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Maksims Volkovs, PhD |
ABOUT
I received my PhD at the University of Toronto where I was a member of the Machine Learning Group led by Geoffrey Hinton. My research interests broadly span the field of machine learning with emphasis on recommender systems, information retrieval and time series modeling. I co-founded Layer 6 that was acquired by the TD Bank.
I actively participate in machine learning competitions on Kaggle,
where I achieved the grandmaster rank, and other platforms. My team won the
ACM RecSys Challenge, a leading international competition on recommender systems,
in 2017,
2018 and 2023. Layer 6
is the only organization that has won this challenge three times.
PUBLICATIONS
- Retrieval & Fine-Tuning for In-Context Tabular Models
Valentin Thomas, Junwei Ma, Rasa Hosseinzadeh, Keyvan Golestan, Guangwei Yu, Maksims Volkovs, Anthony Caterini
NeurIPS-2024: Neural Information Processing Systems
[pdf][code] - Data-Efficient Multimodal Fusion on a Single GPU
(Spotlight)
Noel Vouitsis, Zhaoyan Liu, Satya Krishna Gorti, Valentin Villecroze, Jesse C. Cresswell, Guangwei Yu, Gabriel Loaiza-Ganem, Maksims Volkovs
CVPR-2024: Computer Vision and Pattern Recognition
[pdf][code] - Self-supervised Representation Learning From Random Data Projectors
Yi Sui, Tongzi Wu, Jesse C. Cresswell, Ga Wu, George Stein, Xiao Shi Huang, Xiaochen Zhang, Maksims Volkovs
ICLR-2024: International Conference on Learning Representations
[pdf][code] - Robust User Engagement Modeling With Transformers and Self Supervision
(1'st Place 2023 ACM RecSys Challenge)
Yichao Lu, Maksims Volkovs
RecSys-2023: ACM Recommender Systems
[pdf] - Decentralized Federated Learning Through Proxy Model Sharing
Shivam Kalra, Junfeng Wen, Jesse Cresswell, Maksims Volkovs, Hamid Tizhoosh
Nature Communications 14 (2023)
[pdf][code][link] - DuETT: Dual Event Time Transformer for Electronic Health Records
Alex Labach, Saba Zuberi, Seung Eun Yi, Maksims Volkovs, Tomi Poutanen, Rahul G. Krishnan
MLHC-2023: Machine Learning for Healthcare
[pdf][code] - DiMS: Distilling Multiple Steps of Iterative Non-Autoregressive Transformers for Machine Translation
Sajad Norouzi, Rasa Hosseinzadeh, Felipe Pérez, Maksims Volkovs
ACL-2023: Association for Computational Linguistics
[pdf][code] - Temporal Dependencies in Feature Importance for Time Series Prediction
Kin Kwan Leung, Clayton Rooke, Jonathan Smith, Saba Zuberi, Maksims Volkovs
ICLR-2023: International Conference on Learning Representations
[pdf][code] - Session-based Recommendation with Transformers
Yichao Lu, Zhaolin Gao, Zhaoyue Cheng, Jianing Sun, Bradley Brown, Guangwei Yu, Anson Wong, Felipe Perez, Maksims Volkovs
RecSys-2022: ACM Recommender Systems
[pdf] - X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval
Satya Krishna Gorti, Noel Vouitsis, Junwei Ma, Keyvan Golestan, Maksims Volkovs, Animesh Garg, Guangwei Yu
CVPR-2022: Computer Vision and Pattern Recognition
[pdf][code] - Improving Non-Autoregressive Translation Models Without Distillation
Xiao Shi Huang, Felipe Pérez, Maksims Volkovs
ICLR-2022: International Conference on Learning Representations
[pdf][code] - MCL: Mixed-Centric Loss for Collaborative Filtering
