Jan Gasthaus

Publication Activity (10 Years)

Years Active: 2008-2023
Publications (10 Years): 45

Top Topics

Variational Inference

Demand Forecasting

Anomaly Detection

Top Venues

Proc. VLDB Endow.

Publications

Syama Sundar Rangapuram, Jan Gasthaus, Lorenzo Stella, Valentin Flunkert, David Salinas, Yuyang Wang, Tim Januschowski
Deep Non-Parametric Time Series Forecaster. CoRR (2023)
Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Yuyang Wang, Danielle C. Maddix, Ali Caner Türkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, François-Xavier Aubet, Laurent Callot, Tim Januschowski
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey. ACM Comput. Surv. 55 (6) (2023)
Richard Kurle, Ralf Herbrich, Tim Januschowski, Yuyang Wang, Jan Gasthaus
On the detrimental effect of invariances in the likelihood for variational inference. CoRR (2022)
Kelvin Kan, François-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus
Multivariate Quantile Function Forecaster. CoRR (2022)
Sanjay Purushotham, Jun Huan, Cong Shen, Dongjin Song, Yuyang Wang, Jan Gasthaus, Hilaf Hasson, Youngsuk Park, Sungyong Seo, Yuriy Nevmyvaka
8th SIGKDD International Workshop on Mining and Learning from Time Series - Deep Forecasting: Models, Interpretability, and Applications. KDD (2022)
Christian Bock, François-Xavier Aubet, Jan Gasthaus, Andrey Kan, Ming Chen, Laurent Callot
Online Time Series Anomaly Detection with State Space Gaussian Processes. CoRR (2022)
Tim Januschowski, Jan Gasthaus, Yuyang Wang, David Salinas, Valentin Flunkert, Michael Bohlke-Schneider, Laurent Callot
Criteria for Classifying Forecasting Methods. CoRR (2022)
Youngsuk Park, Danielle C. Maddix, François-Xavier Aubet, Kelvin Kan, Jan Gasthaus, Yuyang Wang
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting. AISTATS (2022)
Déborah Sulem, Michele Donini, Muhammad Bilal Zafar, Francois-Xavier Aubet, Jan Gasthaus, Tim Januschowski, Sanjiv Das, Krishnaram Kenthapadi, Cédric Archambeau
Diverse Counterfactual Explanations for Anomaly Detection in Time Series. CoRR (2022)
Paul Jeha, Michael Bohlke-Schneider, Pedro Mercado, Shubham Kapoor, Rajbir-Singh Nirwan, Valentin Flunkert, Jan Gasthaus, Tim Januschowski
PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series. ICLR (2022)
Kelvin Kan, François-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus
Multivariate Quantile Function Forecaster. AISTATS (2022)
Chris U. Carmona, François-Xavier Aubet, Valentin Flunkert, Jan Gasthaus
Neural Contextual Anomaly Detection for Time Series. IJCAI (2022)
Stephan Rabanser, Tim Januschowski, Kashif Rasul, Oliver Borchert, Richard Kurle, Jan Gasthaus, Michael Bohlke-Schneider, Nicolas Papernot, Valentin Flunkert
Intrinsic Anomaly Detection for Multi-Variate Time Series. CoRR (2022)
Richard Kurle, Ralf Herbrich, Tim Januschowski, Yuyang Wang, Jan Gasthaus
On the detrimental effect of invariances in the likelihood for variational inference. NeurIPS (2022)
Chris U. Carmona, François-Xavier Aubet, Valentin Flunkert, Jan Gasthaus
Neural Contextual Anomaly Detection for Time Series. CoRR (2021)
Hilaf Hasson, Bernie Wang, Tim Januschowski, Jan Gasthaus
Probabilistic Forecasting: A Level-Set Approach. NeurIPS (2021)
Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann
Detecting Anomalous Event Sequences with Temporal Point Processes. CoRR (2021)
Paul Jeha, Michael Bohlke-Schneider, Pedro Mercado, Rajbir-Singh Nirwan, Shubham Kapoor, Valentin Flunkert, Jan Gasthaus, Tim Januschowski
PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series. CoRR (2021)
François-Xavier Aubet, Daniel Zügner, Jan Gasthaus
Monte Carlo EM for Deep Time Series Anomaly Detection. CoRR (2021)
Daniel Zügner, François-Xavier Aubet, Victor Garcia Satorras, Tim Januschowski, Stephan Günnemann, Jan Gasthaus
A Study of Joint Graph Inference and Forecasting. CoRR (2021)
Youngsuk Park, Danielle C. Maddix, François-Xavier Aubet, Kelvin Kan, Jan Gasthaus, Yuyang Wang
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting. CoRR (2021)
Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann
Detecting Anomalous Event Sequences with Temporal Point Processes. NeurIPS (2021)
Syama Sundar Rangapuram, Lucien D. Werner, Konstantinos Benidis, Pedro Mercado, Jan Gasthaus, Tim Januschowski
End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series. ICML (2021)
