4 citations · 7 across the 12 of their papers we have counts for
13 papers
On the Effect of Regularization on Nonparametric Mean-Variance Regression
Eliot Wong-Toi, Alex Boyd, Vincent Fortuin +1
Uncertainty quantification is vital for decision-making and risk assessment in machine learning. Mean-variance regression models, which predict both a mean and residual noise for e…
Bayesian Inference for Correlated Human Experts and Classifiers
Markelle Kelly, Alex Boyd, Sam Showalter +2
Applications of machine learning often involve making predictions based on both model outputs and the opinions of human experts. In this context, we investigate the problem of quer…
Deep Continuous-Time State-Space Models for Marked Event Sequences
Yuxin Chang, Alex Boyd, Cao Xiao +4
Marked temporal point processes (MTPPs) model sequences of events occurring at irregular time intervals, with wide-ranging applications in fields such as healthcare, finance and so…
On the Efficient Marginalization of Probabilistic Sequence Models
Alex Boyd
Real-world data often exhibits sequential dependence, across diverse domains such as human behavior, medicine, finance, and climate modeling. Probabilistic methods capture the inhe…
Bayesian Online Learning for Consensus Prediction
Sam Showalter, Alex Boyd, Padhraic Smyth +1
Given a pre-trained classifier and multiple human experts, we investigate the task of online classification where model predictions are provided for free but querying humans incurs…
Probabilistic Modeling for Sequences of Sets in Continuous-Time
Yuxin Chang, Alex Boyd, Padhraic Smyth
Neural marked temporal point processes have been a valuable addition to the existing toolbox of statistical parametric models for continuous-time event data. These models are usefu…