Maksims Volkovs, Ph.D.
Affiliations: | 2013 | Computer Science | University of Toronto, Toronto, ON, Canada |
Area:
neural coding, visual perceptionGoogle:
"Maksims Volkovs"Mean distance: 14.52 (cluster 17) | S | N | B | C | P |
Parents
Sign in to add mentor| Rich S. Zemel | grad student | 2013 | University of Toronto | |
| (Machine learning methods and models for ranking.) | ||||
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Publications
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| Kalra S, Wen J, Cresswell JC, et al. (2023) Decentralized federated learning through proxy model sharing. Nature Communications. 14: 2899 |
| Volkovs MN, Zemel RS. (2014) New learning methods for supervised and unsupervised preference aggregation Journal of Machine Learning Research. 15: 1135-1176 |
| Volkovs MN, Zemel RS. (2013) CRF framework for supervised preference aggregation International Conference On Information and Knowledge Management, Proceedings. 89-98 |
| Volkovs MN, Larochelle H, Zemel RS. (2012) Learning to rank by aggregating expert preferences Acm International Conference Proceeding Series. 843-851 |
| Volkovs MN, Zemel RS. (2012) A flexible generative model for preference aggregation Www'12 - Proceedings of the 21st Annual Conference On World Wide Web. 479-488 |
| Volkovs MN, Zemel RS. (2012) Efficient sampling for bipartite matching problems Advances in Neural Information Processing Systems. 2: 1313-1321 |
| Volkovs MN, Zemel RS. (2012) Collaborative ranking with 17 parameters Advances in Neural Information Processing Systems. 3: 2294-2302 |
| Svore KM, Volkovs MN, Burges CJC. (2011) Learning to rank with multiple objective functions Proceedings of the 20th International Conference On World Wide Web, Www 2011. 367-376 |
| Volkovs MN, Zemel RS. (2009) BoltzRank: Learning to maximize expected ranking gain Proceedings of the 26th International Conference On Machine Learning, Icml 2009. 1089-1096 |
| Volkovs MN, Zemel RS. (2009) BoltzRank: Learning to maximize expected ranking gain Proceedings of the 26th International Conference On Machine Learning, Icml 2009. 1089-1096 |