Dawen Liang

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Citations	7440	6541
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i10-index	28	22

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Daniel P W EllisResearch Scientist, GoogleVerified email at google.com
Matthew D. HoffmanResearch Scientist, Google ResearchVerified email at google.com
Brian McFeeMusic and Performing Arts Professions / Center for Data Science, New York UniversityVerified email at nyu.edu
Oriol NietoSenior Research Engineer at AdobeVerified email at adobe.com
David BleiProfessor of Statistics and Computer Science, Columbia UniversityVerified email at columbia.edu
Colin RaffelUniversity of Toronto, Vector Institute and Hugging FaceVerified email at cs.toronto.edu
Laurent CharlinAssociate Professor, HEC Montréal & Mila, Canada CIFAR AI ChairVerified email at hec.ca
Justin SalamonSenior Research Scientist, Adobe ResearchVerified email at adobe.com
Eric J. HumphreyLead Research Scientist, This One LtdVerified email at this.one
James McInerneySenior Research Scientist, NetflixVerified email at netflix.com
Dustin TranResearch Scientist, GoogleVerified email at google.com
Alp KucukelbirAdjunct Professor of Computer Science, Columbia UniversityVerified email at cs.columbia.edu
Maja RudolphUW MadisonVerified email at wisc.edu
Adji Bousso DiengAssistant Professor of Computer Science, Princeton UniversityVerified email at princeton.edu
Gus (Guangyu) XiaAssistant Professor of Machine Learning, MBZUAIVerified email at nyu.edu
Roger B. DannenbergProfessor of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Gautham J. MysoreSenior Principal Scientist, Adobe ResearchVerified email at adobe.com
Nicolas GoldAssociate Professor in Computer Science, University College LondonVerified email at ucl.ac.uk
John PaisleyColumbia UniversityVerified email at columbia.edu
Brendan O'ConnorUniversity of Massachusetts AmherstVerified email at cs.umass.edu

Dawen Liang

Research Scientist, Netflix Inc.

Verified email at netflix.com - Homepage

Machine Learning Recommender Systems RL


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librosa: Audio and music signal analysis in python. B McFee, C Raffel, D Liang, DPW Ellis, M McVicar, E Battenberg, O Nieto SciPy, 18-24, 2015	3153	2015
Variational autoencoders for collaborative filtering D Liang, RG Krishnan, MD Hoffman, T Jebara Proceedings of the 2018 World Wide Web Conference, 689-698, 2018	1373	2018
mir_eval: a transparent implementation of common MIR metrics C Raffel, B McFee, EJ Humphrey, J Salamon, O Nieto, D Liang, DPW Ellis ISMIR, 367-372, 2014	617	2014
Modeling user exposure in recommendation D Liang, L Charlin, J McInerney, DM Blei Proceedings of the 25th international conference on World Wide Web, 951-961, 2016	422	2016
Edward: A library for probabilistic modeling, inference, and criticism D Tran, A Kucukelbir, AB Dieng, M Rudolph, D Liang, DM Blei arXiv preprint arXiv:1610.09787, 2016	354	2016
Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence D Liang, J Altosaar, L Charlin, DM Blei Proceedings of the 10th ACM conference on recommender systems, 59-66, 2016	316	2016
Deep learning for recommender systems: A Netflix case study H Steck, L Baltrunas, E Elahi, D Liang, Y Raimond, J Basilico AI Magazine 42 (3), 7-18, 2021	147	2021
Causal inference for recommender systems Y Wang, D Liang, L Charlin, DM Blei Proceedings of the 14th ACM Conference on Recommender Systems, 426-431, 2020	146	2020
Causal inference for recommendation D Liang, L Charlin, DM Blei Causation: Foundation to Application, Workshop at UAI. AUAI 6 (41), 108, 2016	130	2016
Content-Aware Collaborative Music Recommendation Using Pre-trained Neural Networks D Liang, M Zhan, DPW Ellis ISMIR, 295-301, 2015	101	2015
Large language models as zero-shot conversational recommenders Z He, Z Xie, R Jha, H Steck, D Liang, Y Feng, BP Majumder, N Kallus, ... Proceedings of the 32nd ACM international conference on information and …, 2023	97	2023
On the challenges of learning with inference networks on sparse, high-dimensional data R Krishnan, D Liang, M Hoffman International conference on artificial intelligence and statistics, 143-151, 2018	93	2018
The deconfounded recommender: A causal inference approach to recommendation Y Wang, D Liang, L Charlin, DM Blei arXiv preprint arXiv:1808.06581, 2018	77	2018
librosa 0.5. 0 B McFee, M McVicar, O Nieto, S Balke, C Thome, D Liang, E Battenberg, ... Zenodo. URL: https://doi. org/10 5281, 2017	55	2017
librosa: 0.4. 1 B McFee, M McVicar, C Raffel, D Liang, O Nieto, E Battenberg, J Moore, ... Zenodo, 2015	48	2015
Beta Process Sparse Nonnegative Matrix Factorization for Music D Liang, MD Hoffman, DPW Ellis ISMIR, 375-380, 2013	35	2013
Music genre classification with the million song dataset D Liang, H Gu, B O’Connor Machine Learning Department, CMU, 2011	32	2011
Methods and prospects for human–computer performance of popular music RB Dannenberg, NE Gold, D Liang, G Xia Computer Music Journal 38 (2), 36-50, 2014	30	2014
Correlated variational auto-encoders D Tang, D Liang, T Jebara, N Ruozzi International Conference on Machine Learning, 6135-6144, 2019	22	2019
Active scores: Representation and synchronization in human–computer performance of popular music RB Dannenberg, NE Gold, D Liang, G Xia Computer Music Journal 38 (2), 51-62, 2014	22	2014

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