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\n  \n 2024\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Self-supervised Representation Learning from Random Data Projectors.\n \n \n \n\n\n \n Sui, Y.; Wu, T.; Cresswell, J. C.; Wu, G.; Stein, G.; Huang, X. S.; Zhang, X.; and Volkovs, M.\n\n\n \n\n\n\n In Proceedings of the 12th International Conference on Learning Representations (ICLR-2024), Vienna, Austria, 2024. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Wu:iclr24,\n  author = {Yi Sui and Tongzi Wu and Jesse C. Cresswell and Ga Wu and George Stein and Xiao Shi Huang and Xiaochen Zhang and Maksims Volkovs},\n  year = {2024},\n  title = {Self-supervised Representation Learning from Random Data Projectors},\n  booktitle = {Proceedings of the 12th International Conference on Learning Representations (ICLR-2024)},\n  address = {Vienna, Austria},\n}\n\n
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\n  \n 2023\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n A User-Centric Analysis of Social Media for Stock Market Prediction.\n \n \n \n\n\n \n Bouadjenek, M. R.; Sanner, S.; and Wu, G.\n\n\n \n\n\n\n ACM Transactions on the Web (TWEB). 2023.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{Wu:tweb23,\n  author    = {Mohamed Reda Bouadjenek and Scott Sanner and Ga Wu},\n  title     = {A User-Centric Analysis of Social Media for Stock Market Prediction},\n  journal   = {ACM Transactions on the Web (TWEB)},\n  year      = {2023},\n}\n\n
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\n \n\n \n \n \n \n \n Large-scale User Preference Tracking via Asynchronous and Asymmetric Updating at Twitter.\n \n \n \n\n\n \n Wu, G.; Khare, S.; Zhou, L.; Brumer, Y.; Ng, J.; and Wang, R.\n\n\n \n\n\n\n In Proceedings of the 2023 IEEE International Conference on Big Data (IEEE BigData-2023), Sorrento, Italy, 2023. \n (17.4% acceptance rate)\n\n\n\n
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@inproceedings{Wu:bd23,\n  author = {Ga Wu and Shivam Khare and Li Zhou and Yael Brumer and Jun-Ping Ng and Ruowei Wang},\n  year = {2023},\n  title = {Large-scale User Preference Tracking via Asynchronous and Asymmetric Updating at Twitter},\n  booktitle = {Proceedings of the 2023 IEEE International Conference on Big Data (IEEE BigData-2023)},\n  address = {Sorrento, Italy},\n  note = {<font color="#778899"> (17.4\\% acceptance rate)</font>},\n}\n\n
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\n  \n 2022\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n \n PUMA: Performance Unchanged Model Augmentation for Training Data Removal.\n \n \n \n \n\n\n \n Wu, G.; Hashemi, M.; and Srinivasa, C.\n\n\n \n\n\n\n In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI-2022), Vancouver, Canada, 2022. \n (15% acceptance rate)\n\n\n\n
\n\n\n\n \n \n \"PUMA: paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 26 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Wu:aaai22,\n  author = {Ga Wu and Masoud Hashemi and Christopher Srinivasa},\n  year = {2022},\n  title = {PUMA: Performance Unchanged Model Augmentation for Training Data Removal},\n  booktitle = {Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI-2022)},\n  address = {Vancouver, Canada},\n  note = {<font color="#778899"> (15\\% acceptance rate)</font>},\n  url_paper = {https://www.aaai.org/AAAI22Papers/AAAI-10608.WuG.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n Arbitrary conditional inference in variational autoencoders via fast prior network training.\n \n \n \n\n\n \n Wu, G.; Domke, J.; and Sanner, S.\n\n\n \n\n\n\n Machine Learning, Springer. 2022.\n \n\n\n\n
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@article{Wu:ml2022,\n  author    = {Ga Wu and Justin Domke and Scott Sanner},\n  title     = {Arbitrary conditional inference in variational autoencoders via fast prior network training},\n  journal   = {Machine Learning, Springer},\n  year      = {2022},\n}\n\n
