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2020 – today
- 2024
[c33]Noël Vouitsis, Zhaoyan Liu, Satya Krishna Gorti, Valentin Villecroze, Jesse C. Cresswell, Guangwei Yu, Gabriel Loaiza-Ganem, Maksims Volkovs:
Data-Efficient Multimodal Fusion on a Single GPU. CVPR 2024: 27229-27241- [c32]Yi Sui, Tongzi Wu, Jesse C. Cresswell, Ga Wu, George Stein, Xiao Shi Huang, Xiaochen Zhang, Maksims Volkovs:
Self-supervised Representation Learning from Random Data Projectors. ICLR 2024 - [i19]Valentin Thomas, Junwei Ma, Rasa Hosseinzadeh, Keyvan Golestan, Guangwei Yu, Maksims Volkovs, Anthony L. Caterini:
Retrieval & Fine-Tuning for In-Context Tabular Models. CoRR abs/2406.05207 (2024) - [i18]Junwei Ma, Valentin Thomas, Rasa Hosseinzadeh, Hamidreza Kamkari, Alex Labach, Jesse C. Cresswell, Keyvan Golestan, Guangwei Yu, Maksims Volkovs, Anthony L. Caterini:
TabDPT: Scaling Tabular Foundation Models. CoRR abs/2410.18164 (2024) - 2023
- [c31]Sajad Norouzi, Rasa Hosseinzadeh, Felipe Pérez, Maksims Volkovs:
DiMS: Distilling Multiple Steps of Iterative Non-Autoregressive Transformers for Machine Translation. ACL (Findings) 2023: 8538-8553 - [c30]Kin Kwan Leung, Clayton Rooke, Jonathan Smith, Saba Zuberi, Maksims Volkovs:
Temporal Dependencies in Feature Importance for Time Series Prediction. ICLR 2023 - [c29]Alex Labach, Aslesha Pokhrel, Xiao Shi Huang, Saba Zuberi, Seung Eun Yi, Maksims Volkovs, Tomi Poutanen, Rahul G. Krishnan:
DuETT: Dual Event Time Transformer for Electronic Health Records. MLHC 2023: 403-422 - [c28]Yichao Lu
, Maksims Volkovs:
Robust User Engagement Modeling With Transformers and Self Supervision. RecSys Challenge 2023: 23-27 - [i17]Alex Labach, Aslesha Pokhrel, Xiao Shi Huang, Saba Zuberi, Seung Eun Yi, Maksims Volkovs, Tomi Poutanen, Rahul G. Krishnan:
DuETT: Dual Event Time Transformer for Electronic Health Records. CoRR abs/2304.13017 (2023) - [i16]Yi Sui, Tongzi Wu, Jesse C. Cresswell, Ga Wu, George Stein, Xiao Shi Huang, Xiaochen Zhang, Maksims Volkovs:
Self-supervised Representation Learning From Random Data Projectors. CoRR abs/2310.07756 (2023) - [i15]Linfeng Du, Ji Xin, Alex Labach, Saba Zuberi, Maksims Volkovs, Rahul G. Krishnan:
MultiResFormer: Transformer with Adaptive Multi-Resolution Modeling for General Time Series Forecasting. CoRR abs/2311.18780 (2023) - [i14]Noël Vouitsis, Zhaoyan Liu, Satya Krishna Gorti, Valentin Villecroze, Jesse C. Cresswell, Guangwei Yu, Gabriel Loaiza-Ganem, Maksims Volkovs:
Data-Efficient Multimodal Fusion on a Single GPU. CoRR abs/2312.10144 (2023) - 2022
- [c27]Satya Krishna Gorti, Noël Vouitsis, Junwei Ma, Keyvan Golestan, Maksims Volkovs, Animesh Garg, Guangwei Yu:
X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval. CVPR 2022: 4996-5005 - [c26]Xiao Shi Huang, Felipe Pérez, Maksims Volkovs:
Improving Non-Autoregressive Translation Models Without Distillation. ICLR 2022 - [c25]Zhaolin Gao, Zhaoyue Cheng, Felipe Pérez, Jianing Sun, Maksims Volkovs:
MCL: Mixed-Centric Loss for Collaborative Filtering. WWW 2022: 2339-2347 - [i13]Satya Krishna Gorti, Noël Vouitsis, Junwei Ma, Keyvan Golestan, Maksims Volkovs, Animesh Garg, Guang Wei Yu:
