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Found 5 papers, 1 papers with code
Directed Chain Generative Adversarial Networks
Real-world data can be multimodal distributed, e. g., data describing the opinion divergence in a community, the interspike interval distribution of neurons, and the oscillators natural frequencies.
Deep Learning for Systemic Risk Measures
The aim of this paper is to study a new methodological framework for systemic risk measures by applying deep learning method as a tool to compute the optimal strategy of capital allocations.
Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost
In this paper, we propose a new algorithm based on stage-wise exploration and adaptive policy elimination that achieves a regret of $\widetilde{O}(\sqrt{H^4S^2AT})$ while requiring a switching cost of $O(HSA \log\log T)$.
Signatured Deep Fictitious Play for Mean Field Games with Common Noise
In this paper, based on the rough path theory, we propose a novel single-loop algorithm, named signatured deep fictitious play, by which we can work with the unfixed common noise setup to avoid the nested-loop structure and reduce the computational complexity significantly.
Convolutional Signature for Sequential Data
Signature is an infinite graded sequence of statistics known to characterize geometric rough paths, which includes the paths with bounded variation.