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\n \n\n \n \n \n \n \n Distributional Contrastive Embedding for Clarification-based Conversational Critiquing.\n \n \n \n\n\n \n Shen, T.; Mai, Z.; Wu, G.; and Sanner, S.\n\n\n \n\n\n\n In Proceedings of the 31th international conference on the topic of the World Wide Web (WWW-22), Online, hosted by Lyon, France, 2022. \n (17.7% acceptance rate)\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 23 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Wu:www22,\n  author    = {Tianshu Shen and Zheda Mai and Ga Wu and Scott Sanner },\n  title     = {Distributional Contrastive Embedding for Clarification-based Conversational Critiquing},\n  booktitle = {Proceedings of the 31th international conference on the topic of the World Wide Web {(WWW-22)}},\n  address   = {Online, hosted by Lyon, France},\n  year      = {2022},\n  note = {<font color="#778899">(17.7\\% acceptance rate)</font>}\n}\n\n
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\n  \n 2021\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models.\n \n \n \n \n\n\n \n Sui, Y.; Wu, G.; and Sanner, S.\n\n\n \n\n\n\n In Proceedings of the 35th Annual Conference on Advances in Neural Information Processing Systems (NeurIPS-21), Online, 2021. \n (26% acceptance rate)\n\n\n\n
\n\n\n\n \n \n \"Representer paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 23 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Wu:neurips21,\n  author = {Yi Sui and Ga Wu and Scott Sanner},\n  year = {2021},\n  title = {Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models},\n  booktitle = {Proceedings of the 35th Annual Conference on Advances in Neural Information Processing Systems ({NeurIPS-21})},\n  address = {Online},\n  note = {<font color="#778899"> (26\\% acceptance rate)</font>},\n  url_paper = {https://openreview.net/pdf?id=Wl32WBZnSP4},\n}  \n\n
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\n \n\n \n \n \n \n \n \n Bayesian Preference Elicitation with Keyphase-Item Coembeddings for Interactive Recommendation.\n \n \n \n \n\n\n \n Yang, H.; Sanner, S.; Wu, G.; and Zhou, J. P.\n\n\n \n\n\n\n In Proceedings of the 29th International Conference on User Modeling, Adaptation, and Personalization (UMAP-21), Online, 2021. \n (23.3% acceptance rate)\n\n\n\n
\n\n\n\n \n \n \"Bayesian paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 62 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Wu:umap21,\n  author    = {Hojin Yang and Scott Sanner and Ga Wu and Jin Peng Zhou},\n  title     = {Bayesian Preference Elicitation with Keyphase-Item Coembeddings for Interactive Recommendation},\n  booktitle = {Proceedings of the 29th International Conference on User Modeling, Adaptation, and Personalization {(UMAP-21)}},\n  address   = {Online},\n  year      = {2021},\n  note = {<font color="#778899"> (23.3\\% acceptance rate)</font>},\n  url_paper = {https://dl.acm.org/doi/10.1145/3450613.3456814},\n}\n\n
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\n  \n 2020\n \n \n (6)\n \n \n
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\n \n\n \n \n \n \n \n \n A Ranking Optimization Approach to Latent Linear Critiquing in Conversational Recommender System.\n \n \n \n \n\n\n \n Li, H.; Sanner, S.; Luo, K.; and Wu, G.\n\n\n \n\n\n\n In Proceedings of the 14th International ACM Conference on Recommender Systems (RecSys-20), Online, 2020. \n (18% acceptance rate)\n\n\n\n
\n\n\n\n \n \n \"A paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 95 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Wu:recsys20,\n  author    = {Hanze Li and Scott Sanner and Kai Luo and Ga Wu},\n  title     = {A Ranking Optimization Approach to Latent Linear Critiquing in Conversational Recommender System},\n  booktitle = {Proceedings of the 14th International {ACM} Conference on Recommender Systems {(RecSys-20)}},\n  address   = {Online},\n  year      = {2020},\n  note = {<font color="#778899"> (18\\% acceptance rate)</font>},\n  url_paper = {https://dl.acm.org/doi/10.1145/3383313.3412240},\n}\n\n
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\n \n\n \n \n \n \n \n \n Scalable Planning with Deep Neural Network Learned Transition Models.\n \n \n \n \n\n\n \n Wu, G.; Say, B.; and Sanner, S.\n\n\n \n\n\n\n Journal of Artificial Intelligence Research. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"Scalable paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 53 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{Wu:jair20,\n  author    = {Ga Wu and Buser Say and Scott Sanner},\n  title     = {Scalable Planning with Deep Neural Network Learned Transition Models},\n  journal   = {Journal of Artificial Intelligence Research},\n  year      = {2020},\n  url_paper = {https://www.jair.org/index.php/jair/article/view/11829}\n}\n\n
