{"type":"ScholarlyArticle","title":"Probabilistic Machine Learning: Models, Algorithms and a Programming Library","authors":[{"name":"Jun Zhu","slug":"jun-zhu","given":"Jun","family":"Zhu","affiliation":"Department of Computer Science and Technology , Tsinghua Lab of Brain and Intelligence State Key Lab for Intell. Tech & Sys. , BNRist Lab , Tsinghua University , 100084 , China"}],"abstract":"Probabilistic machine learning provides a suite of powerful tools for modeling uncertainty, performing probabilistic inference, and making predictions or decisions in uncertain environments. In this paper, we present an overview of our recent work on probabilistic machine learning, including the theory of regularized Bayesian inference, Bayesian deep learning, scalable inference algorithms, a probabilistic programming library named ZhuSuan, and applications in representation learning as well as learning from crowds.","doi":"10.24963/ijcai.2018/823","url":"https://ijcai.org/proceedings/2018/823","pdf":"https://ijcai.org/proceedings/2018/0823.pdf","markdown":"https://ijcai.org/proceedings/2018/823.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/823","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Early Career","track":"Early Career","number":823,"pages":{"first":5754,"last":5759},"keywords":["Machine Learning: Probabilistic Machine Learning","Machine Learning: Learning Generative Models","Machine Learning: Machine Learning","Machine Learning: Deep Learning"],"videos":[],"references":[{"position":1,"raw":"[Abadi et al., 2016] Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al. Tensorflow: Large-scale machine learn- ing on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467, 2016.","title":"Tensorflow: Large-scale machine learning on heterogeneous distributed systems","authors":"Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bhadury et al., 2016] Arnab Bhadury, Jianfei Chen, Jun Zhu, and Shixia Liu. Scaling up dynamic topic models. In WWW, 2016.","title":"Scaling up dynamic topic models","authors":"Arnab Bhadury, Jianfei Chen, Jun Zhu, Shixia Liu","year":"2016","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Blei et al., 2003] David Blei, Andrew Ng, and Michael I. Jordan. Latent Dirichlet Allocation. JMLR, (3):993-1022, 2003.","title":"Latent Dirichlet Allocation","authors":"David Blei, Andrew Ng, Michael I Jordan","year":"2003","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bottou and Bousquet, 2008] Leon Bottou and Olivier Bous- quet. The tradeoffs of large scale learning. In NIPS, 2008.","title":"The tradeoffs of large scale learning","authors":"Bousquet Bottou, Leon Bottou, Olivier Bousquet","year":"2008","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Carlson et al., 2010] Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam R. Hruschka, and Tom Mitchell. Toward an architecture for never-ending lan- guage learning. In AAAI, 2010.","title":"Toward an architecture for never-ending language learning","authors":"Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam R Hruschka, Tom Mitchell","year":"2010","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chen et al., 2012] Ning Chen, Jun Zhu, Fuchun Sun, and Eric P. Xing. Large-margin predictive latent subspace learning for multi-view data analysis. IEEE Trans. on PAMI, 34(12):2365-2378, 2012.","title":"Large-margin predictive latent subspace learning for multi-view data analysis","authors":"Ning Chen, Jun Zhu, Fuchun Sun, Eric P Xing","year":"2012","venue":"IEEE Trans. on PAMI","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen et al., 2013] Jianfei Chen, Jun Zhu, Zi Wang, Xun Zheng, and Bo Zhang. Scalable inference for logistic- normal topic models. In NIPS, 2013.","title":"Scalable inference for logisticnormal topic models","authors":"Jianfei Chen, Jun Zhu, Zi Wang, Xun Zheng, Bo Zhang","year":"2013","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chen et al., 2014] Chaoyou Chen, Jun Zhu, and Xinhua Zhang. Robust bayesian max-margin clustering. In NIPS, 2014.","title":"Robust bayesian max-margin clustering","authors":"Chen","year":"2014","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Chen et al., 2015] Ning Chen, Jun Zhu, Fei Xia, and Bo Zhang. Discriminative relational topic models. IEEE Trans. on PAMI, 37(5):973-986, 2015.","title":"Discriminative relational topic models","authors":"Ning Chen, Jun Zhu, Fei Xia, Bo Zhang","year":"2015","venue":"IEEE Trans. on PAMI","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Chen et al., 2016] Jianfei Chen, Kaiwei Li, Jun Zhu, and Wenguang Chen. WarpLDA: a Cache Efficient O(1) Al- gorithm for Latent Dirichlet Allocation. In VLDB, 2016.","title":"WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation","authors":"Jianfei Chen, Kaiwei Li, Jun Zhu, Wenguang Chen","year":"2016","venue":"VLDB","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Dawid