Photo

    Maksims Volkovs, Ph.D.

    Layer 6 AI
    Email: maksims [dot] volkovs [at] gmail [dot] com



ABOUT

I completed my PhD in 2013 at the University of Toronto where I was a member of the Machine Learning Group working with Richard Zemel. My research interests broadly span the field of machine learning with emphasis on information retrieval, recommender systems and NLP. I co-founded Layer 6 AI, a start-up that focuses on machine learning for the enterprise which was acquired by the TD Bank.

I actively participate in data science competitions on Kaggle and other platforms, here are some highlights:

Publications

  • Explore-Exploit Graph Traversal for Image Retrieval
    Cheng Chang, Guangwei Yu, Chundi Liu and Maksims Volkovs
    CVPR-2019: Conference on Computer Vision and Pattern Recognition
    [pdf][code]
  • Two-stage Model for Automatic Playlist Continuation at Scale
    (2018 ACM RecSys Challenge winner)
    Maksims Volkovs, Himanshu Rai, Zhaoyue Cheng, Ga Wu, Yichao Lu and Scott Sanner
    RecSys-2018: ACM Conference on Recommender Systems
    [pdf][code]
  • DropoutNet: Addressing Cold Start in Recommender Systems
    Maksims Volkovs, Guang Wei Yu and Tomi Poutanen
    NIPS-2017: Neural Information Processing Systems
    [pdf][code]
  • Content-based Neighbor Models for Cold Start in Recommender Systems
    (2017 ACM RecSys Challenge winner)
    Maksims Volkovs, Guang Wei Yu and Tomi Poutanen
    RecSys-2017: ACM Conference on Recommender Systems
    [pdf]
  • Two-Stage Approach to Item Recommendation from User Sessions
    Maksims Volkovs
    RecSys-2015: ACM Conference on Recommender Systems
    [pdf]
  • Effective Latent Models for Binary Feedback in Recommender Systems
    Maksims Volkovs and Guang Wei Yu
    SIGIR-2015: Special Interest Group on Information Retrieval
    [pdf] [code]
  • Context Models For Web Search Personalization
    Maksims Volkovs
    WSDM-2014: WSCD Workshop on Log-based Personalization
    [arXiv: 1502.00527]
  • New Learning Methods for Supervised and Unsupervised Preference Aggregation
    Maksims Volkovs and Richard Zemel
    JMLR-2014: Journal of Machine Learning Research
    [pdf]
  • Continuous Data Cleaning
    Maksims Volkovs, Fei Chiang, Jaroslaw Szlichta and Renée J. Miller
    ICDE-2014: IEEE International Conference on Data Engineering
    [pdf]
  • Supervised CRF Framework for Preference Aggregation
    Maksims Volkovs and Richard Zemel
    CIKM-2013: International Conference on Information and Knowledge Management
    [pdf][code]
  • Collaborative Ranking with 17 Parameters
    Maksims Volkovs and Richard Zemel
    NIPS-2012: Neural Information Processing Systems
    [pdf]
  • Efficient Sampling for Bipartite Matching Problems
    Maksims Volkovs and Richard Zemel
    NIPS-2012: Neural Information Processing Systems
    [pdf][supplementary]
  • Learning to Rank By Aggregating Expert Preferences
    Maksims Volkovs, Hugo Larochelle and Richard Zemel
    CIKM-2012: International Conference on Information and Knowledge Management
    [pdf][code]
  • A Flexible Generative Model for Preference Aggregation
    Maksims Volkovs and Richard Zemel
    WWW-2012: International World Wide Web Conference
    [pdf][code]
  • Learning to Rank with Multiple Objective Functions
    Krysta M. Svore, Maksims Volkovs and Christopher J. C. Burges
    WWW-2011: International World Wide Web Conference
    [pdf]
  • BoltzRank: Learning to Maximize Expected Ranking Gain
    (Best student paper)
    Maksims Volkovs and Richard Zemel
    ICML-2009: International Conference on Machine Learning
    [pdf]
  • ConEx: A System for Monitoring Queries
    Chaitanya Mishra and Maksims Volkovs
    SIGMOD-2007: International Conference on Management of Data
    [pdf]