22 citations · 67 across the 9 of their papers we have counts for
9 papers
Speculative Sampling for Parametric Temporal Point Processes
Marin Biloš, Anderson Schneider, Yuriy Nevmyvaka
Temporal point processes are powerful generative models for event sequences that capture complex dependencies in time-series data. They are commonly specified using autoregressive…
Recurrent Interpolants for Probabilistic Time Series Prediction
Yu Chen, Marin Biloš, Sarthak Mittal +3
Sequential models like recurrent neural networks and transformers have become standard for probabilistic multivariate time series forecasting across various domains. Despite their…
Variational Schrödinger Diffusion Models
Wei Deng, Weijian Luo, Yixin Tan +4
Schrödinger bridge (SB) has emerged as the go-to method for optimizing transportation plans in diffusion models. However, SB requires estimating the intractable forward score funct…
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion
Marin Biloš, Kashif Rasul, Anderson Schneider +2
Temporal data such as time series can be viewed as discretized measurements of the underlying function. To build a generative model for such data we have to model the stochastic pr…
Irregularly-Sampled Time Series Modeling with Spline Networks
Marin Biloš, Emanuel Ramneantu, Stephan Günnemann
Observations made in continuous time are often irregular and contain the missing values across different channels. One approach to handle the missing data is imputing it using spli…
Scalable Normalizing Flows for Permutation Invariant Densities
Marin Biloš, Stephan Günnemann
Modeling sets is an important problem in machine learning since this type of data can be found in many domains. A promising approach defines a family of permutation invariant densi…
Deep Representation Learning and Clustering of Traffic Scenarios
Nick Harmening, Marin Biloš, Stephan Günnemann
Determining the traffic scenario space is a major challenge for the homologation and coverage assessment of automated driving functions. In contrast to current approaches that are…
Uncertainty on Asynchronous Time Event Prediction
Marin Biloš, Bertrand Charpentier, Stephan Günnemann
Asynchronous event sequences are the basis of many applications throughout different industries. In this work, we tackle the task of predicting the next event (given a history), an…
Intensity-Free Learning of Temporal Point Processes
Oleksandr Shchur, Marin Biloš, Stephan Günnemann
Temporal point processes are the dominant paradigm for modeling sequences of events happening at irregular intervals. The standard way of learning in such models is by estimating t…