Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale. - Matthias W. Seeger; Syama Sundar Rangapuram; Yuyang Wang 001; David Salinas; Jan Gasthaus; Tim Januschowski; Valentin Flunkert (CoRR, 2017)

Probabilistic Demand Forecasting at Scale. - Joos-Hendrik Boese; Valentin Flunkert; Jan Gasthaus; Tim Januschowski; Dustin Lange; David Salinas; Sebastian Schelter; Matthias W. Seeger; Bernie Wang 001 (Proc. VLDB Endow., 2017)

Deep State Space Models for Time Series Forecasting. - Syama Sundar Rangapuram; Matthias W. Seeger; Jan Gasthaus; Lorenzo Stella; Yuyang Wang 001; Tim Januschowski (NeurIPS, 2018)

Deep Factors with Gaussian Processes for Forecasting. - Danielle C. Maddix; Yuyang Wang 001; Alex Smola (CoRR, 2018)

Forecasting Big Time Series: Old and New. - Christos Faloutsos; Jan Gasthaus; Tim Januschowski; Yuyang Wang 001 (Proc. VLDB Endow., 2018)

Probabilistic Forecasting with Spline Quantile Function RNNs. - Jan Gasthaus; Konstantinos Benidis; Yuyang Wang 001; Syama Sundar Rangapuram; David Salinas; Valentin Flunkert; Tim Januschowski (AISTATS, 2019)

Deep Factors for Forecasting. - Yuyang Wang 001; Alex Smola; Danielle C. Maddix; Jan Gasthaus; Dean P. Foster; Tim Januschowski (ICML, 2019)

FastPoint: Scalable Deep Point Processes. - Ali Caner Türkmen; Yuyang Wang 001; Alexander J. Smola (ECML/PKDD (2), 2019)

Classical and Contemporary Approaches to Big Time Series Forecasting. - Christos Faloutsos; Jan Gasthaus; Tim Januschowski; Yuyang Wang 001 (SIGMOD Conference, 2019)

GluonTS: Probabilistic Time Series Models in Python. - Alexander Alexandrov 001; 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; et al. (CoRR, 2019)

Intermittent Demand Forecasting with Deep Renewal Processes. - Ali Caner Türkmen; Yuyang Wang 001; Tim Januschowski (CoRR, 2019)

Elastic Machine Learning Algorithms in Amazon SageMaker. - 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; et al. (SIGMOD Conference, 2020)

Forecasting Big Time Series: Theory and Practice. - Christos Faloutsos; Valentin Flunkert; Jan Gasthaus; Tim Januschowski; Yuyang Wang 001 (WWW (Companion Volume), 2020)

Neural forecasting: Introduction and literature overview. - Konstantinos Benidis; Syama Sundar Rangapuram; Valentin Flunkert; Bernie Wang 001; Danielle C. Maddix; Ali Caner Türkmen; Jan Gasthaus; Michael Bohlke-Schneider; David Salinas; Lorenzo Stella; Laurent Callot; Tim Januschowski (CoRR, 2020)

Intermittent Demand Forecasting with Renewal Processes. - Ali Caner Türkmen; Tim Januschowski; Yuyang Wang 001; Ali Taylan Cemgil (CoRR, 2020)

GluonTS: Probabilistic and Neural Time Series Modeling in Python. - Alexander Alexandrov 001; 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; et al. (J. Mach. Learn. Res., 2020)

GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics. - Ke Alexander Wang; Danielle C. Maddix; Yuyang Wang 001 (ICBINB@NeurIPS, 2021)

Correcting Exposure Bias for Link Recommendation. - Shantanu Gupta; Hao Wang 014; Zachary C. Lipton; Yuyang Wang 001 (ICML, 2021)

Variance Reduced Training with Stratified Sampling for Forecasting Models. - Yucheng Lu; Youngsuk Park; Lifan Chen; Yuyang Wang 001; Christopher De Sa; Dean P. Foster (ICML, 2021)

Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems. - Rui Wang 086; Danielle C. Maddix; Christos Faloutsos; Yuyang Wang 001; Rose Yu (L4DC, 2021)

Deep Explicit Duration Switching Models for Time Series. - Abdul Fatir Ansari; Konstantinos Benidis; Richard Kurle; Ali Caner Türkmen; Harold Soh; Alexander J. Smola; Bernie Wang 001; Tim Januschowski (NeurIPS, 2021)

Probabilistic Forecasting: A Level-Set Approach. - Hilaf Hasson; Bernie Wang 001; Tim Januschowski; Jan Gasthaus (NeurIPS, 2021)

Attention-based Domain Adaptation for Time Series Forecasting. - Xiaoyong Jin; Youngsuk Park; Danielle C. Maddix; Yuyang Wang 001; Xifeng Yan (CoRR, 2021)

Variance Reduction in Training Forecasting Models with Subgroup Sampling. - Yucheng Lu; Youngsuk Park; Lifan Chen; Yuyang Wang 001; Christopher De Sa; Dean P. Foster (CoRR, 2021)

Zero-Shot Recommender Systems. - Hao Ding 003; Yifei Ma; Anoop Deoras; Yuyang Wang 001; Hao Wang 014 (CoRR, 2021)

Dynamic Regret for Strongly Adaptive Methods and Optimality of Online KRR. - Dheeraj Baby; Hilaf Hasson; Yuyang Wang 001 (CoRR, 2021)

Context Uncertainty in Contextual Bandits with Applications to Recommender Systems. - Hao Wang 014; Yifei Ma; Hao Ding 003; Yuyang Wang 001 (AAAI, 2022)

Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting. - Youngsuk Park; Danielle C. Maddix; François-Xavier Aubet; Kelvin Kan; Jan Gasthaus; Yuyang Wang 001 (AISTATS, 2022)

Robust Probabilistic Time Series Forecasting. - Taeho Yoon; Youngsuk Park; Ernest K. Ryu; Yuyang Wang 001 (AISTATS, 2022)

Graph-Relational Domain Adaptation. - Zihao Xu 001; Hao He 011; Guang-He Lee; Bernie Wang 001; Hao Wang 014 (ICLR, 2022)

Domain Adaptation for Time Series Forecasting via Attention Sharing. - Xiaoyong Jin; Youngsuk Park; Danielle C. Maddix; Hao Wang 014; Yuyang Wang 001 (ICML, 2022)

8th SIGKDD International Workshop on Mining and Learning from Time Series - Deep Forecasting: Models, Interpretability, and Applications. - Sanjay Purushotham; Jun Huan; Cong Shen 001; Dongjin Song; Yuyang Wang 001; Jan Gasthaus; Hilaf Hasson; Youngsuk Park; Sungyong Seo; Yuriy Nevmyvaka (KDD, 2022)

Earthformer: Exploring Space-Time Transformers for Earth System Forecasting. - Zhihan Gao 001; Xingjian Shi; Hao Wang 014; Yi Zhu 001; Yuyang Wang 001; Mu Li 003; Dit-Yan Yeung (NeurIPS, 2022)

On the detrimental effect of invariances in the likelihood for variational inference. - Richard Kurle; Ralf Herbrich; Tim Januschowski; Yuyang Wang 001; Jan Gasthaus (NeurIPS, 2022)

Modeling Advection on Directed Graphs using Matérn Gaussian Processes for Traffic Flow. - Danielle C. Maddix; Nadim Saad; Yuyang Wang 001 (CoRR, 2022)

Criteria for Classifying Forecasting Methods. - Tim Januschowski; Jan Gasthaus; Yuyang Wang 001; David Salinas; Valentin Flunkert; Michael Bohlke-Schneider; Laurent Callot (CoRR, 2022)

