Zobrazeno 1 - 10
of 96
pro vyhledávání: '"Krach, Florian"'
Autor:
Krach, Florian, Teichmann, Josef
The Path-dependent Neural Jump ODE (PD-NJ-ODE) is a model for online prediction of generic (possibly non-Markovian) stochastic processes with irregular (in time) and potentially incomplete (with respect to coordinates) observations. It is a model for
Externí odkaz:
http://arxiv.org/abs/2407.18808
Autor:
Saqur, Raeid, Kratsios, Anastasis, Krach, Florian, Limmer, Yannick, Tian, Jacob-Junqi, Willes, John, Horvath, Blanka, Rudzicz, Frank
We propose MoE-F -- a formalised mechanism for combining $N$ pre-trained expert Large Language Models (LLMs) in online time-series prediction tasks by adaptively forecasting the best weighting of LLM predictions at every time step. Our mechanism leve
Externí odkaz:
http://arxiv.org/abs/2406.02969
Robust utility optimization enables an investor to deal with market uncertainty in a structured way, with the goal of maximizing the worst-case outcome. In this work, we propose a generative adversarial network (GAN) approach to (approximately) solve
Externí odkaz:
http://arxiv.org/abs/2403.15243
We propose an optimal iterative scheme for federated transfer learning, where a central planner has access to datasets ${\cal D}_1,\dots,{\cal D}_N$ for the same learning model $f_{\theta}$. Our objective is to minimize the cumulative deviation of th
Externí odkaz:
http://arxiv.org/abs/2309.04557
Publikováno v:
Transactions on Machine Learning Research (TMLR) 2024
The Path-Dependent Neural Jump Ordinary Differential Equation (PD-NJ-ODE) is a model for predicting continuous-time stochastic processes with irregular and incomplete observations. In particular, the method learns optimal forecasts given irregularly
Externí odkaz:
http://arxiv.org/abs/2307.13147
This paper studies the problem of forecasting general stochastic processes using a path-dependent extension of the Neural Jump ODE (NJ-ODE) framework \citep{herrera2021neural}. While NJ-ODE was the first framework to establish convergence guarantees
Externí odkaz:
http://arxiv.org/abs/2206.14284
This paper presents the benefits of using randomized neural networks instead of standard basis functions or deep neural networks to approximate the solutions of optimal stopping problems. The key idea is to use neural networks, where the parameters o
Externí odkaz:
http://arxiv.org/abs/2104.13669
Autor:
Ofenheimer, Alina, Breyer, Marie-Kathrin, Wouters, Emiel F.M., Schiffers, Caspar, Hartl, Sylvia, Burghuber, Otto C., Krach, Florian, Maninno, David M., Franssen, Frits M.E., Mraz, Tobias, Puchhammer, Patricia, Breyer-Kohansal, Robab
Publikováno v:
In Clinical Nutrition February 2024 43(2):476-481
Publikováno v:
International Conference on Learning Representations (2021)
Combinations of neural ODEs with recurrent neural networks (RNN), like GRU-ODE-Bayes or ODE-RNN are well suited to model irregularly observed time series. While those models outperform existing discrete-time approaches, no theoretical guarantees for
Externí odkaz:
http://arxiv.org/abs/2006.04727
Publikováno v:
Transactions on Machine Learning Research (2023)
The robust PCA of covariance matrices plays an essential role when isolating key explanatory features. The currently available methods for performing such a low-rank plus sparse decomposition are matrix specific, meaning, those algorithms must re-run
Externí odkaz:
http://arxiv.org/abs/2004.13612