Zhaolin Gao, Zhaoyue Cheng, Felipe Pérez, Jianing Sun, Maksims Volkovs
WWW-2022: International World Wide Web Conference
[pdf][code] - Context-aware Scene Graph Generation with Seq2Seq Transformers
Yichao Lu, Himanshu Rai, Jason Chang, Boris Knyazev, Shashank Shekhar, Graham W. Taylor, Maksims Volkovs
ICCV-2021: International Conference on Computer Vision
[pdf][code] - User Engagement Modeling with Deep Learning and Language Models
Maksims Volkovs, Felipe Perez, Zhaoyue Cheng, Jianing Sun, Sajad Norouzi, Anson Wong, Pawel Jankiewicz, Barum Rho
RecSys-2021: ACM Recommender Systems
[pdf] - Probabilistic Simulation of Quantum Circuits Using a Deep Learning Architecture
Juan Carrasquilla, Di Luo, Felipe Pérez, Ashley Milsted, Bryan K. Clark, Maksims Volkovs, Leandro Aolita
Physical Review 104 (2021)
[pdf][link] - Weakly Supervised Action Selection Learning in Video
Junwei Ma, Satya Krishna Gorti, Maksims Volkovs, Guangwei Yu
CVPR-2021: Computer Vision and Pattern Recognition
[pdf][code] - Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes
Mathieu Ravaut, Vinyas Harish, Hamed Sadeghi, Kin Kwan Leung, Maksims Volkovs, Kathy Kornas, Tomi Poutanen, Laura Rosella
JAMA Network Open 4, 5 (2021)
[pdf][link] [press coverage: 1, 2, 3, 4 ] - Predicting Adverse Outcomes Due to Diabetes Complications With Machine Learning Using Administrative Health Data
Mathieu Ravaut, Hamed Sadeghi, Kin Kwan Leung, Maksims Volkovs, Kathy Kornas, Vinyas Harish, Tristan Watson, Gary F. Lewis, Alanna Weisman, Tomi Poutanen, Laura Rosella
Nature Digital Medicine 4, 24 (2021)
[pdf][link] - HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering
Jianing Sun, Zhaoyue Cheng, Saba Zuberi, Felipe Pérez, Maksims Volkovs
WWW-2021: International World Wide Web Conference
[pdf][code] - Risk Stratification for COVID-19 Hospitalization: A Multivariable Model Based on Gradient-Boosting Decision Trees
Jahir M. Gutierrez, Maksims Volkovs, Tomi Poutanen, Tristan Watson, Laura C. Rosella
CMAJ 4, 9 (2021)
[pdf][link] - TAFA: Two-headed Attention Fused Autoencoder for Context-Aware Recommendations
Jin Peng Zhou, Zhaoyue Cheng, Felipe Pérez, Maksims Volkovs
RecSys-2020: ACM Recommender Systems
[pdf][code] - Predicting Twitter Engagement With Deep Language Models
Maksims Volkovs, Zhaoyue Cheng, Mathieu Ravaut, Hojin Yang, Kevin Shen, Jin Peng Zhou, Anson Wong, Saba Zuberi, Ivan Zhang, Nick Frosst, Helen Ngo, Carol Chen, Bharat Venkitesh, Stephen Gou, Aidan N. Gomez
RecSys-2020: ACM Recommender Systems
[pdf] - Improving Transformer Optimization Through Better Initialization
Xiao Shi Huang, Felipe Pérez, Jimmy Ba, Maksims Volkovs
ICML-2020: International Conference on Machine Learning
[pdf][supp][code] - Guided Similarity Separation for Image Retrieval
(Oral)
Chundi Liu, Guangwei Yu, Cheng Chang, Himanshu Rai, Junwei Ma, Satya Krishna Gorti, Maksims Volkovs
NeurIPS-2019: Neural Information Processing Systems
[pdf][code] - Cross-Class Relevance Learning for Temporal Concept Localization
(1'st Place 2019 YouTube-8M Video Understanding Challenge)
Junwei Ma, Satya Krishna Gorti, Maksims Volkovs, Ilya Stanevich, Guangwei Yu
ICCV-2019: Workshop on YouTube-8M Large-Scale Video Understanding
[pdf] - Learning Effective Visual Relationship Detector on 1 GPU
