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ORCIDDawen Liang
Person information
- affiliation: Netflix Inc., Los Gatos, CA, USA
- affiliation (PhD 2016): Columbia University, New York, NY, USA
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2020 – today
- 2024
[c23]Alex Ayoub, Kaiwen Wang, Vincent Liu, Samuel Robertson, James McInerney, Dawen Liang, Nathan Kallus, Csaba Szepesvári:
Switching the Loss Reduces the Cost in Batch Reinforcement Learning. ICML 2024- [c22]Noveen Sachdeva
, Lequn Wang, Dawen Liang, Nathan Kallus, Julian J. McAuley:
Off-Policy Evaluation for Large Action Spaces via Policy Convolution. WWW 2024: 3576-3585 - [i14]Alex Ayoub, Kaiwen Wang, Vincent Liu, Samuel Robertson, James McInerney, Dawen Liang, Nathan Kallus, Csaba Szepesvári:
Switching the Loss Reduces the Cost in Batch Reinforcement Learning. CoRR abs/2403.05385 (2024) - [i13]Kaiwen Wang, Dawen Liang, Nathan Kallus, Wen Sun:
Risk-Sensitive RL with Optimized Certainty Equivalents via Reduction to Standard RL. CoRR abs/2403.06323 (2024) - [i12]Zhankui He, Zhouhang Xie, Harald Steck, Dawen Liang, Rahul Jha, Nathan Kallus, Julian J. McAuley:
Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation. CoRR abs/2405.12119 (2024) - 2023
- [c21]Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, Julian J. McAuley:
Large Language Models as Zero-Shot Conversational Recommenders. CIKM 2023: 720-730 - [i11]Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, Julian J. McAuley:
Large Language Models as Zero-Shot Conversational Recommenders. CoRR abs/2308.10053 (2023) - [i10]Noveen Sachdeva, Lequn Wang, Dawen Liang, Nathan Kallus, Julian J. McAuley:
Off-Policy Evaluation for Large Action Spaces via Policy Convolution. CoRR abs/2310.15433 (2023) - 2022
- [i9]Dawen Liang, Nikos Vlassis:
Local Policy Improvement for Recommender Systems. CoRR abs/2212.11431 (2022) - 2021
- [j3]Harald Steck, Linas Baltrunas, Ehtsham Elahi, Dawen Liang, Yves Raimond, Justin Basilico:
Deep Learning for Recommender Systems: A Netflix Case Study. AI Mag. 42(3): 7-18 (2021) - [c20]Nicolas Chopin, Mike Gartrell, Dawen Liang, Alberto Lumbreras, David Rohde, Yixin Wang:
Bayesian Causal Inference for Real World Interactive Systems. KDD 2021: 4114-4115 - [c19]Harald Steck, Dawen Liang:
Negative Interactions for Improved Collaborative Filtering: Don't go Deeper, go Higher. RecSys 2021: 34-43 - 2020
- [c18]Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei:
Causal Inference for Recommender Systems. RecSys 2020: 426-431 - [e2]Cheng Zhang, Francisco J. R. Ruiz, Thang D. Bui, Adji Bousso Dieng, Dawen Liang:
Symposium on Advances in Approximate Bayesian Inference, AABI 2019, Vancouver, BC, Canada, December 8, 2019. Proceedings of Machine Learning Research 118, PMLR 2020 [contents]
2010 – 2019
- 2019
- [c17]Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi:
Correlated Variational Auto-Encoders. DGS@ICLR 2019 - [c16]Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi:
Correlated Variational Auto-Encoders. ICML 2019: 6135-6144 - [e1]Francisco J. R. Ruiz, Cheng Zhang, Dawen Liang, Thang D. Bui:
Symposium on Advances in Approximate Bayesian Inference, AABI 2018, Montréal, QC, Canada, December 2, 2018. Proceedings of Machine Learning Research 96, PMLR 2019 [contents] - [i8]Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi:
Correlated Variational Auto-Encoders. CoRR abs/1905.05335 (2019) - [i7]Da Tang, Dawen Liang, Nicholas Ruozzi, Tony Jebara:
Learning Correlated Latent Representations with Adaptive Priors. CoRR abs/1906.06419 (2019) - 2018
- [c15]Rahul G. Krishnan, Dawen Liang, Matthew D. Hoffman:
