| Nonlinear Hybrid Planning with deep net ltearned transition models and mixed-integer linear programming B Say, G Wu, YQ Zhou, S Sanner International Joint Conference on Artificial Intelligence 2017, 750-756, 2017 | 72 | 2017 |
| PUMA: Performance Unchanged Model Augmentation for Training Data Removal G Wu, M Hashemi, C Srinivasa In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI …, 2022 | 68 | 2022 |
| Deep Language-based Critiquing for Recommender Systems G Wu, K Luo, S Sanner, H Soh In Proceedings of the 13th ACM Conference of Recommender Systems (RecSys-19 …, 2019 | 65 | 2019 |
| Deep Critiquing for VAE-based Recommender Systems K Luo, H Yang, G Wu, S Sanner In Proceedings of the 43nd International ACM SIGIR Conference on Research …, 2020 | 56 | 2020 |
| Two-stage Model for Automatic Playlist Continuation at Scale M Volkovs, H Rai, Z Cheng, G Wu, Y Lu, S Sanner RecSys-2018: ACM Conference on Recommender Systems, 2018 | 56 | 2018 |
| Latent Linear Critiquing for Conversational Recommender Systems K Luo, S Sanner, G Wu, H Li, H Yang In Proceedings of the 29th International Conference on the World Wide Web …, 2020 | 55 | 2020 |
| Noise Contrastive Estimation for One-Class Collaborative Filtering G Wu, M Volkovs, CL Soon, S Sanner, H Rai In Proceedings of the 42nd International ACM SIGIR Conference on Research …, 2019 | 50 | 2019 |
| Scalable planning with Tensorflow for hybrid nonlinear domains G Wu, B Say, S Sanner NIPS, Advances in Neural Information Processing Systems, 6273-6283, 2017 | 40 | 2017 |
| Scalable Planning with Deep Neural Network Learned Transition Models G Wu, B Say, S Sanner Journal of Artificial Intelligence Research (JAIR) 68, 571-606, 2020 | 31 | 2020 |
| A ranking optimization approach to latent linear critiquing for conversational recommender systems H Li, S Sanner, K Luo, G Wu Proceedings of the 14th ACM conference on recommender systems, 13-22, 2020 | 28 | 2020 |
| Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models Y Sui, G Wu, S Sanner Thirty-Fifth Conference on Neural Information Processing Systems (NeurIPS-21), 2021 | 17 | 2021 |
| A user-centric analysis of social media for stock market prediction MR Bouadjenek, S Sanner, G Wu ACM Transactions on the Web 17 (2), 1-22, 2023 | 13 | 2023 |
| One-Class Collaborative Filtering with the Queryable Variational Autoencoder G Wu, MR Bouadjenek, S Sanner In Proceedings of the 42nd International ACM SIGIR Conference on Research …, 2019 | 13 | 2019 |
| Arbitrary conditional inference in variational autoencoders via fast prior network training G Wu, J Domke, S Sanner Machine Learning 111 (7), 2537-2559, 2022 | 12* | 2022 |
| Bayesian Preference Elicitation with Keyphase-Item Coembeddings for Interactive Recommendation H Yang, S Sanner, G Wu, JP Zhou 29th Conference on User Modeling, Adaptation and Personalization (UMAP), 2021 | 9 | 2021 |
| Bayesian model averaging naive bayes (bma-nb): Averaging over an exponential number of feature models in linear time G Wu, S Sanner, R Oliveira Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 8 | 2015 |
| Self-supervised Representation Learning From Random Data Projectors Y Sui, T Wu, J Cresswell, G Wu, G Stein, X Huang, X Zhang, M Volkovs 12th International Conference on Learning Representations (ICLR 2024), 2024 | 6 | 2024 |
| Distributional Contrastive Embedding for Clarification-based Conversational Critiquing T Shen, Z Mai, G Wu, S Sanner In Proceedings of the 31st International Conference on the World Wide Web …, 2022 | 5 | 2022 |
| System and method for adversarial vulnerability testing of machine learning models GMA Castiglione, W Ding, SH AMROABADI, G Wu, CC Srinivasa US Patent App. 17/750,205, 2022 | 3 | 2022 |
| Scalable whitebox attacks on tree-based models G Castiglione, G Ding, M Hashemi, C Srinivasa, G Wu arXiv preprint arXiv:2204.00103, 2022 | 3 | 2022 |
Scott SannerUniversity of TorontoVerified email at mie.utoronto.ca