(1'st Place Open Images 2019 Visual Relationship Challenge)
Yichao Lu, Cheng Chang, Himanshu Rai, Guangwei Yu, Maksims Volkovs
ICCV-2019: Open Images 2019 Challenge Workshop
[pdf] - Robust Contextual Models for In-Session Personalization
Maksims Volkovs, Anson Wong, Zhaoyue Cheng, Felipe Perez, Ilya Stanevich, Yichao Lu
RecSys-2019: ACM Recommender Systems
[pdf][code] - Explore-Exploit Graph Traversal for Image Retrieval
Cheng Chang, Guangwei Yu, Chundi Liu, and Maksims Volkovs
CVPR-2019: Computer Vision and Pattern Recognition
[pdf][code] - Noise Contrastive Estimation for One-Class Collaborative Filtering
Ga Wu, Maksims Volkovs, Chee Loong Soon, Scott Sanner, Himanshu Rai
SIGIR-2019: Special Interest Group on Information Retrieval
[pdf] - Two-stage Model for Automatic Playlist Continuation at Scale
(1'st Place 2018 ACM RecSys Challenge)
Maksims Volkovs, Himanshu Rai, Zhaoyue Cheng, Ga Wu, Yichao Lu, Scott Sanner
RecSys-2018: ACM Recommender Systems
[pdf][code] - DropoutNet: Addressing Cold Start in Recommender Systems
Maksims Volkovs, Guang Wei Yu, Tomi Poutanen
NeurIPS-2017: Neural Information Processing Systems
[pdf][code] - Content-based Neighbor Models for Cold Start in Recommender Systems
(1'st Place 2017 ACM RecSys Challenge)
Maksims Volkovs, Guang Wei Yu, Tomi Poutanen
RecSys-2017: ACM Recommender Systems
[pdf] - Two-Stage Approach to Item Recommendation from User Sessions
Maksims Volkovs
RecSys-2015: ACM Recommender Systems
[pdf] - Effective Latent Models for Binary Feedback in Recommender Systems
Maksims Volkovs, 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
[pdf] - New Learning Methods for Supervised and Unsupervised Preference Aggregation
Maksims Volkovs, Richard Zemel
JMLR-2014: Journal of Machine Learning Research
[pdf] - Continuous Data Cleaning
Maksims Volkovs, Fei Chiang, Jaroslaw Szlichta, Renée J. Miller
ICDE-2014: IEEE International Conference on Data Engineering
[pdf] - Supervised CRF Framework for Preference Aggregation
Maksims Volkovs, Richard Zemel
CIKM-2013: International Conference on Information and Knowledge Management
[pdf][code] - Collaborative Ranking with 17 Parameters
Maksims Volkovs, Richard Zemel
NeurIPS-2012: Neural Information Processing Systems
[pdf] - Efficient Sampling for Bipartite Matching Problems
Maksims Volkovs, Richard Zemel
NeurIPS-2012: Neural Information Processing Systems
[pdf][supp] - Learning to Rank By Aggregating Expert Preferences
Maksims Volkovs, Hugo Larochelle, Richard Zemel
CIKM-2012: International Conference on Information and Knowledge Management
[pdf][code] - A Flexible Generative Model for Preference Aggregation
Maksims Volkovs, Richard Zemel
WWW-2012: International World Wide Web Conference
[pdf][code] - Learning to Rank with Multiple Objective Functions
Krysta M. Svore, Maksims Volkovs, Christopher J. C. Burges
WWW-2011: International World Wide Web Conference
[pdf] - BoltzRank: Learning to Maximize Expected Ranking Gain
(Best Student Paper)
Maksims Volkovs, Richard Zemel
ICML-2009: International Conference on Machine Learning
[pdf] - ConEx: A System for Monitoring Queries
Chaitanya Mishra, Maksims Volkovs
SIGMOD-2007: International Conference on Management of Data
[pdf]