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Found 9 papers, 1 papers with code
SigFormer: Signature Transformers for Deep Hedging
To mitigate such difficulties, we introduce SigFormer, a novel deep learning model that combines the power of path signatures and transformers to handle sequential data, particularly in cases with irregularities.
Conditional Support Alignment for Domain Adaptation with Label Shift
In this paper, we propose a novel conditional adversarial support alignment (CASA) whose aim is to minimize the conditional symmetric support divergence between the source's and target domain's feature representation distributions, aiming at a more helpful representation for the classification task.
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior
Choosing a proper set of kernel functions is an important problem in learning Gaussian Process (GP) models since each kernel structure has different model complexity and data fitness.
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems
Recent advances in Deep Gaussian Processes (DGPs) show the potential to have more expressive representation than that of traditional Gaussian Processes (GPs).
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes
We also provide conditions under which CBOCPD provides the lower prediction error compared to BOCPD.
Discovering Latent Covariance Structures for Multiple Time Series
In this paper, we present a new GP model which naturally handles multiple time series by placing an Indian Buffet Process (IBP) prior on the presence of shared kernels.
Automatic Generation of Probabilistic Programming from Time Series Data
In this paper, we provide a new perspective to build expressive probabilistic program from continue time series data when the structure of model is not given.
Searching for Topological Symmetry in Data Haystack
To compute the symmetry in a grid structure, we introduce three legal grid moves (i) Commutation (ii) Cyclic Permutation (iii) Stabilization on sets of local grid squares, grid blocks.
The Automatic Statistician: A Relational Perspective
Gaussian Processes (GPs) provide a general and analytically tractable way of modeling complex time-varying, nonparametric functions.
