16 papers
Covariate Selection for Joint Latent Space Modeling of Sparse Network Data
Emma G Crenshaw, Yuhua Zhang, Jukka-Pekka Onnela
Network data are increasingly common in the social sciences and infectious disease epidemiology. Analyses often link network structure to node-level covariates, but existing method…
Information is localized in growing network models
Till Hoffmann, Jukka-Pekka Onnela
Mechanistic network models can capture salient characteristics of empirical networks using a small set of domain-specific, interpretable mechanisms. Yet inference remains challengi…
Approximate Bayesian Inference on Mechanisms of Network Growth and Evolution
Maxwell H Wang, Till Hoffmann, Jukka-Pekka Onnela
Mechanistic models can provide an intuitive and interpretable explanation of network growth by specifying a set of generative rules. These rules can be defined by domain knowledge…
Unifying Summary Statistic Selection for Approximate Bayesian Computation
Till Hoffmann, Jukka-Pekka Onnela
Extracting low-dimensional summary statistics from large datasets is essential for efficient (likelihood-free) inference. We characterize three different classes of summaries and d…
Describe Where You Are: Improving Noise-Robustness for Speech Emotion Recognition with Text Description of the Environment
Seong-Gyun Leem, Daniel Fulford, Jukka-Pekka Onnela +2
Speech emotion recognition (SER) systems often struggle in real-world environments, where ambient noise severely degrades their performance. This paper explores a novel approach th…
Conditional Mean and Variance Estimation via \textit{k}-NN Algorithm with Automated Variance Selection
Marcos Matabuena, Juan C. Vidal, Oscar Hernan Madrid Padilla +1
We introduce a novel \textit{k}-nearest neighbor (\textit{k}-NN) regression method for joint estimation of the conditional mean and variance. The proposed algorithm preserves the c…