and Skene, 1979] Alexander P. Dawid and A. M. Skene. Maximum likelihood estimation of observer error- rates using the EM algorithm. J. Roy. Statist. Soc. C (Ap- plied Statistics), 2:20-28, 1979.","title":"Maximum likelihood estimation of observer errorrates using the EM algorithm","authors":"Alexander P Dawid, A M Skene","year":"1979","venue":"J. Roy. Statist. Soc. C (Applied Statistics)","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Deng et al., 2017] Zhijie Deng, Hao Zhang, Xiaodan Liang, Luona Yang, Shizhen Xu, Jun Zhu, and Eric Xing. Struc- tured generative adversarial networks. In NIPS, 2017.","title":"Structured generative adversarial networks","authors":"Zhijie Deng, Hao Zhang, Xiaodan Liang, Luona Yang, Shizhen Xu, Jun Zhu, Eric Xing","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Dong et al., 2018] Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li. Boosting adversarial attacks with momentum. In CVPR, 2018.","title":"Boosting adversarial attacks with momentum","authors":"Dong","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Goodfellow et al., 2014] Ian Goodfellow, Jean Pouget- Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Gen- erative adversarial nets. In Advances in Neural Informa- tion Processing Systems, pages 2672-2680, 2014.","title":"Generative adversarial nets","authors":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio","year":"2014","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Karger et al., 2011] David R. Karger, Sewoong Oh, and De- vavrat Shah. Iterative learning for reliable crowdsourcing systems. In NIPS, 2011.","title":"Iterative learning for reliable crowdsourcing systems","authors":"David R Karger, Sewoong Oh, Devavrat Shah","year":"2011","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Kingma and Welling, 2013] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.","title":"Auto-encoding variational bayes","authors":"P Diederik, Max Kingma, Welling","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[LeCun et al., 2015] Yann LeCun, Yoshua Bengio, and Ge- offrey Hinton. Deep learning. Nature, (521):436-444, 2015.","title":"Deep learning","authors":"Yann Lecun, Yoshua Bengio, Geoffrey Hinton","year":"2015","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Li et al., 2015] Chongxuan Li, Jun Zhu, Tianlin Shi, and Bo Zhang. Max-margin deep generative models. In NIPS, 2015.","title":"Max-margin deep generative models","authors":"Li","year":"2015","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Li et al., 2017a] Chongxuan Li, Kun Xu, Jun Zhu, and Bo Zhang. Triple generative adversarial nets. In NIPS, 2017.","title":"Triple generative adversarial nets","authors":"Li","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Li et al., 2017b] Chongxuan Li, Jun Zhu, and Bo Zhang. Max-margin deep generative models for (semi-)supervised learning. IEEE TPAMI (in press), 2017.","title":"Max-margin deep generative models for (semi-)supervised learning","authors":"Li","year":"2017","venue":"IEEE TPAMI","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Li et al., 2017c] Kaiwei Li, Jianfei Chen, Wenguang Chen, and Jun Zhu. SaberLDA: Sparsity-aware learning of topic models on gpus. In ASPLOS, 2017.","title":"SaberLDA: Sparsity-aware learning of topic models on gpus","authors":"Li","year":"2017","venue":"ASPLOS","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Liu and Zhu, 2018] Chang Liu and Jun Zhu. Riemannian stein variational gradient descent for bayesian inference. In AAAI, 2018.","title":"Riemannian stein variational gradient descent for bayesian inference","authors":"Chang Liu, Jun Zhu","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Liu et al., 2016] Chang Liu, Jun Zhu, and Yang Song. Stochastic gradient geodesic mcmc methods. In NIPS, 2016.","title":"Stochastic gradient geodesic mcmc methods","authors":"Chang Liu, Jun Zhu, Yang Song","year":"2016","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Mei et al., 2014] Shike Mei, Jun Zhu, and Xiaojin Zhu. Ro- bust RegBayes: Selectively incorporating first-order logic domain knowledge into Bayesian models. In ICML, 2014.","title":"Robust RegBayes: Selectively incorporating first-order logic domain knowledge into Bayesian models","authors":"Shike Mei, Jun Zhu, Xiaojin Zhu","year":"2014","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Pang et al., 2018] Tianyu Pang, Chao Du, and Jun Zhu. Max-mahalanobis linear discriminant analysis networks. In ICML, 2018.","title":"Max-mahalanobis linear discriminant analysis networks","authors":"Tianyu Pang, Chao Du, Jun Zhu","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Raykar et al., 2010] Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Gerardo H. Valadez, Charles Florin, Luca Bogoni, and Linda Moy. Learning from crowds. JMLR, 11:1297- 1322, 2010.","title":"Learning from