ConEx: a system for monitoring queries. - Chaitanya Mishra; Maksims Volkovs (SIGMOD Conference, 2007)

BoltzRank: learning to maximize expected ranking gain. - Maksims Volkovs; Richard S. Zemel (ICML, 2009)

Learning to rank with multiple objective functions. - Krysta M. Svore; Maksims Volkovs; Christopher J. C. Burges (WWW, 2011)

Loss-sensitive Training of Probabilistic Conditional Random Fields - Maksims Volkovs; Hugo Larochelle; Richard S. Zemel (CoRR, 2011)

Learning to rank by aggregating expert preferences. - Maksims Volkovs; Hugo Larochelle; Richard S. Zemel (CIKM, 2012)

Efficient Sampling for Bipartite Matching Problems. - Maksims Volkovs; Richard S. Zemel (NIPS, 2012)

Collaborative Ranking With 17 Parameters. - Maksims Volkovs; Richard S. Zemel (NIPS, 2012)

A flexible generative model for preference aggregation. - Maksims Volkovs; Richard S. Zemel (WWW, 2012)

CRF framework for supervised preference aggregation. - Maksims Volkovs; Richard S. Zemel (CIKM, 2013)

Machine Learning Methods and Models for Ranking. - Maksims Volkovs (2013)

Continuous data cleaning. - Maksims Volkovs; Fei Chiang; Jaroslaw Szlichta; Renée J. Miller (ICDE, 2014)

New learning methods for supervised and unsupervised preference aggregation. - Maksims Volkovs; Richard S. Zemel (J. Mach. Learn. Res., 2014)

Two-Stage Approach to Item Recommendation from User Sessions. - Maksims Volkovs (RecSys Challenge, 2015)

Effective Latent Models for Binary Feedback in Recommender Systems. - Maksims Volkovs; Guangwei Yu (SIGIR, 2015)

Context Models For Web Search Personalization. - Maksims Volkovs (CoRR, 2015)

DropoutNet: Addressing Cold Start in Recommender Systems. - Maksims Volkovs; Guangwei Yu; Tomi Poutanen (NIPS, 2017)

Learning Document Embeddings With CNNs. - Chundi Liu; Shunan Zhao; Maksims Volkovs (CoRR, 2017)

Two-stage Model for Automatic Playlist Continuation at Scale. - Maksims Volkovs; Himanshu Rai; Zhaoyue Cheng; Ga Wu; Yichao Lu; Scott Sanner (RecSys Challenge, 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)

Explore-Exploit Graph Traversal for Image Retrieval. - Cheng Chang; Guangwei Yu; Chundi Liu; Maksims Volkovs (CVPR, 2019)

Guided Similarity Separation for Image Retrieval. - Chundi Liu; Guangwei Yu; Maksims Volkovs; Cheng Chang; Himanshu Rai; Junwei Ma; Satya Krishna Gorti (NeurIPS, 2019)

Robust contextual models for in-session personalization. - Maksims Volkovs; Anson Wong; Zhaoyue Cheng; Felipe Pérez; Ilya Stanevich; Yichao Lu (RecSys Challenge, 2019)

Noise Contrastive Estimation for One-Class Collaborative Filtering. - Ga Wu; Maksims Volkovs; Chee Loong Soon; Scott Sanner; Himanshu Rai (SIGIR, 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)

Semi-Supervised Exploration in Image Retrieval. - Cheng Chang; Himanshu Rai; Satya Krishna Gorti; Junwei Ma; Chundi Liu; Guangwei Yu; Maksims Volkovs (CoRR, 2019)

Cross-Class Relevance Learning for Temporal Concept Localization. - Junwei Ma; Satya Krishna Gorti; Maksims Volkovs; Ilya Stanevich; Guangwei Yu (CoRR, 2019)

Learning Effective Visual Relationship Detector on 1 GPU. - Yichao Lu; Cheng Chang; Himanshu Rai; Guangwei Yu; Maksims Volkovs (CoRR, 2019)

Improving Transformer Optimization Through Better Initialization. - Xiao Shi Huang; Felipe Pérez; Jimmy Ba; Maksims Volkovs (ICML, 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; et al. (RecSys Challenge, 2020)

TAFA: Two-headed Attention Fused Autoencoder for Context-Aware Recommendations. - Jin Peng Zhou; Zhaoyue Cheng; Felipe Pérez; Maksims Volkovs (RecSys, 2020)

Weakly Supervised Action Selection Learning in Video. - Junwei Ma; Satya Krishna Gorti; Maksims Volkovs; Guangwei Yu (CVPR, 2021)

Context-aware Scene Graph Generation with Seq2Seq Transformers. - Yichao Lu; Himanshu Rai; Jason Chang; Boris Knyazev 001; Guangwei Yu; Shashank Shekhar 005; Graham W. Taylor; Maksims Volkovs (ICCV, 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 (RecSys Challenge, 2021)

HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering. - Jianing Sun; Zhaoyue Cheng; Saba Zuberi; Felipe Pérez; Maksims Volkovs (WWW, 2021)

Temporal Dependencies in Feature Importance for Time Series Predictions. - Clayton Rooke; Jonathan Smith; Kin Kwan Leung; Maksims Volkovs; Saba Zuberi (CoRR, 2021)

ProxyFL: Decentralized Federated Learning through Proxy Model Sharing. - Shivam Kalra; Junfeng Wen; Jesse C. Cresswell; Maksims Volkovs; Hamid R. Tizhoosh (CoRR, 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)

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 (CVPR, 2022)

Improving Non-Autoregressive Translation Models Without Distillation. - Xiao Shi Huang; Felipe Pérez; Maksims Volkovs (ICLR, 2022)

MCL: Mixed-Centric Loss for Collaborative Filtering. - Zhaolin Gao; Zhaoyue Cheng; Felipe Pérez; Jianing Sun; Maksims Volkovs (WWW, 2022)

DiMS: Distilling Multiple Steps of Iterative Non-Autoregressive Transformers. - Sajad Norouzi; Rasa Hosseinzadeh; Felipe Pérez; Maksims Volkovs (CoRR, 2022)

DiMS: Distilling Multiple Steps of Iterative Non-Autoregressive Transformers for Machine Translation. - Sajad Norouzi; Rasa Hosseinzadeh; Felipe Pérez; Maksims Volkovs (ACL (Findings), 2023)

Temporal Dependencies in Feature Importance for Time Series Prediction. - Kin Kwan Leung; Clayton Rooke; Jonathan Smith; Saba Zuberi; Maksims Volkovs (ICLR, 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 (MLHC, 2023)

Robust User Engagement Modeling With Transformers and Self Supervision. - Yichao Lu; Maksims Volkovs (RecSys Challenge, 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)

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 (CVPR, 2024)

Self-supervised Representation Learning from Random Data Projectors. - Yi Sui 001; Tongzi Wu; Jesse C. Cresswell; Ga Wu; George Stein; Xiao Shi Huang; Xiaochen Zhang; Maksims Volkovs (ICLR, 2024)

Retrieval & Fine-Tuning for In-Context Tabular Models. - Valentin Thomas; Junwei Ma; Rasa Hosseinzadeh; Keyvan Golestan; Guangwei Yu; Maksims Volkovs; Anthony L. Caterini (NeurIPS, 2024)

TabDPT: Scaling Tabular Foundation Models. - Junwei Ma; Valentin Thomas; Rasa Hosseinzadeh; Hamidreza Kamkari; Alex Labach; Jesse C. Cresswell; Keyvan Golestan; Guangwei Yu; Maksims Volkovs; Anthony L. Caterini (CoRR, 2024)