Stephan Rabanser, Tim Januschowski, Valentin Flunkert, David Salinas, Jan Gasthaus
The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models. CoRR (2020)
Christos Faloutsos, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Yuyang Wang
Forecasting Big Time Series: Theory and Practice. WWW (Companion Volume) (2020)
Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Sundar Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang
GluonTS: Probabilistic and Neural Time Series Modeling in Python. J. Mach. Learn. Res. 21 (2020)
Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting. NeurIPS (2020)
Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Bernie Wang, Danielle C. Maddix, Ali Caner Türkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, Laurent Callot, Tim Januschowski
Neural forecasting: Introduction and literature overview. CoRR (2020)
Fadhel Ayed, Lorenzo Stella, Tim Januschowski, Jan Gasthaus
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models. CoRR (2020)
Fadhel Ayed, Lorenzo Stella, Tim Januschowski, Jan Gasthaus
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models. ICSOC Workshops (2020)
Edo Liberty, Zohar S. Karnin, Bing Xiang, Laurence Rouesnel, Baris Coskun, Ramesh Nallapati, Julio Delgado, Amir Sadoughi, Yury Astashonok, Piali Das, Can Balioglu, Saswata Chakravarty, Madhav Jha, Philip Gautier, David Arpin, Tim Januschowski, Valentin Flunkert, Yuyang Wang, Jan Gasthaus, Lorenzo Stella, Syama Sundar Rangapuram, David Salinas, Sebastian Schelter, Alex Smola
Elastic Machine Learning Algorithms in Amazon SageMaker. SIGMOD Conference (2020)
Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean Foster, Tim Januschowski
Deep Factors for Forecasting. ICML (2019)
Christos Faloutsos, Jan Gasthaus, Tim Januschowski, Yuyang Wang
Classical and Contemporary Approaches to Big Time Series Forecasting. SIGMOD Conference (2019)
David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes. CoRR (2019)
Christos Faloutsos, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Yuyang Wang
Forecasting Big Time Series: Theory and Practice. KDD (2019)
Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Sundar Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang
GluonTS: Probabilistic Time Series Models in Python. CoRR (2019)
David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus
High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes. NeurIPS (2019)
Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean Foster, Tim Januschowski
Deep Factors for Forecasting. CoRR (2019)
Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, Tim Januschowski
Probabilistic Forecasting with Spline Quantile Function RNNs. AISTATS (2019)
Christos Faloutsos, Jan Gasthaus, Tim Januschowski, Yuyang Wang
Forecasting Big Time Series: Old and New. Proc. VLDB Endow. 11 (12) (2018)
Syama Sundar Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, Tim Januschowski
Deep State Space Models for Time Series Forecasting. NeurIPS (2018)
Matthias W. Seeger, Syama Sundar Rangapuram, Yuyang Wang, David Salinas, Jan Gasthaus, Tim Januschowski, Valentin Flunkert
Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale. CoRR (2017)
Jacquelyn A. Shelton, Jan Gasthaus, Zhenwen Dai, Jörg Lücke, Arthur Gretton
GP-Select: Accelerating EM Using Adaptive Subspace Preselection. Neural Comput. 29 (8) (2017)
Joos-Hendrik Boese, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Dustin Lange, David Salinas, Sebastian Schelter, Matthias W. Seeger, Bernie Wang
Probabilistic Demand Forecasting at Scale. Proc. VLDB Endow. 10 (12) (2017)
Valentin Flunkert, David Salinas, Jan Gasthaus
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. CoRR (2017)
Jacquelyn A. Shelton, Jan Gasthaus, Zhenwen Dai, Jörg Lücke, Arthur Gretton
GP-select: Accelerating EM using adaptive subspace preselection. CoRR (2014)
Frank D. Wood, Jan Gasthaus, Cédric Archambeau, Lancelot James, Yee Whye Teh
The sequence memoizer. Commun. ACM 54 (2) (2011)
Jan Gasthaus, Yee Whye Teh
Improvements to the Sequence Memoizer. NIPS (2010)
Jan Gasthaus, Frank D. Wood, Yee Whye Teh
Lossless Compression Based on the Sequence Memoizer. DCC (2010)
Frank D. Wood, Cédric Archambeau, Jan Gasthaus, Lancelot James, Yee Whye Teh
A stochastic memoizer for sequence data. ICML (2009)
Jan Gasthaus, Frank D. Wood, Dilan Görür, Yee Whye Teh
Dependent Dirichlet Process Spike Sorting. NIPS (2008)