A Sparse Expansion For Deep Gaussian Processes
This work addresses computational bottlenecks in DGP inference for researchers and practitioners in machine learning, representing an incremental improvement in efficiency.
The paper tackles the high computational complexity of Deep Gaussian Processes (DGPs) by proposing a Deep Tensor Markov Gaussian Process (DTMGP) model, which achieves superior computational efficiency with only polylog(M) non-zero activation functions out of M, as demonstrated in numerical experiments on synthetic and real datasets.
In this work, we use Deep Gaussian Processes (DGPs) as statistical surrogates for stochastic processes with complex distributions. Conventional inferential methods for DGP models can suffer from high computational complexity as they require large-scale operations with kernel matrices for training and inference. In this work, we propose an efficient scheme for accurate inference and efficient training based on a range of Gaussian Processes, called the Tensor Markov Gaussian Processes (TMGP). We construct an induced approximation of TMGP referred to as the hierarchical expansion. Next, we develop a deep TMGP (DTMGP) model as the composition of multiple hierarchical expansion of TMGPs. The proposed DTMGP model has the following properties: (1) the outputs of each activation function are deterministic while the weights are chosen independently from standard Gaussian distribution; (2) in training or prediction, only polylog(M) (out of M) activation functions have non-zero outputs, which significantly boosts the computational efficiency. Our numerical experiments on synthetic models and real datasets show the superior computational efficiency of DTMGP over existing DGP models.