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ORCIDYuyang Wang 0001
Bernie Wang 0001
Person information
- affiliation: Amazon Web Services, AWS, AI Labs, Palo Alto, CA, USA
- affiliation: Amazon Research, Amazon Development Center, Berlin, Germany
Other persons with the same name
- Yuyang Wang (aka: Yu-Yang Wang) — disambiguation page
- Yuyang Wang 0002
![[0000-0003-0242-8935]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0003-0242-8935")
— HESAM University, Hautes Écoles Sorbonne Arts et Métiers Institute of Technology, LISPEN, France (and 1 more) - Yuyang Wang 0003 — Columbia University, New York, NY, USA (and 2 more)
- Yuyang Wang 0004 — Apple Inc., Cupertino, CA, USA (and 1 more)
- Yuyang Wang 0005 — Carnegie Mellon University, Department of Mechanical Engineering, Pittsburgh, PA, USA
- Bernie Wang 0002 — University of California, Berkeley AI Research, CA, USA
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2020 – today
- 2025
[c32]Wenqi Jiang, Shuai Zhang, Boran Han, Jie Wang, Bernie Wang, Tim Kraska:
PipeRAG: Fast Retrieval-Augmented Generation via Adaptive Pipeline Parallelism. KDD (1) 2025: 589-600- [i38]Sebastian Pineda-Arango, Pedro Mercado, Shubham Kapoor, Abdul Fatir Ansari, Lorenzo Stella, Huibin Shen, Hugo Senetaire, Caner Turkmen, Oleksandr Shchur, Danielle C. Maddix, Michael Bohlke-Schneider, Yuyang Wang, Syama Sundar Rangapuram:
ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables. CoRR abs/2503.12107 (2025) - 2024
- [j5]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) - [c31]Dyah Adila, Shuai Zhang, Boran Han, Bernie Wang:
Discovering Bias in Latent Space: An Unsupervised Debiasing Approach. ICML 2024 - [c30]S. Chandra Mouli, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Andrew Stuart, Michael W. Mahoney, Bernie Wang:
Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs. ICML 2024 - [c29]Shikai Qiu, Boran Han, Danielle C. Maddix, Shuai Zhang, Bernie Wang, Andrew Gordon Wilson:
Transferring Knowledge From Large Foundation Models to Small Downstream Models. ICML 2024 - [i37]Wenqi Jiang, Shuai Zhang, Boran Han, Jie Wang, Bernie Wang, Tim Kraska:
PipeRAG: Fast Retrieval-Augmented Generation via Algorithm-System Co-design. CoRR abs/2403.05676 (2024) - [i36]Abdul Fatir Ansari, Lorenzo Stella, Ali Caner Türkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda-Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang:
Chronos: Learning the Language of Time Series. CoRR abs/2403.07815 (2024) - [i35]S. Chandra Mouli, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Andrew Stuart, Michael W. Mahoney, Yuyang Wang:
Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs. CoRR abs/2403.10642 (2024) - [i34]Shikai Qiu, Boran Han, Danielle C. Maddix, Shuai Zhang, Yuyang Wang, Andrew Gordon Wilson:
Transferring Knowledge from Large Foundation Models to Small Downstream Models. CoRR abs/2406.07337 (2024) - [i33]Matthias Karlbauer, Danielle C. Maddix, Abdul Fatir Ansari, Boran Han, Gaurav Gupta, Yuyang Wang, Andrew Stuart, Michael W. Mahoney:
Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics. CoRR abs/2407.14129 (2024) - [i32]Chaoran Cheng, Boran Han, Danielle C. Maddix, Abdul Fatir Ansari, Andrew Stuart, Michael W. Mahoney, Yuyang Wang:
Hard Constraint Guided Flow Matching for Gradient-Free Generation of PDE Solutions. CoRR abs/2412.01786 (2024) - [i31]Luca Masserano, Abdul Fatir Ansari, Boran Han, Xiyuan Zhang, Christos Faloutsos, Michael W. Mahoney, Andrew Gordon Wilson, Youngsuk Park, Syama Sundar Rangapuram, Danielle C. Maddix, Yuyang Wang:
Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization. CoRR abs/2412.05244 (2024) - 2023
- [j4]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): 121:1-121:36 (2023) - [c28]Charles Marx, Youngsuk Park, Hilaf Hasson, Yuyang Wang, Stefano Ermon, Luke Huan:
But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI. AISTATS 2023: 7375-7391 - [c27]Syama Sundar Rangapuram, Shubham Kapoor, Rajbir-Singh Nirwan, Pedro Mercado, Tim Januschowski, Yuyang Wang, Michael Bohlke-Schneider:
Coherent Probabilistic Forecasting of Temporal Hierarchies. AISTATS 2023: 9362-9376 - [c26]Oleksandr Shchur, Ali Caner Türkmen, Nick Erickson, Huibin Shen, Alexander Shirkov, Tony Hu, Bernie Wang:
AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting. AutoML 2023: 9/1-21 - [c25]Hilaf Hasson, Danielle C. Maddix, Bernie Wang, Gaurav Gupta, Youngsuk Park:
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting. ICML 2023: 12616-12632 - [c24]Zhihan Gao, Xingjian Shi, Boran Han, Hao Wang, Xiaoyong Jin, Danielle C. Maddix, Yi Zhu, Mu Li, Yuyang Wang:
PreDiff: Precipitation Nowcasting with Latent Diffusion Models. NeurIPS 2023 - [c23]Marcel Kollovieh, Abdul Fatir Ansari, Michael Bohlke-Schneider, Jasper Zschiegner, Hao Wang, Yuyang Wang:
Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting. NeurIPS 2023 - [i30]Arun Jambulapati, Hilaf Hasson, Youngsuk Park, Yuyang Wang:
Testing Causality for High Dimensional Data. CoRR abs/2303.07774 (2023) - [i29]Hilaf Hasson, Danielle C. Maddix, Yuyang Wang, Gaurav Gupta, Youngsuk Park:
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting. CoRR abs/2305.15786 (2023) - [i28]Zhihan Gao, Xingjian Shi, Boran Han, Hao Wang, Xiaoyong Jin, Danielle C. Maddix, Yi Zhu, Mu Li, Yuyang Wang:
PreDiff: Precipitation Nowcasting with Latent Diffusion Models. CoRR abs/2307.10422 (2023) - [i27]Marcel Kollovieh, Abdul Fatir Ansari, Michael Bohlke-Schneider, Jasper Zschiegner, Hao Wang, Yuyang Wang:
Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting. CoRR abs/2307.11494 (2023) - [i26]Oleksandr Shchur, Ali Caner Türkmen, Nick Erickson, Huibin Shen, Alexander Shirkov, Tony Hu, Yuyang Wang:
AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting. CoRR abs/2308.05566 (2023) - [i25]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
- [c22]Hao Wang, Yifei Ma, Hao Ding, Yuyang Wang:
Context Uncertainty in Contextual Bandits with Applications to Recommender Systems. AAAI 2022: 8539-8547 - [c21]Taeho Yoon, Youngsuk Park, Ernest K. Ryu, Yuyang Wang:
Robust Probabilistic Time Series Forecasting. AISTATS 2022: 1336-1358 - [c20]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 - [c19]Zihao Xu, Hao He, Guang-He Lee, Bernie Wang, Hao Wang:
Graph-Relational Domain Adaptation. ICLR 2022 - [c18]Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Hao Wang, Yuyang Wang:
Domain Adaptation for Time Series Forecasting via Attention Sharing. ICML 2022: 10280-10297 - [c17]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 - [c16]Zhihan Gao, Xingjian Shi, Hao Wang, Yi Zhu, Yuyang Wang, Mu Li, Dit-Yan Yeung:
Earthformer: Exploring Space-Time Transformers for Earth System Forecasting. NeurIPS 2022 - [c15]Richard Kurle, Ralf Herbrich, Tim Januschowski, Yuyang Wang, Jan Gasthaus:
On the detrimental effect of invariances in the likelihood for variational inference. NeurIPS 2022 - [i24]Danielle C. Maddix, Nadim Saad, Yuyang Wang:
Modeling Advection on Directed Graphs using Matérn Gaussian Processes for Traffic Flow. CoRR abs/2201.00001 (2022) - [i23]Hao Wang, Yifei Ma, Hao Ding, Yuyang Wang:
Context Uncertainty in Contextual Bandits with Applications to Recommender Systems. CoRR abs/2202.00805 (2022) - [i22]Zihao Xu, Hao He, Guang-He Lee, Yuyang Wang, Hao Wang:
Graph-Relational Domain Adaptation. CoRR abs/2202.03628 (2022) - [i21]Taeho Yoon, Youngsuk Park, Ernest K. Ryu, Yuyang Wang:
Robust Probabilistic Time Series Forecasting. CoRR abs/2202.11910 (2022) - [i20]Zhihan Gao, Xingjian Shi, Hao Wang, Yi Zhu, Yuyang Wang, Mu Li, Dit-Yan Yeung:
Earthformer: Exploring Space-Time Transformers for Earth System Forecasting. CoRR abs/2207.05833 (2022) - [i19]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) - [i18]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) - [i17]Xiyuan Zhang, Xiaoyong Jin, Karthick Gopalswamy, Gaurav Gupta, Youngsuk Park, Xingjian Shi, Hao Wang, Danielle C. Maddix, Yuyang Wang:
First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting. CoRR abs/2212.08151 (2022) - 2021
- [c14]Ke Alexander Wang, Danielle C. Maddix, Yuyang Wang:
GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics. ICBINB@NeurIPS 2021: 80-85 - [c13]Shantanu Gupta, Hao Wang, Zachary C. Lipton, Yuyang Wang:
