7.1k citations · 8k across the 17 of their papers we have counts for
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Self-Interpretable Time Series Prediction with Counterfactual Explanations
Jingquan Yan, Hao Wang
Interpretable time series prediction is crucial for safety-critical areas such as healthcare and autonomous driving. Most existing methods focus on interpreting predictions by assi…
Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing
Ziyan Wang, Hao Wang
Existing regression models tend to fall short in both accuracy and uncertainty estimation when the label distribution is imbalanced. In this paper, we propose a probabilistic deep…
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…
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks
Hao Wang, Xingjian Shi, Dit-Yan Yeung
Hybrid methods that utilize both content and rating information are commonly used in many recommender systems. However, most of them use either handcrafted features or the bag-of-w…
Natural-Parameter Networks: A Class of Probabilistic Neural Networks
Hao Wang, Xingjian Shi, Dit-Yan Yeung
Neural networks (NN) have achieved state-of-the-art performance in various applications. Unfortunately in applications where training data is insufficient, they are often prone to…
Collaborative Deep Learning for Recommender Systems
Hao Wang, Naiyan Wang, Dit-Yan Yeung
Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole s…