X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval. CoRR abs/2203.15086 (2022) - [i12]Sajad Norouzi, Rasa Hosseinzadeh, Felipe Pérez, Maksims Volkovs:
DiMS: Distilling Multiple Steps of Iterative Non-Autoregressive Transformers. CoRR abs/2206.02999 (2022) - 2021
- [j2]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:
Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data. npj Digit. Medicine 4 (2021) - [c24]Junwei Ma, Satya Krishna Gorti, Maksims Volkovs, Guang Wei Yu:
Weakly Supervised Action Selection Learning in Video. CVPR 2021: 7587-7596 - [c23]Yichao Lu, Himanshu Rai, Jason Chang, Boris Knyazev, Guang Wei Yu, Shashank Shekhar, Graham W. Taylor, Maksims Volkovs:
Context-aware Scene Graph Generation with Seq2Seq Transformers. ICCV 2021: 15911-15921 - [c22]Maksims Volkovs, Felipe Pérez, Zhaoyue Cheng, Jianing Sun, Sajad Norouzi, Anson Wong, Pawel Jankiewicz, Barum Rho:
User Engagement Modeling with Deep Learning and Language Models. RecSys Challenge 2021: 22-27 - [c21]Jianing Sun, Zhaoyue Cheng, Saba Zuberi, Felipe Pérez, Maksims Volkovs:
HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering. WWW 2021: 593-601 - [i11]Junwei Ma, Satya Krishna Gorti, Maksims Volkovs, Guang Wei Yu:
Weakly Supervised Action Selection Learning in Video. CoRR abs/2105.02439 (2021) - [i10]Clayton Rooke, Jonathan Smith, Kin Kwan Leung, Maksims Volkovs, Saba Zuberi:
Temporal Dependencies in Feature Importance for Time Series Predictions. CoRR abs/2107.14317 (2021) - [i9]Shivam Kalra, Junfeng Wen, Jesse C. Cresswell, Maksims Volkovs, Hamid R. Tizhoosh:
ProxyFL: Decentralized Federated Learning through Proxy Model Sharing. CoRR abs/2111.11343 (2021) - 2020
- [c20]Xiao Shi Huang, Felipe Pérez, Jimmy Ba, Maksims Volkovs:
Improving Transformer Optimization Through Better Initialization. ICML 2020: 4475-4483 - [c19]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:
Predicting Twitter Engagement With Deep Language Models. RecSys Challenge 2020: 38-43 - [c18]Jin Peng Zhou, Zhaoyue Cheng, Felipe Pérez, Maksims Volkovs:
TAFA: Two-headed Attention Fused Autoencoder for Context-Aware Recommendations. RecSys 2020: 338-347
2010 – 2019
- 2019
- [c17]Cheng Chang, Guang Wei Yu, Chundi Liu, Maksims Volkovs:
Explore-Exploit Graph Traversal for Image Retrieval. CVPR 2019: 9423-9431 - [c16]Chundi Liu, Guang Wei Yu, Maksims Volkovs, Cheng Chang, Himanshu Rai, Junwei Ma, Satya Krishna Gorti:
Guided Similarity Separation for Image Retrieval. NeurIPS 2019: 1554-1564 - [c15]Maksims Volkovs, Anson Wong, Zhaoyue Cheng, Felipe Pérez, Ilya Stanevich, Yichao Lu:
Robust contextual models for in-session personalization. RecSys Challenge 2019: 2:1-2:5 - [c14]Ga Wu, Maksims Volkovs, Chee Loong Soon, Scott Sanner, Himanshu Rai:
Noise Contrastive Estimation for One-Class Collaborative Filtering. SIGIR 2019: 135-144 - [i8]Mathieu Ravaut, Hamed Sadeghi, Kin Kwan Leung, Maksims Volkovs, Laura C. Rosella:
Diabetes Mellitus Forecasting Using Population Health Data in Ontario, Canada. CoRR abs/1904.04137 (2019) - [i7]Cheng Chang, Himanshu Rai, Satya Krishna Gorti, Junwei Ma, Chundi Liu, Guang Wei Yu, Maksims Volkovs:
Semi-Supervised Exploration in Image Retrieval. CoRR abs/1906.04944 (2019) - [i6]Junwei Ma, Satya Krishna Gorti, Maksims Volkovs, Ilya Stanevich, Guang Wei Yu:
Cross-Class Relevance Learning for Temporal Concept Localization. CoRR abs/1911.08548 (2019) - [i5]Yichao Lu, Cheng Chang, Himanshu Rai, Guang Wei Yu, Maksims Volkovs:
Learning Effective Visual Relationship Detector on 1 GPU. CoRR abs/1912.06185 (2019) - 2018
- [c13]Maksims Volkovs, Himanshu Rai, Zhaoyue Cheng, Ga Wu, Yichao Lu, Scott Sanner:
Two-stage Model for Automatic Playlist Continuation at Scale. RecSys Challenge 2018: 9:1-9:6 - [i4]Ga Wu, Maksims Volkovs, Chee Loong Soon, Scott Sanner, Himanshu Rai:
Noise Contrastive Estimation for Scalable Linear Models for One-Class Collaborative Filtering. CoRR abs/1811.00697 (2018) - 2017
- [c12]Maksims Volkovs, Guang Wei Yu, Tomi Poutanen:
DropoutNet: Addressing Cold Start in Recommender Systems. NIPS 2017: 4957-4966 - [i3]Chundi Liu, Shunan Zhao, Maksims Volkovs:
Learning Document Embeddings With CNNs. CoRR abs/1711.04168 (2017) - 2015
- [c11]Maksims Volkovs:
Two-Stage Approach to Item Recommendation from User Sessions. RecSys Challenge 2015: 3:1-3:4 - [c10]Maksims Volkovs, Guang Wei Yu:
Effective Latent Models for Binary Feedback in Recommender Systems. SIGIR 2015: 313-322 - [i2]Maksims Volkovs:
Context Models For Web Search Personalization. CoRR abs/1502.00527 (2015) - 2014
- [j1]Maksims Volkovs, Richard S. Zemel:
New learning methods for supervised and unsupervised preference aggregation. J. Mach. Learn. Res. 15(1): 1135-1176 (2014) - [c9]Maksims Volkovs, Fei Chiang, Jaroslaw Szlichta, Renée J. Miller:
Continuous data cleaning. ICDE 2014: 244-255 - 2013
- [b1]Maksims Volkovs:
Machine Learning Methods and Models for Ranking. University of Toronto, Canada, 2013 - [c8]Maksims Volkovs, Richard S. Zemel:
CRF framework for supervised preference aggregation. CIKM 2013: 89-98 - 2012
- [c7]Maksims Volkovs, Hugo Larochelle, Richard S. Zemel:
Learning to rank by aggregating expert preferences. CIKM 2012: 843-851 - [c6]Maksims Volkovs, Richard S. Zemel:
Efficient Sampling for Bipartite Matching Problems. NIPS 2012: 1322-1330 - [c5]Maksims Volkovs, Richard S. Zemel:
Collaborative Ranking With 17 Parameters. NIPS 2012: 2303-2311 - [c4]Maksims Volkovs, Richard S. Zemel:
A flexible generative model for preference aggregation. WWW 2012: 479-488 - 2011
- [c3]Krysta M. Svore, Maksims Volkovs, Christopher J. C. Burges:
Learning to rank with multiple objective functions. WWW 2011: 367-376 - [i1]Maksims Volkovs, Hugo Larochelle, Richard S. Zemel:
Loss-sensitive Training of Probabilistic Conditional Random Fields. CoRR abs/1107.1805 (2011)
2000 – 2009
- 2009
- [c2]Maksims Volkovs, Richard S. Zemel:
BoltzRank: learning to maximize expected ranking gain. ICML 2009: 1089-1096 - 2007
- [c1]Chaitanya Mishra, Maksims Volkovs:
ConEx: a system for monitoring queries. SIGMOD Conference 2007: 1076-1078
Coauthor Index
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last updated on 2024-11-28 20:34 CET by the dblp team
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