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\n \n\n \n \n \n \n \n \n Deep Critiquing for VAE-based Recommender Systems.\n \n \n \n \n\n\n \n Luo, K.; Yang, H.; Wu, G.; and Sanner, S.\n\n\n \n\n\n\n In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-20), Xi'an, China, 2020. \n Oral presentation (26% acceptance rate)\n\n\n\n
\n\n\n\n \n \n \"Deep paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 137 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Wu:sigir20,\n  author    = {Kai Luo and Hojin Yang and Ga Wu and Scott Sanner},\n  title     = {Deep Critiquing for VAE-based Recommender Systems},\n  booktitle = {Proceedings of the 43rd International {ACM} {SIGIR} Conference on Research and Development in Information Retrieval {(SIGIR-20)}},\n  address   = {Xi'an, China},\n  year      = {2020},\n  url_paper = {https://dl.acm.org/doi/abs/10.1145/3397271.3401091},\n  note = {<font color="#778899">Oral presentation (26\\% acceptance rate)</font>}\n}\n\n
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\n \n\n \n \n \n \n \n \n Latent Linear Critiquing for Conversational Recommender Systems.\n \n \n \n \n\n\n \n Luo, K.; Sanner, S.; Wu, G.; Li, H.; and Yang, H.\n\n\n \n\n\n\n In Proceedings of the 29th international conference on the topic of the World Wide Web (WWW-20), Taipei, 2020. \n Oral presentation (25% acceptance rate)\n\n\n\n
\n\n\n\n \n \n \"Latent paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 76 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Wu:www20,\n  author    = {Kai Luo and Scott Sanner and Ga Wu and Hanze Li and Hojin Yang},\n  title     = {Latent Linear Critiquing for Conversational Recommender Systems},\n  booktitle = {Proceedings of the 29th international conference on the topic of the World Wide Web {(WWW-20)}},\n  address   = {Taipei},\n  year      = {2020},\n  url_paper = {https://dl.acm.org/doi/10.1145/3366423.3380003},\n  note = {<font color="#778899">Oral presentation (25\\% acceptance rate)</font>}\n}\n\n
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\n \n\n \n \n \n \n \n Attentive Autoencoders for Multifaceted Preference Learning in One-class Collaborative Filtering.\n \n \n \n\n\n \n Mai, Z.; Wu, G.; Luo, K.; and Sanner, S.\n\n\n \n\n\n\n IEEE International Conference on Data Mining Workshop (ICDMW). 2020.\n \n\n\n\n
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@article{mai2020attentive,\n  title={Attentive Autoencoders for Multifaceted Preference Learning in One-class Collaborative Filtering},\n  author={Zheda Mai and Ga Wu and Kai Luo and Scott Sanner},\n  journal={IEEE International Conference on Data Mining Workshop (ICDMW)},\n  year={2020}\n}\n\n
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\n \n\n \n \n \n \n \n One-class Collaborative Filtering with Latent Embeddings: Improvements and Interactive Extensions.\n \n \n \n\n\n \n Wu, G.\n\n\n \n\n\n\n Ph.D. Thesis, University of Toronto. 2020.\n \n\n\n\n
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@article{Wu:thesis20,\n  title={One-class Collaborative Filtering with Latent Embeddings: Improvements and Interactive Extensions},\n  author={Ga Wu},\n  journal={Ph.D. Thesis, University of Toronto},\n  address={ON, Canada},\n  year={2020}\n}\n\n
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\n  \n 2019\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n \n Deep Language-based Critiquing for Recommender Systems.\n \n \n \n \n\n\n \n Wu, G.; Luo, K.; Sanner, S.; and Soh, H.\n\n\n \n\n\n\n In Proceedings of the 13 International ACM Conference on Recommender Systems (RecSys-19), Copenhagen, Denmark, 2019. \n Oral presentation (19% acceptance rate)\n\n\n\n
\n\n\n\n \n \n \"Deep paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 70 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Wu:recsys19,\n  author    = {Ga Wu and Kai Luo and Scott Sanner and Harold Soh},\n  title     = {Deep Language-based Critiquing for Recommender Systems},\n  booktitle = {Proceedings of the 13 International {ACM} Conference on Recommender Systems {(RecSys-19)}},\n  address   = {Copenhagen, Denmark},\n  year      = {2019},\n  url_paper = {https://dl.acm.org/doi/10.1145/3298689.3347009},\n  note = {<font color="#778899">Oral presentation (19\\% acceptance rate)</font>}\n}\n\n
