Maksims Volkovs

According to our database, Maksims Volkovs authored at least 50 papers between 2007 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Retrieval & Fine-Tuning for In-Context Tabular Models.

Valentin Thomas, Junwei Ma, Rasa Hosseinzadeh, Keyvan Golestan, Guangwei Yu, Maksims Volkovs, Anthony L. Caterini

CoRR, 2024

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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Data-Efficient Multimodal Fusion on a Single GPU.

Noël Vouitsis, Zhaoyan Liu, Satya Krishna Gorti, Valentin Villecroze, Jesse C. Cresswell, Guangwei Yu, Gabriel Loaiza-Ganem, Maksims Volkovs

CoRR, 2023

MultiResFormer: Transformer with Adaptive Multi-Resolution Modeling for General Time Series Forecasting.

Linfeng Du, Ji Xin, Alex Labach, Saba Zuberi, Maksims Volkovs, Rahul G. Krishnan

CoRR, 2023

Robust User Engagement Modeling With Transformers and Self Supervision.

Yichao Lu, Maksims Volkovs

Proceedings of the ACM RecSys Challenge 2023, Singapore, 19 September 2023, 2023

DuETT: Dual Event Time Transformer for Electronic Health Records.

Alex Labach, Aslesha Pokhrel, Xiao Shi Huang, Saba Zuberi, Seung Eun Yi, Maksims Volkovs, Tomi Poutanen, Rahul G. Krishnan

Proceedings of the Machine Learning for Healthcare Conference, 2023

Temporal Dependencies in Feature Importance for Time Series Prediction.

Kin Kwan Leung, Clayton Rooke, Jonathan Smith, Saba Zuberi, Maksims Volkovs

Proceedings of the Eleventh International Conference on Learning Representations, 2023

DiMS: Distilling Multiple Steps of Iterative Non-Autoregressive Transformers for Machine Translation.

Sajad Norouzi, Rasa Hosseinzadeh, Felipe Pérez, Maksims Volkovs

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

2022

DiMS: Distilling Multiple Steps of Iterative Non-Autoregressive Transformers.

Sajad Norouzi, Rasa Hosseinzadeh, Felipe Pérez, Maksims Volkovs

CoRR, 2022

X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval.

Satya Krishna Gorti, Noël Vouitsis, Junwei Ma, Keyvan Golestan, Maksims Volkovs, Animesh Garg, Guang Wei Yu

CoRR, 2022

MCL: Mixed-Centric Loss for Collaborative Filtering.

Zhaolin Gao, Zhaoyue Cheng, Felipe Pérez, Jianing Sun, Maksims Volkovs

Proceedings of the WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25, 2022

Improving Non-Autoregressive Translation Models Without Distillation.

Xiao Shi Huang, Felipe Pérez, Maksims Volkovs

Proceedings of the Tenth International Conference on Learning Representations, 2022

X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval.

Satya Krishna Gorti, Noël Vouitsis, Junwei Ma, Keyvan Golestan, Maksims Volkovs, Animesh Garg, Guangwei Yu

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

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 C. Rosella

npj Digit. Medicine, 2021

ProxyFL: Decentralized Federated Learning through Proxy Model Sharing.

Shivam Kalra, Junfeng Wen, Jesse C. Cresswell, Maksims Volkovs, Hamid R. Tizhoosh

CoRR, 2021

Temporal Dependencies in Feature Importance for Time Series Predictions.

Clayton Rooke, Jonathan Smith, Kin Kwan Leung, Maksims Volkovs, Saba Zuberi

CoRR, 2021

HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering.

Jianing Sun, Zhaoyue Cheng, Saba Zuberi, Felipe Pérez, Maksims Volkovs

Proceedings of the WWW '21: The Web Conference 2021, 2021

User Engagement Modeling with Deep Learning and Language Models.

Maksims Volkovs, Felipe Pérez, Zhaoyue Cheng, Jianing Sun, Sajad Norouzi, Anson Wong, Pawel Jankiewicz, Barum Rho

Proceedings of the RecSys Challenge 2021: Proceedings of the Recommender Systems Challenge 2021, 2021

Context-aware Scene Graph Generation with Seq2Seq Transformers.

Yichao Lu, Himanshu Rai, Jason Chang, Boris Knyazev, Guang Wei Yu, Shashank Shekhar, Graham W. Taylor, Maksims Volkovs

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Weakly Supervised Action Selection Learning in Video.

Junwei Ma, Satya Krishna Gorti, Maksims Volkovs, Guang Wei Yu

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020

TAFA: Two-headed Attention Fused Autoencoder for Context-Aware Recommendations.

Jin Peng Zhou, Zhaoyue Cheng, Felipe Pérez, Maksims Volkovs

Proceedings of the RecSys 2020: Fourteenth ACM Conference on Recommender Systems, 2020

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

Proceedings of the RecSys Challenge '20: Proceedings of the Recommender Systems Challenge 2020, 2020

Improving Transformer Optimization Through Better Initialization.

