Selected work

Representative Papers

Information Geometry of Message Passing

Aug 16, 2026

This study addresses the accuracy limitations of variational inference in non-conjugate and persistent uncertainty settings by proposing the Natural Gradient Message Passing (NGMP) algorithm. Grounded in information geometry and Forney-style factor graphs, NGMP reformulates stationarity conditions into edge-local forms and employs natural gradient projections to preserve exact messages representable within the receiving family, thereby effectively mitigating precision loss caused by averaging in traditional methods. Empirical evaluations across Poisson smoothing, heteroscedastic regression, and ETTH forecasting tasks demonstrate that NGMP significantly enhances both uncertainty calibration and inference performance in non-conjugate models. These results establish NGMP as a robust approach for improving variational inference accuracy where standard mean-field approximations typically fail due to structural mismatches or sustained uncertainty propagation.

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A Factor Graph Approach to Scalable Multi-Output Gaussian Process Regression

Aug 12, 2026

This work addresses the cubic computational complexity of multi-output Gaussian process regression and its difficulty in handling missing observations under heterogeneous inputs. The authors formulate the problem using a Forney-style factor graph, ordering inputs into a one-dimensional sequence via a nearest-neighbor chain and performing Gaussian message passing along this chain. This approach naturally accommodates missing data without requiring recomputation of the covariance matrix. By integrating Matérn latent processes, linear-Gaussian transition factors, and the linear model of coregionalization, the method achieves accuracy comparable to exact kernel methods in low-dimensional input settings. Evaluated on power time series forecasting tasks, the proposed approach matches baseline prediction accuracy while attaining linear scalability with respect to data size, thereby substantially improving computational efficiency.

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Recent publications

Latest Papers

Information Geometry of Message Passing

Aug 16, 2026

This study addresses the accuracy limitations of variational inference in non-conjugate and persistent uncertainty settings by proposing the Natural Gradient Message Passing (NGMP) algorithm. Grounded in information geometry and Forney-style factor graphs, NGMP reformulates stationarity conditions into edge-local forms and employs natural gradient projections to preserve exact messages representable within the receiving family, thereby effectively mitigating precision loss caused by averaging in traditional methods. Empirical evaluations across Poisson smoothing, heteroscedastic regression, and ETTH forecasting tasks demonstrate that NGMP significantly enhances both uncertainty calibration and inference performance in non-conjugate models. These results establish NGMP as a robust approach for improving variational inference accuracy where standard mean-field approximations typically fail due to structural mismatches or sustained uncertainty propagation.

0 citationsRead paper

A Factor Graph Approach to Scalable Multi-Output Gaussian Process Regression

Aug 12, 2026

This work addresses the cubic computational complexity of multi-output Gaussian process regression and its difficulty in handling missing observations under heterogeneous inputs. The authors formulate the problem using a Forney-style factor graph, ordering inputs into a one-dimensional sequence via a nearest-neighbor chain and performing Gaussian message passing along this chain. This approach naturally accommodates missing data without requiring recomputation of the covariance matrix. By integrating Matérn latent processes, linear-Gaussian transition factors, and the linear model of coregionalization, the method achieves accuracy comparable to exact kernel methods in low-dimensional input settings. Evaluated on power time series forecasting tasks, the proposed approach matches baseline prediction accuracy while attaining linear scalability with respect to data size, thereby substantially improving computational efficiency.

0 citationsRead paper