Bayesian Learning
Distributionally Robust Skeleton Learning of Discrete Bayesian Networks Y eshu Li
The DAG structure of a Bayesian network is typically unknown in practice [Natori et al., 2017,
Switching Autoregressive Low-rank Tensor Models
An important problem in time-series analysis is modeling systems with time-varying dynamics.
Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory
Since their inception, V ariational Autoencoders (V AEs) have become central in machine learning. Despite their widespread use, numerous questions regarding their theoretical properties remain open.
A Bayesian Take on Gaussian Process Networks
In the absence of full prior knowledge of the underlying graph, inference on the structure is necessary.
New Complexity-Theoretic Frontiers of Tractability for Neural Network Training
A neural network (cf. Figure 1) can be thought of as a directed acyclic network consisting of
Learning via Wasserstein-Based High Probability Generalisation Bounds
The authors contributed equally to this work 37th Conference on Neural Information Processing Systems (NeurIPS 2023). Developing upper bounds on the generalisation gap, i.e., generalisation bounds has been a longstanding topic in statistical learning.
Learning via Wasserstein-Based High Probability Generalisation Bounds
The authors contributed equally to this work 37th Conference on Neural Information Processing Systems (NeurIPS 2023). Developing upper bounds on the generalisation gap, i.e., generalisation bounds has been a longstanding topic in statistical learning.
Sparse Deep Learning for Time Series Data: Theory and Applications
Specifically, we lay the foundation within the Bayesian framework.
Sparse Deep Learning for Time Series Data: Theory and Applications
Specifically, we lay the foundation within the Bayesian framework.