistic Framework of Howard's Policy Iteration: BML Evaluation and Robust Convergence Analysis
This paper aims to build a probabilistic framework for Howard's policy iteration algorithm using the language of forward-backward stochastic differential equations (FBSDEs). As opposed to conventional formulations based on partial differential equati
Externí odkaz:
http://arxiv.org/abs/2210.07473