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Jan Gasthaus
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2020 – today
- 2024
- [j7]Tim Januschowski, Yuyang Wang, Jan Gasthaus, Syama Sundar Rangapuram, Caner Turkmen, Jasper Zschiegner, Lorenzo Stella, Michael Bohlke-Schneider, Danielle C. Maddix, Konstantinos Benidis, Alexander Alexandrov, Christos Faloutsos, Sebastian Schelter:
A Flexible Forecasting Stack. Proc. VLDB Endow. 17(12): 3883-3892 (2024) - 2023
- [j6]Konstantinos Benidis
, Syama Sundar Rangapuram
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey. ACM Comput. Surv. 55(6): 121:1-121:36 (2023) - [i22]Syama Sundar Rangapuram, Jan Gasthaus, Lorenzo Stella, Valentin Flunkert, David Salinas, Yuyang Wang, Tim Januschowski:
Deep Non-Parametric Time Series Forecaster. CoRR abs/2312.14657 (2023) - 2022
- [c23]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: 8127-8150 - [c22]Kelvin Kan, François-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus:
Multivariate Quantile Function Forecaster. AISTATS 2022: 10603-10621 - [c21]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 - [c20]Chris U. Carmona, François-Xavier Aubet, Valentin Flunkert, Jan Gasthaus:
Neural Contextual Anomaly Detection for Time Series. IJCAI 2022: 2843-2851 - [c19]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: 4896-4897 - [c18]Richard Kurle, Ralf Herbrich, Tim Januschowski, Yuyang Wang, Jan Gasthaus:
On the detrimental effect of invariances in the likelihood for variational inference. NeurIPS 2022 - [i21]Christian Bock, François-Xavier Aubet, Jan Gasthaus, Andrey Kan, Ming Chen, Laurent Callot:
Online Time Series Anomaly Detection with State Space Gaussian Processes. CoRR abs/2201.06763 (2022) - [i20]Kelvin Kan, François-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus:
Multivariate Quantile Function Forecaster. CoRR abs/2202.11316 (2022) - [i19]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 abs/2203.11103 (2022) - [i18]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 abs/2206.14342 (2022) - [i17]Richard Kurle, Ralf Herbrich, Tim Januschowski, Yuyang Wang, Jan Gasthaus:
On the detrimental effect of invariances in the likelihood for variational inference. CoRR abs/2209.07157 (2022) - [i16]Tim Januschowski, Jan Gasthaus, Yuyang Wang, David Salinas, Valentin Flunkert, Michael Bohlke-Schneider, Laurent Callot:
Criteria for Classifying Forecasting Methods. CoRR abs/2212.03523 (2022) - 2021
- [c17]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: 8832-8843 - [c16]Hilaf Hasson, Bernie Wang, Tim Januschowski, Jan Gasthaus:
Probabilistic Forecasting: A Level-Set Approach. NeurIPS 2021: 6404-6416 - [c15]Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann:
Detecting Anomalous Event Sequences with Temporal Point Processes. NeurIPS 2021: 13419-13431 - [i15]Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann:
Detecting Anomalous Event Sequences with Temporal Point Processes. CoRR abs/2106.04465 (2021) - [i14]Chris U. Carmona
Neural Contextual Anomaly Detection for Time Series. CoRR abs/2107.07702 (2021) - [i13]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 abs/2108.00981 (2021) - [i12]Daniel Zügner, François-Xavier Aubet, Victor Garcia Satorras, Tim Januschowski, Stephan Günnemann, Jan Gasthaus:
A Study of Joint Graph Inference and Forecasting. CoRR abs/2109.04979 (2021) - [i11]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 abs/2111.06581 (2021) - [i10]François-Xavier Aubet, Daniel Zügner, Jan Gasthaus:
Monte Carlo EM for Deep Time Series Anomaly Detection. CoRR abs/2112.14436 (2021) - 2020
- [b1]Jan Gasthaus:
Hierarchical Bayesian nonparametric models for power-law sequences. University College London (University of London), UK, 2020 - [j5]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: 116:1-116:6 (2020) - [c14]Fadhel Ayed, Lorenzo Stella, Tim Januschowski, Jan Gasthaus:
