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It is well-known that modern neural networks are vulnerable to adversarial examples. To mitigate this problem, a series of robust learning algorithms have been proposed. However, although the robust t...
现存的含未知输入系统的卡尔曼滤波方法假设噪声协方差矩阵已知而且要求系统强可观。针对实际中广泛存在的不精确知道噪声协方差矩阵或系统非强可观的情况,如何在工程应用如机器人定位中实现卡尔曼滤波是一个重要且亟需解决的难题。本报告中,报告人将探讨含未知输入滤波领域的研究现状,并以基于相关性的方法为典型,讨论含未知输入下噪声协方差矩阵的可辨识性问题。如果时间允许,报告人也将探讨其在机器人智能感知与决策方面的一...
Consider the linear wave equations with a potential outside a compact set with a non-trapping boundary condition. Assuming that the potential satisfies an appropriate decreasing condition and that zer...
We report 3 recent works along this direction: (A) The nature of L1, L21 error function robustness, using an outlier regularization framework.(B)Labeled robust principal component analysis (RPCA), by ...
We develop the Double Principal Component Analysis (DPCA) based on a dual factor structure for high-frequency intraday returns contaminated by microstructure noise. The dual factor structure allows a ...
This report consists of two parts associated with graph neural networks: generalization and graph structural learning. We first study the Rademacher complexity of GNNs, as one of independent-algorithm...
The formation control problem for a group of unmanned aerial vehicles (UAVs) has attracted significant attention recently in multiple fields, such as robotics, aerospace, and wireless communication. U...
System optimization and control have become the key design philosophies in modern control system theories, where reinforcement learning (RL) algorithm has drawn considerable attention in recent litera...
Optimal control, reinforcement learning and other optimization-based methods have been widelyapplied to control synthesis for real systems in physics, biology and robotics. However, in many complicate...
This talk considers the distributed estimation problem of a linear stochastic system described by an autoregressive model with exogenous inputs when both the system orders and parameters are unknown. ...

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