crowds","authors":"C Vikas, Shipeng Raykar, Linda H Yu, Gerardo H Zhao, Charles Valadez, Luca Florin, Linda Bogoni, Moy","year":"2010","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Ren et al., 2017] Yong Ren, Yining Wang, and Jun Zhu. Spectral learning for supervised topic models. IEEE TPAMI (in press), 2017.","title":"Spectral learning for supervised topic models","authors":"Yong Ren, Yining Wang, Jun Zhu","year":"2017","venue":"IEEE TPAMI","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Rezende et al., 2016] Danilo Jimenez Rezende, Shakir Mo- hamed, Ivo Danihelka, Karol Gregor, and Daan Wierstra. One-shot generalization in deep generative models. arXiv preprint arXiv:1603.05106, 2016.","title":"One-shot generalization in deep generative models","authors":"Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, Daan Wierstra","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Salimans et al., 2016] Tim Salimans, Ian Goodfellow, Woj- ciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. In Advances in Neural Information Processing Systems, pages 2226- 2234, 2016.","title":"Improved techniques for training gans","authors":"Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen","year":"2016","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Shi and Zhu, 2017] Tianlin Shi and Jun Zhu. Online Bayesian passive-aggressive learning. JMLR, 18(33):1- 39, 2017.","title":"Online Bayesian passive-aggressive learning","authors":"Zhu ; ] Tianlin Shi, Jun Shi, Zhu","year":"2017","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Shi et al., 2017] Jiaxin Shi, Jianfei Chen, Jun Zhu, Shengyang Sun, Yucen Luo, Yihong Gu, and Yuhao Zhou. ZhuSuan: A library for bayesian deep learning. preprint arXiv:1709.05870, 2017.","title":"ZhuSuan: A library for bayesian deep learning","authors":"Jiaxin Shi, Jianfei Chen, Jun Zhu, Shengyang Sun, Yucen Luo, Yihong Gu, Yuhao Zhou","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Shi et al., 2018a] Jiaxin Shi, Shengyang Sun, and Jun Zhu. Kernel implicit variational inference. In ICLR, 2018.","title":"Kernel implicit variational inference","authors":"Shi","year":"2018","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Shi et al., 2018b] Jiaxin Shi, Shengyang Sun, and Jun Zhu. A spectral approach to gradient estimation for implicit dis- tributions. In ICML, 2018.","title":"A spectral approach to gradient estimation for implicit distributions","authors":"Shi","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Song et al., 2016] Yang Song, Jun Zhu, and Yong Ren. Kernel Bayesian inference with posterior regularization. NIPS, 2016.","title":"Kernel Bayesian inference with posterior regularization","authors":"Yang Song, Jun Zhu, Yong Ren","year":"2016","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Suchanek et al., 2007] Fabian M. Suchanek, Gjergji Kas- neci, and Gerhard Weikum. YAGO -a core of semantic knowledge. In WWW, 2007.","title":"YAGO -a core of semantic knowledge","authors":"Fabian M Suchanek, Gjergji Kasneci, Gerhard Weikum","year":"2007","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Tian and Zhu, 2012] Yuandong Tian and Jun Zhu. Learn- ing from crowds in the presence of schools of thought. In SIGKDD, 2012.","title":"Learning from crowds in the presence of schools of thought","authors":"Zhu ; ] Yuandong Tian, Jun Tian, Zhu","year":"2012","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Tian and Zhu, 2015] Tian Tian and Jun Zhu. Max-margin majority voting for learning from crowds. In NIPS, 2015.","title":"Max-margin majority voting for learning from crowds","authors":"Zhu ; ] Tian Tian, Jun Tian, Zhu","year":"2015","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Tian et al., 2015] Tian Tian, Jun Zhu, Fen Xia, Xin Zhuang, and Tong Zhang. Crowd fraud detection in internet adver- tising. In WWW, 2015.","title":"Crowd fraud detection in internet advertising","authors":"Tian Tian, Jun Zhu, Fen Xia, Xin Zhuang, Tong Zhang","year":"2015","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Tian et al., 2017] Tian Tian, Ning Chen, and Jun Zhu. Learning attributes from the crowdsourced relative labels. In AAAI, 2017.","title":"Learning attributes from the crowdsourced relative labels","authors":"Tian Tian, Ning Chen, Jun Zhu","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Wang and Zhu, 2014a] Yining Wang and Jun Zhu. Small- variance asymptotics for dirichlet process mixtures of svms. In AAAI, 2014.","title":"Smallvariance asymptotics for dirichlet process mixtures of svms","authors":"Wang, Zhu, Yining Wang, Jun Zhu","year":"2014","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Wang and Zhu, 2014b] Yining Wang and Jun Zhu. Spectral methods for supervised topic models. In NIPS, 2014.","title":"Spectral methods for supervised topic models","authors":"Wang, Zhu ;, Yining Wang, Jun