Hongning Wang

Kukunuri, R., Batra, N., & Wang, H. (2020). An Open Problem: Energy Data Super-Resolution. In NILM’20: Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring (p. 4). http://doi.org/10.1145/3427771.3429995
Kukunuri, R., Batra, N., & Wang, H. (2020). Lessons and Insights from Super-Resolution of Energy Data. In ACM India Joint International Conference on Data Science & Management of Data (p. 2). http://doi.org/10.1145/3371158.3371224
Jia, Y., Batra, N., Wang, H., & Whitehouse, K. (2019). Active Collaborative Sensing for Energy Breakdown. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (p. 10). http://doi.org/10.1145/3357384.3357929
Tao, Y., Jia, Y., Wang, N., & Wang, H. (2019). The FacT: Taming Latent Factor Models for Explainability with Factorization Trees. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (p. 10). http://doi.org/10.1145/3331184.3331244
Jia, Y., Batra, N., Wang, H., & Whitehouse, K. (2019). A Tree-Structured Neural Network Model for Household Energy Breakdown. In The World Wide Web Conference (p. 7). http://doi.org/10.1145/3308558.3313405
Wang, N., Wang, H., Jia, Y., & Yin, Y. (2018). Explainable Recommendation via Multi-Task Learning in Opinionated Text Data. In SIGIR ’18 The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (p. 10). http://doi.org/10.1145/3209978.3210010
Batra, N., Wang, H., Singh, A., & Whitehouse, K. (2017). Matrix Factorisation for Scalable Energy Breakdown. In Proceedings of the . AAAI Conference on Artificial Intelligence. Retrieved from https://par.nsf.gov/biblio/10039453