First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting. - Xiyuan Zhang; Xiaoyong Jin; Karthick Gopalswamy; Gaurav Gupta; Youngsuk Park; Xingjian Shi; Hao Wang 014; Danielle C. Maddix; Yuyang Wang 001 (CoRR, 2022)

But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI. - Charles Marx; Youngsuk Park; Hilaf Hasson; Yuyang Wang 001; Stefano Ermon; Luke Huan (AISTATS, 2023)

Coherent Probabilistic Forecasting of Temporal Hierarchies. - Syama Sundar Rangapuram; Shubham Kapoor; Rajbir-Singh Nirwan; Pedro Mercado; Tim Januschowski; Yuyang Wang 001; Michael Bohlke-Schneider (AISTATS, 2023)

AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting. - Oleksandr Shchur; Ali Caner Türkmen; Nick Erickson; Huibin Shen; Alexander Shirkov; Tony Hu; Bernie Wang 001 (AutoML, 2023)

Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting. - Hilaf Hasson; Danielle C. Maddix; Bernie Wang 001; Gaurav Gupta; Youngsuk Park (ICML, 2023)

PreDiff: Precipitation Nowcasting with Latent Diffusion Models. - Zhihan Gao 001; Xingjian Shi; Boran Han; Hao Wang 014; Xiaoyong Jin; Danielle C. Maddix; Yi Zhu 001; Mu Li 003; Yuyang Wang 001 (NeurIPS, 2023)

Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting. - Marcel Kollovieh; Abdul Fatir Ansari; Michael Bohlke-Schneider; Jasper Zschiegner; Hao Wang 014; Yuyang Wang 001 (NeurIPS, 2023)

Testing Causality for High Dimensional Data. - Arun Jambulapati; Hilaf Hasson; Youngsuk Park; Yuyang Wang 001 (CoRR, 2023)

Deep Non-Parametric Time Series Forecaster. - Syama Sundar Rangapuram; Jan Gasthaus; Lorenzo Stella; Valentin Flunkert; David Salinas; Yuyang Wang 001; Tim Januschowski (CoRR, 2023)

Deep Learning for Time Series Forecasting: Tutorial and Literature Survey. - Konstantinos Benidis; Syama Sundar Rangapuram; Valentin Flunkert; Yuyang Wang 001; Danielle C. Maddix; Ali Caner Türkmen; Jan Gasthaus; Michael Bohlke-Schneider; David Salinas; Lorenzo Stella; François-Xavier Aubet; Laurent Callot; et al. (ACM Comput. Surv., 2023)

Discovering Bias in Latent Space: An Unsupervised Debiasing Approach. - Dyah Adila; Shuai Zhang 007; Boran Han; Bernie Wang 001 (ICML, 2024)

Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs. - S. Chandra Mouli; Danielle C. Maddix; Shima Alizadeh; Gaurav Gupta; Andrew Stuart; Michael W. Mahoney; Bernie Wang 001 (ICML, 2024)

Transferring Knowledge From Large Foundation Models to Small Downstream Models. - Shikai Qiu; Boran Han; Danielle C. Maddix; Shuai Zhang 007; Bernie Wang 001; Andrew Gordon Wilson (ICML, 2024)

PipeRAG: Fast Retrieval-Augmented Generation via Algorithm-System Co-design. - Wenqi Jiang 001; Shuai Zhang 007; Boran Han; Jie Wang 022; Bernie Wang 001; Tim Kraska (CoRR, 2024)

Chronos: Learning the Language of Time Series. - 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; et al. (CoRR, 2024)

Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics. - Matthias Karlbauer; Danielle C. Maddix; Abdul Fatir Ansari; Boran Han; Gaurav Gupta; Yuyang Wang 001; Andrew Stuart; Michael W. Mahoney (CoRR, 2024)

Hard Constraint Guided Flow Matching for Gradient-Free Generation of PDE Solutions. - Chaoran Cheng; Boran Han; Danielle C. Maddix; Abdul Fatir Ansari; Andrew Stuart; Michael W. Mahoney; Yuyang Wang 001 (CoRR, 2024)

