368 citations · 725 across the 24 of their papers we have counts for
15 papers · 1 filter
AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting
Oleksandr Shchur, Caner Turkmen, Nick Erickson +4
We introduce AutoGluon-TimeSeries - an open-source AutoML library for probabilistic time series forecasting. Focused on ease of use and robustness, AutoGluon-TimeSeries enables use…
PreDiff: Precipitation Nowcasting with Latent Diffusion Models
Zhihan Gao, Xingjian Shi, Boran Han +6
Earth system forecasting has traditionally relied on complex physical models that are computationally expensive and require significant domain expertise. In the past decade, the un…
On the detrimental effect of invariances in the likelihood for variational inference
Richard Kurle, Ralf Herbrich, Tim Januschowski +2
Variational Bayesian posterior inference often requires simplifying approximations such as mean-field parametrisation to ensure tractability. However, prior work has associated the…
Correcting Exposure Bias for Link Recommendation
Shantanu Gupta, Hao Wang, Zachary C. Lipton +1
Link prediction methods are frequently applied in recommender systems, e.g., to suggest citations for academic papers or friends in social networks. However, exposure bias can aris…
Zero-Shot Recommender Systems
Hao Ding, Yifei Ma, Anoop Deoras +2
Performance of recommender systems (RS) relies heavily on the amount of training data available. This poses a chicken-and-egg problem for early-stage products, whose amount of data…
Variance Reduced Training with Stratified Sampling for Forecasting Models
Yucheng Lu, Youngsuk Park, Lifan Chen +3
In large-scale time series forecasting, one often encounters the situation where the temporal patterns of time series, while drifting over time, differ from one another in the same…
Domain Adaptation for Time Series Forecasting via Attention Sharing
Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix +2
Recently, deep neural networks have gained increasing popularity in the field of time series forecasting. A primary reason for their success is their ability to effectively capture…
Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems
Rui Wang, Danielle Maddix, Christos Faloutsos +2
How can we learn a dynamical system to make forecasts, when some variables are unobserved? For instance, in COVID-19, we want to forecast the number of infected and death cases but…
Intermittent Demand Forecasting with Renewal Processes
Ali Caner Turkmen, Tim Januschowski, Yuyang Wang +1
Intermittency is a common and challenging problem in demand forecasting. We introduce a new, unified framework for building intermittent demand forecasting models, which incorporat…
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert +10
Deep learning based forecasting methods have become the methods of choice in many applications of time series prediction or forecasting often outperforming other approaches. Conseq…
Intermittent Demand Forecasting with Deep Renewal Processes
Ali Caner Turkmen, Yuyang Wang, Tim Januschowski
Intermittent demand, where demand occurrences appear sporadically in time, is a common and challenging problem in forecasting. In this paper, we first make the connections between…
GluonTS: Probabilistic Time Series Models in Python
Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider +10
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts.mxnet.io), a library for deep-learning-based time series modeling. GluonTS simplifies the development of and…
Nonparametric Bayesian Mixed-effect Model: a Sparse Gaussian Process Approach
Yuyang Wang, Roni Khardon
Multi-task learning models using Gaussian processes (GP) have been developed and successfully applied in various applications. The main difficulty with this approach is the computa…
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes
Yuyang Wang, Roni Khardon, Pavlos Protopapas
Multi-task learning leverages shared information among data sets to improve the learning performance of individual tasks. The paper applies this framework for data where each task…
Nonparametric Bayesian Estimation of Periodic Functions
Yuyang Wang, Roni Khardon, Pavlos Protopapas
Many real world problems exhibit patterns that have periodic behavior. For example, in astrophysics, periodic variable stars play a pivotal role in understanding our universe. An i…