Xiao Shi Huang, Felipe Pérez, Jimmy Ba, Maksims Volkovs

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Learning Effective Visual Relationship Detector on 1 GPU.

Yichao Lu, Cheng Chang, Himanshu Rai, Guang Wei Yu, Maksims Volkovs

CoRR, 2019

Cross-Class Relevance Learning for Temporal Concept Localization.

Junwei Ma, Satya Krishna Gorti, Maksims Volkovs, Ilya Stanevich, Guang Wei Yu

CoRR, 2019

Semi-Supervised Exploration in Image Retrieval.

Cheng Chang, Himanshu Rai, Satya Krishna Gorti, Junwei Ma, Chundi Liu, Guang Wei Yu, Maksims Volkovs

CoRR, 2019

Diabetes Mellitus Forecasting Using Population Health Data in Ontario, Canada.

Mathieu Ravaut, Hamed Sadeghi, Kin Kwan Leung, Maksims Volkovs, Laura C. Rosella

CoRR, 2019

Noise Contrastive Estimation for One-Class Collaborative Filtering.

Ga Wu, Maksims Volkovs, Chee Loong Soon, Scott Sanner, Himanshu Rai

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019

Robust contextual models for in-session personalization.

Maksims Volkovs, Anson Wong, Zhaoyue Cheng, Felipe Pérez, Ilya Stanevich, Yichao Lu

Proceedings of the Workshop on ACM Recommender Systems Challenge, 2019

Guided Similarity Separation for Image Retrieval.

Chundi Liu, Guang Wei Yu, Maksims Volkovs, Cheng Chang, Himanshu Rai, Junwei Ma, Satya Krishna Gorti

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Explore-Exploit Graph Traversal for Image Retrieval.

Cheng Chang, Guang Wei Yu, Chundi Liu, Maksims Volkovs

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Noise Contrastive Estimation for Scalable Linear Models for One-Class Collaborative Filtering.

Ga Wu, Maksims Volkovs, Chee Loong Soon, Scott Sanner, Himanshu Rai

CoRR, 2018

Two-stage Model for Automatic Playlist Continuation at Scale.

Maksims Volkovs, Himanshu Rai, Zhaoyue Cheng, Ga Wu, Yichao Lu, Scott Sanner

Proceedings of the ACM Recommender Systems Challenge, 2018

2017

Learning Document Embeddings With CNNs.

Chundi Liu, Shunan Zhao, Maksims Volkovs

CoRR, 2017

DropoutNet: Addressing Cold Start in Recommender Systems.

Maksims Volkovs, Guang Wei Yu, Tomi Poutanen

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2015

Context Models For Web Search Personalization.

Maksims Volkovs

CoRR, 2015

Effective Latent Models for Binary Feedback in Recommender Systems.

Maksims Volkovs, Guang Wei Yu

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015

Two-Stage Approach to Item Recommendation from User Sessions.

Maksims Volkovs

Proceedings of the 2015 International ACM Recommender Systems Challenge, 2015

2014

New learning methods for supervised and unsupervised preference aggregation.

Maksims Volkovs, Richard S. Zemel

J. Mach. Learn. Res., 2014

Continuous data cleaning.

Maksims Volkovs, Fei Chiang, Jaroslaw Szlichta, Renée J. Miller

Proceedings of the IEEE 30th International Conference on Data Engineering, Chicago, 2014

2013

Machine Learning Methods and Models for Ranking.

Maksims Volkovs

PhD thesis, 2013

CRF framework for supervised preference aggregation.

Maksims Volkovs, Richard S. Zemel

Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, 2013

2012

A flexible generative model for preference aggregation.

Maksims Volkovs, Richard S. Zemel

Proceedings of the 21st World Wide Web Conference 2012, 2012

Collaborative Ranking With 17 Parameters.

Maksims Volkovs, Richard S. Zemel

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Efficient Sampling for Bipartite Matching Problems.

Maksims Volkovs, Richard S. Zemel

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Learning to rank by aggregating expert preferences.

Maksims Volkovs, Hugo Larochelle, Richard S. Zemel

Proceedings of the 21st ACM International Conference on Information and Knowledge Management, 2012

2011

Loss-sensitive Training of Probabilistic Conditional Random Fields

Maksims Volkovs, Hugo Larochelle, Richard S. Zemel

CoRR, 2011

Learning to rank with multiple objective functions.

Krysta M. Svore, Maksims Volkovs, Christopher J. C. Burges

Proceedings of the 20th International Conference on World Wide Web, 2011

2009

BoltzRank: learning to maximize expected ranking gain.

Maksims Volkovs, Richard S. Zemel

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2007

ConEx: a system for monitoring queries.

Chaitanya Mishra, Maksims Volkovs

Proceedings of the ACM SIGMOD International Conference on Management of Data, 2007