107 citations · 469 across the 35 of their papers we have counts for
7 papers · 1 filter
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…
Predictive Querying for Autoregressive Neural Sequence Models
Alex Boyd, Sam Showalter, Stephan Mandt +1
In reasoning about sequential events it is natural to pose probabilistic queries such as "when will event A occur next" or "what is the probability of A occurring before B", with a…
Making Thermodynamic Models of Mixtures Predictive by Machine Learning: Matrix Completion of Pair Interactions
Fabian Jirasek, Robert Bamler, Sophie Fellenz +4
Predictive models of thermodynamic properties of mixtures are paramount in chemical engineering and chemistry. Classical thermodynamic models are successful in generalizing over (c…
Raising the Bar in Graph-level Anomaly Detection
Chen Qiu, Marius Kloft, Stephan Mandt +1
Graph-level anomaly detection has become a critical topic in diverse areas, such as financial fraud detection and detecting anomalous activities in social networks. While most rese…
Detecting Anomalies within Time Series using Local Neural Transformations
Tim Schneider, Chen Qiu, Marius Kloft +4
We develop a new method to detect anomalies within time series, which is essential in many application domains, reaching from self-driving cars, finance, and marketing to medical d…
Hybridizing Physical and Data-driven Prediction Methods for Physicochemical Properties
Fabian Jirasek, Robert Bamler, Stephan Mandt
We present a generic way to hybridize physical and data-driven methods for predicting physicochemical properties. The approach `distills' the physical method's predictions into a p…