Yuyang Wang

Sebastian Pineda-Arango

CoRR, 2024

PipeRAG: Fast Retrieval-Augmented Generation via Algorithm-System Co-design.

[BibT_eX]

[DOI]

CoRR, 2024

2023

Deep Learning for Time Series Forecasting: Tutorial and Literature Survey.

[BibT_eX]

[DOI]

David Salinas

Lorenzo Stella

François-Xavier Aubet

Laurent Callot

Tim Januschowski

ACM Comput. Surv., 2023

Deep Non-Parametric Time Series Forecaster.

[BibT_eX]

[DOI]

CoRR, 2023

Testing Causality for High Dimensional Data.

[BibT_eX]

[DOI]

CoRR, 2023

Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting.

[BibT_eX]

[DOI]

Marcel Kollovieh

Abdul Fatir Ansari

Jasper Zschiegner

Hao Wang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

PreDiff: Precipitation Nowcasting with Latent Diffusion Models.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Automated Machine Learning, 2023

Coherent Probabilistic Forecasting of Temporal Hierarchies.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting.

[BibT_eX]

[DOI]

CoRR, 2022

Criteria for Classifying Forecasting Methods.

[BibT_eX]

[DOI]

Laurent Callot

CoRR, 2022

Modeling Advection on Directed Graphs using Matérn Gaussian Processes for Traffic Flow.

[BibT_eX]

[DOI]

Nadim Saad

CoRR, 2022

On the detrimental effect of invariances in the likelihood for variational inference.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Earthformer: Exploring Space-Time Transformers for Earth System Forecasting.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

8th SIGKDD International Workshop on Mining and Learning from Time Series - Deep Forecasting: Models, Interpretability, and Applications.

[BibT_eX]

[DOI]

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Domain Adaptation for Time Series Forecasting via Attention Sharing.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Graph-Relational Domain Adaptation.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Robust Probabilistic Time Series Forecasting.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting.

[BibT_eX]

[DOI]

Youngsuk Park

François-Xavier Aubet

Kelvin Kan

Jan Gasthaus

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Context Uncertainty in Contextual Bandits with Applications to Recommender Systems.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Dynamic Regret for Strongly Adaptive Methods and Optimality of Online KRR.

[BibT_eX]

[DOI]

Dheeraj Baby

Hilaf Hasson

CoRR, 2021

Zero-Shot Recommender Systems.

[BibT_eX]

[DOI]

CoRR, 2021

Variance Reduction in Training Forecasting Models with Subgroup Sampling.

[BibT_eX]

[DOI]

CoRR, 2021

Attention-based Domain Adaptation for Time Series Forecasting.

[BibT_eX]

[DOI]

CoRR, 2021

Probabilistic Forecasting: A Level-Set Approach.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Deep Explicit Duration Switching Models for Time Series.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems.

[BibT_eX]

[DOI]

Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

Variance Reduced Training with Stratified Sampling for Forecasting Models.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics.

[BibT_eX]

[DOI]

Ke Alexander Wang

Proceedings of the I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021, 2021

2020

GluonTS: Probabilistic and Neural Time Series Modeling in Python.

[BibT_eX]

[DOI]

Alexander Alexandrov

J. Mach. Learn. Res., 2020

Intermittent Demand Forecasting with Renewal Processes.

[BibT_eX]

[DOI]

CoRR, 2020

Neural forecasting: Introduction and literature overview.

[BibT_eX]

[DOI]

CoRR, 2020

Forecasting Big Time Series: Theory and Practice.

[BibT_eX]

[DOI]

Proceedings of the Companion of The 2020 Web Conference 2020, 2020

Elastic Machine Learning Algorithms in Amazon SageMaker.

[BibT_eX]

[DOI]

Proceedings of the 2020 International Conference on Management of Data, 2020

2019

Intermittent Demand Forecasting with Deep Renewal Processes.

[BibT_eX]

[DOI]

Ali Caner Türkmen

Tim Januschowski

CoRR, 2019

GluonTS: Probabilistic Time Series Models in Python.

[BibT_eX]

[DOI]

Alexander Alexandrov

CoRR, 2019

Classical and Contemporary Approaches to Big Time Series Forecasting.

[BibT_eX]

[DOI]

Proceedings of the 2019 International Conference on Management of Data, 2019

FastPoint: Scalable Deep Point Processes.

[BibT_eX]

[DOI]

Ali Caner Türkmen

Alexander J. Smola

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Deep Factors for Forecasting.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

Probabilistic Forecasting with Spline Quantile Function RNNs.

[BibT_eX]

[DOI]

Jan Gasthaus

David Salinas

Valentin Flunkert

Tim Januschowski

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Forecasting Big Time Series: Old and New.

[BibT_eX]

[DOI]

Proc. VLDB Endow., 2018

Deep Factors with Gaussian Processes for Forecasting.

[BibT_eX]

[DOI]

Alex Smola

CoRR, 2018

Deep State Space Models for Time Series Forecasting.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Probabilistic Demand Forecasting at Scale.

[BibT_eX]

[DOI]

Proc. VLDB Endow., 2017

Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale.

[BibT_eX]

[DOI]

Matthias W. Seeger