14 citations · 38 across the 5 of their papers we have counts for
5 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…
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…
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…