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\n \n\n \n \n \n \n \n \n Noise Contrastive Estimation for One-Class Collaborative Filtering.\n \n \n \n \n\n\n \n Wu, G.; Volkovs, M.; Soon, C. L.; Sanner, S.; and Rai, H.\n\n\n \n\n\n\n In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-19), Paris, France, 2019. \n Oral presentation (19.7% acceptance rate)\n\n\n\n
\n\n\n\n \n \n \"Noise paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 41 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Wu:sigir19a,\n  author    = {Ga Wu and Maksims Volkovs and Chee Loong Soon and Scott Sanner and Himanshu Rai},\n  title     = {Noise Contrastive Estimation for One-Class Collaborative Filtering},\n  booktitle = {Proceedings of the 42nd International {ACM} {SIGIR} Conference on Research and Development in Information Retrieval {(SIGIR-19)}},\n  address   = {Paris, France},\n  year      = {2019},\n  url_paper = {https://dl.acm.org/doi/10.1145/3331184.3331201},\n  note = {<font color="#778899">Oral presentation (19.7\\% acceptance rate)</font>}\n}\n\n
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\n \n\n \n \n \n \n \n \n One-Class Collaborative Filtering with the Queryable Variational Autoencoder.\n \n \n \n \n\n\n \n Wu, G.; Bouadjenek, M. R.; and Sanner, S.\n\n\n \n\n\n\n In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-19), Paris, France, 2019. \n (24.4% acceptance rate)\n\n\n\n
\n\n\n\n \n \n \"One-Class paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 23 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Wu:sigir19b,\n  author    = {Ga Wu and Mohamed Reda Bouadjenek and Scott Sanner},\n  title     = {One-Class Collaborative Filtering with the Queryable Variational Autoencoder},\n  booktitle = {Proceedings of the 42nd International {ACM} {SIGIR} Conference on Research and Development in Information Retrieval {(SIGIR-19)}},\n  address   = {Paris, France},\n  year      = {2019},\n  url_paper = {https://dl.acm.org/doi/10.1145/3331184.3331292},\n  note = {<font color="#778899"> (24.4\\% acceptance rate)</font>}\n}\n\n\n
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\n \n\n \n \n \n \n \n \n A Novel Regularizer for Temporally Stable Learning with an Application to Twitter Topic Classification.\n \n \n \n \n\n\n \n Wang, Y.; Wu, G.; Bouadjenek, M. R.; Sanner, S.; Su, S.; and Zhang, Z.\n\n\n \n\n\n\n In Proceedings of the SIAM International Conference on Data Mining (SDM-19), Calgary, Canada, 2019. \n (22.7% acceptance rate)\n\n\n\n
\n\n\n\n \n \n \"A paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 15 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Wu:sdm19,\n  author   = {Yakun Wang and Ga Wu and Mohamed Reda Bouadjenek and Scott Sanner and Sen Su and Zhongbao Zhang},\n  title    = {A Novel Regularizer for Temporally Stable Learning with an Application to Twitter Topic Classification},\n  booktitle = {Proceedings of the SIAM International Conference on Data Mining ({SDM-19})},\n  address   = {Calgary, Canada},\n  year      = {2019},\n  url_paper = {https://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.25},\n  note = {<font color="#778899"> (22.7\\% acceptance rate)</font>}\n}  \n\n
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\n  \n 2018\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Two-stage Model for Automatic Playlist Continuation at Scale.\n \n \n \n \n\n\n \n Volkovs, M.; Rai, H.; Cheng, Z.; Wu, G.; Lu, Y.; and Sanner, S.\n\n\n \n\n\n\n In Proceedings of the ACM Recommender Systems Challenge 2018 (RecSys-18), Vancouver, BC, Canada, 2018. \n Challenge Winner (1st Place) for Both Tracks! \n\n\n\n
\n\n\n\n \n \n \"Two-stage paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Wu:recsys18b,\n  author = {Maksims Volkovs and Himanshu Rai and Zhaoyue Cheng and Ga Wu and Yichao Lu and Scott Sanner},\n  title = {Two-stage Model for Automatic Playlist Continuation at Scale},\n  booktitle = {Proceedings of the ACM Recommender Systems Challenge 2018 ({RecSys-18})},\n  year = {2018},\n  address = {Vancouver, BC, Canada},\n  note = {1st place in 2018 ACM RecSys Challenge.},\n  url_paper = {https://ssanner.github.io/papers/recsys18_challenge.pdf},\n  note = {<font color="#778899"> Challenge Winner (1st Place) for Both Tracks! </font>}\n}\n\n
