| Dropoutnet: Addressing cold start in recommender systems M Volkovs, G Yu, T Poutanen Advances in Neural Information Processing Systems 30, 2017 | 165 | 2017 |
| Continuous data cleaning M Volkovs, F Chiang, J Szlichta, RJ Miller 2014 IEEE 30th international conference on data engineering, 244-255, 2014 | 130 | 2014 |
| Boltzrank: learning to maximize expected ranking gain MN Volkovs, RS Zemel Proceedings of the 26th Annual International Conference on Machine Learning …, 2009 | 124 | 2009 |
| A flexible generative model for preference aggregation MN Volkovs, RS Zemel Proceedings of the 21st International Conference on World Wide Web, 2012 | 80 | 2012 |
| Content-based neighbor models for cold start in recommender systems M Volkovs, GW Yu, T Poutanen Proceedings of the Recommender Systems Challenge 2017, 1-6, 2017 | 79 | 2017 |
| Effective latent models for binary feedback in recommender systems M Volkovs, GW Yu Proceedings of the 38th international ACM SIGIR conference on research and …, 2015 | 70 | 2015 |
| Learning to rank with multiple objective functions KM Svore, MN Volkovs, CJC Burges Proceedings of the 20th international conference on World wide web, 367-376, 2011 | 69 | 2011 |
| Collaborative ranking with 17 parameters M Volkovs, R Zemel Advances in Neural Information Processing Systems 25, 2012 | 65 | 2012 |
| New learning methods for supervised and unsupervised preference aggregation MN Volkovs, RS Zemel The Journal of Machine Learning Research 15 (1), 1135-1176, 2014 | 48 | 2014 |
| Improving transformer optimization through better initialization XS Huang, F Perez, J Ba, M Volkovs International Conference on Machine Learning, 4475-4483, 2020 | 41 | 2020 |
| Two-stage model for automatic playlist continuation at scale M Volkovs, H Rai, Z Cheng, G Wu, Y Lu, S Sanner Proceedings of the ACM Recommender Systems Challenge 2018, 1-6, 2018 | 38 | 2018 |
| Learning to rank by aggregating expert preferences MN Volkovs, H Larochelle, RS Zemel Proceedings of the 21st ACM international conference on Information and …, 2012 | 32 | 2012 |
| Noise Contrastive Estimation for One-Class Collaborative Filtering G Wu, M Volkovs, CL Soon, S Sanner, H Rai Proceedings of the 41st International ACM SIGIR Conference on Research and …, 2019 | 30 | 2019 |
| Explore-exploit graph traversal for image retrieval C Chang, G Yu, C Liu, M Volkovs Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 28 | 2019 |
| Probabilistic simulation of quantum circuits using a deep-learning architecture J Carrasquilla, D Luo, F Pérez, A Milsted, BK Clark, M Volkovs, L Aolita Physical Review A 104 (3), 032610, 2021 | 26 | 2021 |
| HGCF: Hyperbolic graph convolution networks for collaborative filtering J Sun, Z Cheng, S Zuberi, F Pérez, M Volkovs Proceedings of the Web Conference 2021, 593-601, 2021 | 26 | 2021 |
| Context models for web search personalization M Volkovs arXiv preprint arXiv:1502.00527, 2015 | 24 | 2015 |
| Guided similarity separation for image retrieval C Liu, G Yu, M Volkovs, C Chang, H Rai, J Ma, SK Gorti Advances in Neural Information Processing Systems 32, 2019 | 23 | 2019 |
| Efficient sampling for bipartite matching problems M Volkovs, R Zemel Advances in Neural Information Processing Systems 25, 2012 | 21 | 2012 |
| Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data M Ravaut, H Sadeghi, KK Leung, M Volkovs, K Kornas, V Harish, ... NPJ digital medicine 4 (1), 1-12, 2021 | 20 | 2021 |
Guangwei YuVerified email at cs.toronto.edu
