Multi-Output Robust and Conjugate Gaussian Processes
This work addresses robustness issues in multi-output regression for domains like finance and cancer research, but it is incremental as it builds directly on an existing framework.
The paper tackles the sensitivity of multi-output Gaussian processes to model misspecification and outliers by extending the robust and conjugate Gaussian process framework to create MO-RCGP, a provably robust method that jointly captures correlations across outputs, with evaluations in finance and cancer research.
Multi-output Gaussian process (MOGP) regression allows modelling dependencies among multiple correlated response variables. Similarly to standard Gaussian processes, MOGPs are sensitive to model misspecification and outliers, which can distort predictions within individual outputs. This situation can be further exacerbated by multiple anomalous response variables whose errors propagate due to correlations between outputs. To handle this situation, we extend and generalise the robust and conjugate Gaussian process (RCGP) framework introduced by Altamirano et al. (2024). This results in the multi-output RCGP (MO-RCGP): a provably robust MOGP that is conjugate, and jointly captures correlations across outputs. We thoroughly evaluate our approach through applications in finance and cancer research.