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stat.ML2022
Probabilistic Querying of Continuous-Time Event Sequences
Alex Boyd, Yuxin Chang, Stephan Mandt +1
Continuous-time event sequences, i.e., sequences consisting of continuous time stamps and associated event types ("marks"), are an important type of sequential data with many appli…
stat.ML2020
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning
Aodong Li, Alex Boyd, Padhraic Smyth +1
We consider the problem of online learning in the presence of distribution shifts that occur at an unknown rate and of unknown intensity. We derive a new Bayesian online inference…
stat.ML2020★ 4 cited
User-Dependent Neural Sequence Models for Continuous-Time Event Data
Alex Boyd, Robert Bamler, Stephan Mandt +1
Continuous-time event data are common in applications such as individual behavior data, financial transactions, and medical health records. Modeling such data can be very challengi…