On the challenges of learning with inference networks on sparse, high-dimensional data. AISTATS 2018: 143-151 - [c14]Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara:
Variational Autoencoders for Collaborative Filtering. WWW 2018: 689-698 - [i6]Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara:
Variational Autoencoders for Collaborative Filtering. CoRR abs/1802.05814 (2018) - [i5]Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei:
The Deconfounded Recommender: A Causal Inference Approach to Recommendation. CoRR abs/1808.06581 (2018) - 2017
- [i4]Rahul G. Krishnan, Dawen Liang, Matthew D. Hoffman:
On the challenges of learning with inference networks on sparse, high-dimensional data. CoRR abs/1710.06085 (2017) - 2016
- [b1]Dawen Liang:
Understanding Music Semantics and User Behavior with Probabilistic Latent Variable Models. Columbia University, USA, 2016 - [c13]Dawen Liang, Jaan Altosaar, Laurent Charlin, David M. Blei:
Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence. RecSys 2016: 59-66 - [c12]Dawen Liang, Laurent Charlin, James McInerney, David M. Blei:
Modeling User Exposure in Recommendation. WWW 2016: 951-961 - [i3]Dustin Tran, Alp Kucukelbir, Adji B. Dieng, Maja Rudolph, Dawen Liang, David M. Blei:
Edward: A library for probabilistic modeling, inference, and criticism. CoRR abs/1610.09787 (2016) - 2015
- [c11]Dawen Liang, Matthew D. Hoffman, Gautham J. Mysore:
Speech dereverberation using a learned speech model. ICASSP 2015: 1871-1875 - [c10]Dawen Liang, John W. Paisley:
Landmarking Manifolds with Gaussian Processes. ICML 2015: 466-474 - [c9]Dawen Liang, Minshu Zhan, Daniel P. W. Ellis:
Content-Aware Collaborative Music Recommendation Using Pre-trained Neural Networks. ISMIR 2015: 295-301 - [c8]Brian McFee, Colin Raffel, Dawen Liang, Daniel P. W. Ellis, Matt McVicar, Eric Battenberg, Oriol Nieto:
librosa: Audio and Music Signal Analysis in Python. SciPy 2015: 18-24 - [i2]Dawen Liang, Laurent Charlin, James McInerney, David M. Blei:
Modeling User Exposure in Recommendation. CoRR abs/1510.07025 (2015) - 2014
- [j2]Roger B. Dannenberg, Nicolas E. Gold, Dawen Liang, Guangyu Xia:
Methods and Prospects for Human-Computer Performance of Popular Music. Comput. Music. J. 38(2): 36-50 (2014) - [j1]Roger B. Dannenberg, Nicolas E. Gold, Dawen Liang, Guangyu Xia:
Active Scores: Representation and Synchronization in Human-Computer Performance of Popular Music. Comput. Music. J. 38(2): 51-62 (2014) - [c7]Dawen Liang, Daniel P. W. Ellis, Matthew D. Hoffman, Gautham J. Mysore:
Speech decoloration based on the product-of-filters model. ICASSP 2014: 2400-2404 - [c6]Dawen Liang, John W. Paisley, Dan Ellis:
Codebook-based Scalable Music Tagging with Poisson Matrix Factorization. ISMIR 2014: 167-172 - [c5]Colin Raffel, Brian McFee, Eric J. Humphrey, Justin Salamon, Oriol Nieto, Dawen Liang, Daniel P. W. Ellis:
MIR_EVAL: A Transparent Implementation of Common MIR Metrics. ISMIR 2014: 367-372 - [c4]Dawen Liang, Matthew D. Hoffman, Gautham J. Mysore:
A Generative Product-of-Filters Model of Audio. ICLR (Poster) 2014 - [i1]Dawen Liang, Matthew D. Hoffman:
Beta Process Non-negative Matrix Factorization with Stochastic Structured Mean-Field Variational Inference. CoRR abs/1411.1804 (2014) - 2013
- [c3]Dawen Liang, Matthew D. Hoffman, Daniel P. W. Ellis:
Beta Process Sparse Nonnegative Matrix Factorization for Music. ISMIR 2013: 375-380 - 2011
- [c2]Guangyu Xia, Dawen Liang, Roger B. Dannenberg, Mark Harvilla:
Segmentation, Clustering, and Display in a Personal Audio Database for Musicians. ISMIR 2011: 139-144 - [c1]Dawen Liang, Guangyu Xia, Roger B. Dannenberg:
A Framework for Coordination and Synchronization of Media. NIME 2011: 167-172
Coauthor Index
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