104 citations · 167 across the 12 of their papers we have counts for
12 papers
Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting
Marcel Kollovieh, Abdul Fatir Ansari, Michael Bohlke-Schneider +3
Diffusion models have achieved state-of-the-art performance in generative modeling tasks across various domains. Prior works on time series diffusion models have primarily focused…
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting
Hilaf Hasson, Danielle C. Maddix, Yuyang Wang +2
Ensembling is among the most popular tools in machine learning (ML) due to its effectiveness in minimizing variance and thus improving generalization. Most ensembling methods for b…
Testing Causality for High Dimensional Data
Arun Jambulapati, Hilaf Hasson, Youngsuk Park +1
Determining causal relationship between high dimensional observations are among the most important tasks in scientific discoveries. In this paper, we revisited the \emph{linear tra…
First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting
Xiyuan Zhang, Xiaoyong Jin, Karthick Gopalswamy +6
Transformer-based models have gained large popularity and demonstrated promising results in long-term time-series forecasting in recent years. In addition to learning attention in…
Earthformer: Exploring Space-Time Transformers for Earth System Forecasting
Zhihan Gao, Xingjian Shi, Hao Wang +4
Conventionally, Earth system (e.g., weather and climate) forecasting relies on numerical simulation with complex physical models and are hence both expensive in computation and dem…
Robust Probabilistic Time Series Forecasting
TaeHo Yoon, Youngsuk Park, Ernest K. Ryu +1
Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties. Deep forecasting models, however, could…
Context Uncertainty in Contextual Bandits with Applications to Recommender Systems
Hao Wang, Yifei Ma, Hao Ding +1
Recurrent neural networks have proven effective in modeling sequential user feedbacks for recommender systems. However, they usually focus solely on item relevance and fail to effe…
Graph-Relational Domain Adaptation
Zihao Xu, Hao He, Guang-He Lee +2
Existing domain adaptation methods tend to treat every domain equally and align them all perfectly. Such uniform alignment ignores topological structures among different domains; t…
GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics
Ke Alexander Wang, Danielle Maddix, Yuyang Wang
We consider the problem of probabilistic forecasting over categories with graph structure, where the dynamics at a vertex depends on its local connectivity structure. We present GO…
Dynamic Regret for Strongly Adaptive Methods and Optimality of Online KRR
Dheeraj Baby, Hilaf Hasson, Yuyang Wang
We consider the framework of non-stationary Online Convex Optimization where a learner seeks to control its dynamic regret against an arbitrary sequence of comparators. When the lo…
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting
Youngsuk Park, Danielle Maddix, François-Xavier Aubet +3
Quantile regression is an effective technique to quantify uncertainty, fit challenging underlying distributions, and often provide full probabilistic predictions through joint lear…
Deep Explicit Duration Switching Models for Time Series
Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle +5
Many complex time series can be effectively subdivided into distinct regimes that exhibit persistent dynamics. Discovering the switching behavior and the statistical patterns in th…