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\n \n\n \n \n \n \n \n \n Aesthetic Features for Personalized Photo Recommendation.\n \n \n \n \n\n\n \n Zhou, Y. Q.; Wu, G.; Sanner, S.; and Manggala, P.\n\n\n \n\n\n\n In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys-18), Vancouver, BC, Canada, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"Aesthetic paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 7 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Wu:recsys18a,\n  author = {Yu Qing Zhou and Ga Wu and Scott Sanner and Putra Manggala},\n  title = {Aesthetic Features for Personalized Photo Recommendation},\n  year = {2018},\n  booktitle = {Proceedings of the 12th ACM Conference on Recommender Systems ({RecSys-18})},\n  address = {Vancouver, BC, Canada},\n  url_paper = {https://ssanner.github.io/papers/recsys18_aesthetic.pdf}\n}\n\n
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\n  \n 2017\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Scalable Planning with Tensorflow for Hybrid Nonlinear Domains.\n \n \n \n \n\n\n \n Wu, G.; Say, B.; and Sanner, S.\n\n\n \n\n\n\n In Proceedings of the 31st Annual Conference on Advances in Neural Information Processing Systems (NIPS-17), Long Beach, CA, 2017. \n (20.9% acceptance rate)\n\n\n\n
\n\n\n\n \n \n \"Scalable paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 31 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Wu:nips17,\n  author = {Ga Wu and Buser Say and Scott Sanner},\n  year = {2017},\n  title = {Scalable Planning with Tensorflow for Hybrid Nonlinear Domains},\n  booktitle = {Proceedings of the 31st Annual Conference on Advances in Neural Information Processing Systems ({NIPS-17})},\n  address = {Long Beach, CA},\n  url_paper = {https://ssanner.github.io/papers/nips17_tfplan.pdf},\n  note = {<font color="#778899"> (20.9\\% acceptance rate)</font>}\n}  \n\n
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\n \n\n \n \n \n \n \n \n Nonlinear Hybrid Planning with Deep Net Learned Transition Models and Mixed-Integer Linear Programming.\n \n \n \n \n\n\n \n Say, B.; Wu, G.; Zhou, Y. I.; and Sanner, S.\n\n\n \n\n\n\n In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-17), Melbourne, Australia, 2017. \n (24.6% acceptance rate)\n\n\n\n
\n\n\n\n \n \n \"Nonlinear paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 16 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Wu:ijcai17,\n  author = {Buser Say and Ga Wu and Yuqing Ivan Zhou and Scott Sanner},\n  year = {2017},\n  title = {Nonlinear Hybrid Planning with Deep Net Learned Transition Models and Mixed-Integer Linear Programming},\n  booktitle = {Proceedings of the 26th International Joint Conference on Artificial Intelligence ({IJCAI-17})},\n  address = {Melbourne, Australia},\n  url_paper = {https://ssanner.github.io/papers/ijcai17_hdmilp.pdf},\n  note = {<font color="#778899"> (24.6\\% acceptance rate)</font>}\n}  \n\n\n
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\n  \n 2015\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Bayesian Model Averaging Naive Bayes: Averaging over an Exponential Number of Feature Models in Linear Time.\n \n \n \n \n\n\n \n Wu, G.; Sanner, S.; and Oliveira, R. F.\n\n\n \n\n\n\n In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI-15), Austin, USA, 2015. \n Oral presentation (26.7% acceptance rate)\n\n\n\n
\n\n\n\n \n \n \"Bayesian paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 16 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Wu:aaai15d,\n  author = {Ga Wu and Scott Sanner and Rodrigo F.S.C. Oliveira},\n  title = {Bayesian Model Averaging Naive {Bayes}: Averaging over an Exponential Number of Feature Models in Linear Time},\n  year = {2015},\n  booktitle = {Proceedings of the 29th {AAAI} Conference on Artificial Intelligence ({AAAI-15})},\n  address = {Austin, USA},\n  url_paper = {http://users.rsise.anu.edu.au/~ssanner/Papers/aaai15_bmanb.pdf},\n  note = {<font color="#778899">Oral presentation (26.7\\% acceptance rate)</font>}\n}\n
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