Correcting Exposure Bias for Link Recommendation. ICML 2021: 3953-3963 - [c12]Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean P. Foster:
Variance Reduced Training with Stratified Sampling for Forecasting Models. ICML 2021: 7145-7155 - [c11]Rui Wang, Danielle C. Maddix, Christos Faloutsos, Yuyang Wang, Rose Yu:
Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems. L4DC 2021: 385-398 - [c10]Hilaf Hasson, Bernie Wang, Tim Januschowski, Jan Gasthaus:
Probabilistic Forecasting: A Level-Set Approach. NeurIPS 2021: 6404-6416 - [c9]Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle, Ali Caner Türkmen, Harold Soh, Alexander J. Smola, Bernie Wang, Tim Januschowski:
Deep Explicit Duration Switching Models for Time Series. NeurIPS 2021: 29949-29961 - [i16]Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Yuyang Wang, Xifeng Yan:
Attention-based Domain Adaptation for Time Series Forecasting. CoRR abs/2102.06828 (2021) - [i15]Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean P. Foster:
Variance Reduction in Training Forecasting Models with Subgroup Sampling. CoRR abs/2103.02062 (2021) - [i14]Hao Ding, Yifei Ma, Anoop Deoras, Yuyang Wang, Hao Wang:
Zero-Shot Recommender Systems. CoRR abs/2105.08318 (2021) - [i13]Shantanu Gupta, Hao Wang, Zachary C. Lipton, Yuyang Wang:
Correcting Exposure Bias for Link Recommendation. CoRR abs/2106.07041 (2021) - [i12]Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle, Ali Caner Türkmen, Harold Soh, Alexander J. Smola, Yuyang Wang, Tim Januschowski:
Deep Explicit Duration Switching Models for Time Series. CoRR abs/2110.13878 (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]Dheeraj Baby, Hilaf Hasson, Yuyang Wang:
Dynamic Regret for Strongly Adaptive Methods and Optimality of Online KRR. CoRR abs/2111.11550 (2021) - [i9]Ke Alexander Wang, Danielle C. Maddix, Yuyang Wang:
GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics. CoRR abs/2112.09964 (2021) - 2020
- [j3]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) - [c8]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 - [c7]Christos Faloutsos, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Yuyang Wang:
Forecasting Big Time Series: Theory and Practice. WWW (Companion Volume) 2020: 320-321 - [i8]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) - [i7]Ali Caner Türkmen, Tim Januschowski, Yuyang Wang, Ali Taylan Cemgil:
Intermittent Demand Forecasting with Renewal Processes. CoRR abs/2010.01550 (2020) - [i6]Rui Wang, Danielle C. Maddix, Christos Faloutsos, Yuyang Wang, Rose Yu:
Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems. CoRR abs/2011.10616 (2020)
2010 – 2019
- 2019
- [c6]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 - [c5]Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean P. Foster, Tim Januschowski:
Deep Factors for Forecasting. ICML 2019: 6607-6617 - [c4]Christos Faloutsos, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Yuyang Wang:
Forecasting Big Time Series: Theory and Practice. KDD 2019: 3209-3210 - [c3]Ali Caner Türkmen, Yuyang Wang, Alexander J. Smola:
FastPoint: Scalable Deep Point Processes. ECML/PKDD (2) 2019: 465-480 - [c2]Christos Faloutsos, Jan Gasthaus, Tim Januschowski, Yuyang Wang:
Classical and Contemporary Approaches to Big Time Series Forecasting. SIGMOD Conference 2019: 2042-2047 - [i5]Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean P. Foster, Tim Januschowski:
Deep Factors for Forecasting. CoRR abs/1905.12417 (2019) - [i4]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) - [i3]Ali Caner Türkmen, Yuyang Wang, Tim Januschowski:
Intermittent Demand Forecasting with Deep Renewal Processes. CoRR abs/1911.10416 (2019) - 2018
- [j2]Christos Faloutsos, Jan Gasthaus, Tim Januschowski, Yuyang Wang:
Forecasting Big Time Series: Old and New. Proc. VLDB Endow. 11(12): 2102-2105 (2018) - [c1]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 - [i2]Danielle C. Maddix, Yuyang Wang, Alex Smola:
Deep Factors with Gaussian Processes for Forecasting. CoRR abs/1812.00098 (2018) - 2017
- [j1]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) - [i1]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)
Coauthor Index
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