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization. - 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 001 (CoRR, 2024)

Chronos: Learning the Language of Time Series. - 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; et al. (Trans. Mach. Learn. Res., 2024)

A Flexible Forecasting Stack. - Tim Januschowski; Yuyang Wang 001; Jan Gasthaus; Syama Sundar Rangapuram; Caner Türkmen; Jasper Zschiegner; Lorenzo Stella; Michael Bohlke-Schneider; Danielle C. Maddix; Konstantinos Benidis; Alexander Alexandrov 001; Christos Faloutsos; et al. (Proc. VLDB Endow., 2024)

ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables. - Sebastian Pineda Arango; Pedro Mercado; Shubham Kapoor; Abdul Fatir Ansari; Lorenzo Stella; Huibin Shen; Hugo Senetaire; Ali Caner Türkmen; Oleksandr Shchur; Danielle C. Maddix; Michael Bohlke-Schneider; Bernie Wang 001; et al. (AISTATS, 2025)

Adapting to Online Distribution Shifts in Deep Learning: A Black-Box Approach. - Dheeraj Baby; Boran Han; Shuai Zhang 007; Cuixiong Hu; Bernie Wang 001; Yu-Xiang Wang (AISTATS, 2025)

Gradient-Free Generation for Hard-Constrained Systems. - Chaoran Cheng; Boran Han; Danielle C. Maddix; Abdul Fatir Ansari; Andrew Stuart; Michael W. Mahoney; Bernie Wang 001 (ICLR, 2025)

PipeRAG: Fast Retrieval-Augmented Generation via Adaptive Pipeline Parallelism. - Wenqi Jiang 001; Shuai Zhang 007; Boran Han; Jie Wang 022; Bernie Wang 001; Tim Kraska (KDD (1), 2025)

Effectively Steer LLM To Follow Preference via Building Confident Directions. - Bingqing Song; Boran Han; Shuai Zhang 007; Hao Wang 014; Haoyang Fang; Bonan Min; Yuyang Wang 001; Mingyi Hong 001 (CoRR, 2025)

ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables. - 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 001; et al. (CoRR, 2025)

Visual Instruction Tuning with Chain of Region-of-Interest. - Yixin Chen; Shuai Zhang 007; Boran Han; Bernie Wang 001 (CoRR, 2025)

MLZero: A Multi-Agent System for End-to-end Machine Learning Automation. - Haoyang Fang; Boran Han; Nick Erickson; Xiyuan Zhang; Su Zhou; Anirudh Dagar; Jiani Zhang 003; Ali Caner Türkmen; Cuixiong Hu; Huzefa Rangwala; Ying Nian Wu; Yuyang Wang 001; et al. (CoRR, 2025)

Zero-Shot Time Series Forecasting with Covariates via In-Context Learning. - Andreas Auer; Raghul Parthipan; Pedro Mercado; Abdul Fatir Ansari; Lorenzo Stella; Yuyang Wang 001; Michael Bohlke-Schneider; Syama Sundar Rangapuram (CoRR, 2025)

End-to-End Probabilistic Framework for Learning with Hard Constraints. - Utkarsh Utkarsh; Danielle C. Maddix; Ruijun Ma; Michael W. Mahoney; Yuyang Wang 001 (CoRR, 2025)

CrEst: Credibility Estimation for Contexts in LLMs via Weak Supervision. - Dyah Adila; Shuai Zhang 007; Boran Han; Bonan Min; Yuyang Wang 001 (CoRR, 2025)

Does Multimodality Lead to Better Time Series Forecasting? - Xiyuan Zhang; Boran Han; Haoyang Fang; Abdul Fatir Ansari; Shuai Zhang 007; Danielle C. Maddix; Cuixiong Hu; Andrew Gordon Wilson; Michael W. Mahoney; Hao Wang 014; Yan Liu; Huzefa Rangwala; et al. (CoRR, 2025)