Zhu","year":"2014","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Wang and Zhu, 2015] Yining Wang and Jun Zhu. Dp-space: Bayesian nonparametric subspace clustering with small- variance asymptotics. In ICML, 2015.","title":"Dp-space: Bayesian nonparametric subspace clustering with smallvariance asymptotics","authors":"Wang, Zhu ; ] Yining Wang, Jun Zhu","year":"2015","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Xu et al., 2013] Minjie Xu, Jun Zhu, and Bo Zhang. Fast max-margin matrix factorization with data augmenta- tion. In International Conference on Machine Learning (ICML), pages 978-986, 2013.","title":"Fast max-margin matrix factorization with data augmentation","authors":"Minjie Xu, Jun Zhu, Bo Zhang","year":"2013","venue":"International Conference on Machine Learning (ICML)","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Xu et al., 2014] Minjie Xu, Balaji Lakshminarayanan, Yee Whye Teh, Jun Zhu, and Bo Zhang. Distributed Bayesian posterior sampling via moment sharing. In NIPS, 2014.","title":"Distributed Bayesian posterior sampling via moment sharing","authors":"Minjie Xu, Balaji Lakshminarayanan, Yee Whye Teh, Jun Zhu, Bo Zhang","year":"2014","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Zhang et al., 2013] Aonan Zhang, Jun Zhu, and Bo Zhang. Sparse online topic models. In WWW, 2013.","title":"Sparse online topic models","authors":"Zhang","year":"2013","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Zhou et al., 2012] Dengyong Zhou, Sumit Basu, Yi. Mao, and John C. Platt. Learning from the wisdom of crowds by minimax entropy. In NIPS, 2012.","title":"Learning from the wisdom of crowds by minimax entropy","authors":"Dengyong Zhou, Sumit Basu, Yi Mao, John C Platt","year":"2012","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Zhu and Xing, 2011] Jun Zhu and Eric Xing. Sparse topic coding. In UAI, 2011.","title":"Sparse topic coding","authors":"Jun Zhu, Eric Xing","year":"2011","venue":"UAI","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Zhu et al., 2011a] Jun Zhu, Ning Chen, and Eric Xing. In- finite SVM: a Dirichlet process mixture of large-margin kernel machines. In ICML, pages 617-624, 2011.","title":"Infinite SVM: a Dirichlet process mixture of large-margin kernel machines","authors":"Zhu","year":"2011","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Zhu et al., 2011b] Jun Zhu, Ning Chen, and Eric P. Xing. Infinite latent SVM for classification and multi-task learn- ing. In NIPS, 2011.","title":"Infinite latent SVM for classification and multi-task learning","authors":"Zhu","year":"2011","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Zhu et al., 2012] Jun Zhu, A. Ahmed, and E.P Xing. MedLDA: maximum margin supervised topic models. JMLR, 13:2237-2278, 2012.","title":"MedLDA: maximum margin supervised topic models","authors":"Jun Zhu, A Ahmed, E P Xing","year":"2012","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Zhu et al., 2013] Jun Zhu, Xun Zheng, Li Zhou, and Bo Zhang. Scalable inference in max-margin topic mod- els. In SIGKDD, 2013.","title":"Scalable inference in max-margin topic models","authors":"Jun Zhu, Xun Zheng, Li Zhou, Bo Zhang","year":"2013","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Zhu et al., 2014a] Jun Zhu, Ning Chen, Hugh Perkins, and Bo Zhang. Gibbs max-margin topic models with data aug- mentation. JMLR, 15:949-986, 2014.","title":"Gibbs max-margin topic models with data augmentation","authors":"Zhu","year":"2014","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Zhu et al., 2014b] Jun Zhu, Ning Chen, and Eric P. Xing. Bayesian inference with posterior regularization and ap- plications to infinite latent SVMs. JMLR, 15:1799-1847, 2014.","title":"Bayesian inference with posterior regularization and applications to infinite latent SVMs","authors":"Zhu","year":"2014","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Zhu et al., 2017] Jun Zhu, Jianfei Chen, Wenbo Hu, and Bo Zhang. Big learning with bayesian methods. National Science Review, page nwx044, 2017.","title":"Big learning with bayesian methods","authors":"Jun Zhu, Jianfei Chen, Wenbo Hu, Bo Zhang","year":"2017","venue":"National Science Review","doi":null,"url":null,"internal_url":null},{"position":55,"raw":"[Zhu, 2012] Jun Zhu. Max-margin nonparametric latent fea- ture models for link prediction. In ICML, 2012.","title":"Max-margin nonparametric latent feature models for link prediction","authors":"Jun Zhu","year":"2012","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":56,"raw":"[Zhuo et al., 2018] Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, and Bo Zhang. Message passing stein variational gradient descent. In ICML, 2018.","title":"Message passing stein variational gradient descent","authors":"Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, Bo Zhang","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}