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models. ICSOC Workshops 2020: 97-109 - [c13]Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus:
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting. NeurIPS 2020 - [c12]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: 731-737 - [c11]Christos Faloutsos, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Yuyang Wang:
Forecasting Big Time Series: Theory and Practice. WWW (Companion Volume) 2020: 320-321 - [i9]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 abs/2004.10240 (2020) - [i8]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 abs/2005.10111 (2020) - [i7]Fadhel Ayed, Lorenzo Stella, Tim Januschowski, Jan Gasthaus:
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models. CoRR abs/2007.15541 (2020)
2010 – 2019
- 2019
- [c10]Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, Tim Januschowski:
Probabilistic Forecasting with Spline Quantile Function RNNs. AISTATS 2019: 1901-1910 - [c9]Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean P. Foster, Tim Januschowski:
Deep Factors for Forecasting. ICML 2019: 6607-6617 - [c8]Christos Faloutsos, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Yuyang Wang:
Forecasting Big Time Series: Theory and Practice. KDD 2019: 3209-3210 - [c7]David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus:
High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes. NeurIPS 2019: 6824-6834 - [c6]Christos Faloutsos, Jan Gasthaus, Tim Januschowski, Yuyang Wang:
Classical and Contemporary Approaches to Big Time Series Forecasting. SIGMOD Conference 2019: 2042-2047 - [i6]Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean P. Foster, Tim Januschowski:
Deep Factors for Forecasting. CoRR abs/1905.12417 (2019) - [i5]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 abs/1906.05264 (2019) - [i4]David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus:
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes. CoRR abs/1910.03002 (2019) - 2018
- [j4]Christos Faloutsos, Jan Gasthaus, Tim Januschowski, Yuyang Wang:
Forecasting Big Time Series: Old and New. Proc. VLDB Endow. 11(12): 2102-2105 (2018) - [c5]Syama Sundar Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, Tim Januschowski:
Deep State Space Models for Time Series Forecasting. NeurIPS 2018: 7796-7805 - 2017
- [j3]Jacquelyn A. Shelton, Jan Gasthaus, Zhenwen Dai, Jörg Lücke, Arthur Gretton
GP-Select: Accelerating EM Using Adaptive Subspace Preselection. Neural Comput. 29(8): 2177-2202 (2017) - [j2]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): 1694-1705 (2017) - [i3]Valentin Flunkert, David Salinas, Jan Gasthaus:
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. CoRR abs/1704.04110 (2017) - [i2]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 abs/1709.07638 (2017) - 2014
- [i1]Jacquelyn A. Shelton, Jan Gasthaus, Zhenwen Dai, Jörg Lücke, Arthur Gretton:
GP-select: Accelerating EM using adaptive subspace preselection. CoRR abs/1412.3411 (2014) - 2011
- [j1]Frank D. Wood, Jan Gasthaus, Cédric Archambeau, Lancelot James, Yee Whye Teh:
The sequence memoizer. Commun. ACM 54(2): 91-98 (2011) - 2010
- [c4]Jan Gasthaus, Frank D. Wood, Yee Whye Teh:
Lossless Compression Based on the Sequence Memoizer. DCC 2010: 337-345 - [c3]Jan Gasthaus, Yee Whye Teh:
Improvements to the Sequence Memoizer. NIPS 2010: 685-693
2000 – 2009
- 2009
- [c2]Frank D. Wood, Cédric Archambeau, Jan Gasthaus, Lancelot James, Yee Whye Teh:
A stochastic memoizer for sequence data. ICML 2009: 1129-1136 - 2008
- [c1]Jan Gasthaus, Frank D. Wood, Dilan Görür, Yee Whye Teh:
Dependent Dirichlet Process Spike Sorting. NIPS 2008: 497-504
Coauthor Index
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last updated on 2024-11-08 20:29 CET by the dblp team
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