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pro vyhledávání: '"Thomas, T C"'
A powerful concept behind much of the recent progress in machine learning is the extraction of common features across data from heterogeneous sources or tasks. Intuitively, using all of one's data to learn a common representation function benefits bo
Externí odkaz:
http://arxiv.org/abs/2308.04428
While $\mathcal{H}_\infty$ methods can introduce robustness against worst-case perturbations, their nominal performance under conventional stochastic disturbances is often drastically reduced. Though this fundamental tradeoff between nominal performa
Externí odkaz:
http://arxiv.org/abs/2305.16415
We propose Taylor Series Imitation Learning (TaSIL), a simple augmentation to standard behavior cloning losses in the context of continuous control. TaSIL penalizes deviations in the higher-order Taylor series terms between the learned and expert pol
Externí odkaz:
http://arxiv.org/abs/2205.14812
While $\mathcal{H}_\infty$ methods can introduce robustness against worst-case perturbations, their nominal performance under conventional stochastic disturbances is often drastically reduced. Though this fundamental tradeoff between nominal performa
Externí odkaz:
http://arxiv.org/abs/2203.10763
Autor:
Zhang, Thomas T. C. K., Tu, Stephen, Boffi, Nicholas M., Slotine, Jean-Jacques E., Matni, Nikolai
Motivated by bridging the simulation to reality gap in the context of safety-critical systems, we consider learning adversarially robust stability certificates for unknown nonlinear dynamical systems. In line with approaches from robust control, we c
Externí odkaz:
http://arxiv.org/abs/2112.10690
Adversarially robust training has been shown to reduce the susceptibility of learned models to targeted input data perturbations. However, it has also been observed that such adversarially robust models suffer a degradation in accuracy when applied t
Externí odkaz:
http://arxiv.org/abs/2111.08864
Estimating the rank of a corrupted data matrix is an important task in data analysis, most notably for choosing the number of components in PCA. Significant progress on this task was achieved using random matrix theory by characterizing the spectral
Externí odkaz:
http://arxiv.org/abs/2103.13840
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