Zobrazeno 1 - 10
of 67
pro vyhledávání: '"Polak, Pawel"'
Autor:
Li, Yizhou, Polak, Pawel
We incorporate the conditional value-at-risk (CVaR) quantity into a generalized class of Pickands estimators. By introducing CVaR, the newly developed estimators not only retain the desirable properties of consistency, location, and scale invariance
Externí odkaz:
http://arxiv.org/abs/2409.15677
Bilevel optimization methods are increasingly relevant within machine learning, especially for tasks such as hyperparameter optimization and meta-learning. Compared to the offline setting, online bilevel optimization (OBO) offers a more dynamic frame
Externí odkaz:
http://arxiv.org/abs/2409.10470
Autor:
Li, Yizhou, Polak, Pawel
The Pickands estimator for the extreme value index is beneficial due to its universal consistency, location, and scale invariance, which sets it apart from other types of estimators. However, similar to many extreme value index estimators, it is mark
Externí odkaz:
http://arxiv.org/abs/2401.11096
We introduce a novel method that combines differential geometry, kernels smoothing, and spectral analysis to quantify facial muscle activity from widely accessible video recordings, such as those captured on personal smartphones. Our approach emphasi
Externí odkaz:
http://arxiv.org/abs/2401.05625
Music recommendation for videos attracts growing interest in multi-modal research. However, existing systems focus primarily on content compatibility, often ignoring the users' preferences. Their inability to interact with users for further refinemen
Externí odkaz:
http://arxiv.org/abs/2310.06282
Autor:
Miao, Jiaju, Polak, Pawel
Asset-specific factors are commonly used to forecast financial returns and quantify asset-specific risk premia. Using various machine learning models, we demonstrate that the information contained in these factors leads to even larger economic gains
Externí odkaz:
http://arxiv.org/abs/2304.09947
We introduce a unified framework for rapid, large-scale portfolio optimization that incorporates both shrinkage and regularization techniques. This framework addresses multiple objectives, including minimum variance, mean-variance, and the maximum Sh
Externí odkaz:
http://arxiv.org/abs/2303.12751
We propose a method for constructing sparse high-frequency volatility estimators that are robust against change points in the spot volatility process. The estimators we propose are $\ell_1$-regularized versions of existing volatility estimators. We f
Externí odkaz:
http://arxiv.org/abs/2303.10550
Autor:
Kim, Juni, Dong, Zhikang, Guan, Eric, Rosenthal, Judah, Fu, Shi, Rafailovich, Miriam, Polak, Pawel
We provide a new non-invasive, easy-to-scale for large amounts of subjects and a remotely accessible method for (hidden) emotion detection from videos of human faces. Our approach combines face manifold detection for accurate location of the face in
Externí odkaz:
http://arxiv.org/abs/2211.00233
Autor:
Dong, Zhikang, Polak, Pawel
We investigate the inverse problem for Partial Differential Equations (PDEs) in scenarios where the parameters of the given PDE dynamics may exhibit changepoints at random time. We employ Physics-Informed Neural Networks (PINNs) - universal approxima
Externí odkaz:
http://arxiv.org/abs/2208.08626