Dawen Liang

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 24

Top Topics

Probabilistic Modeling

Policy Evaluation

Recommender Systems

Causal Inference

Top Venues

Publications

Kaiwen Wang, Dawen Liang, Nathan Kallus, Wen Sun
Risk-Sensitive RL with Optimized Certainty Equivalents via Reduction to Standard RL. CoRR (2024)
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 (2024)
Noveen Sachdeva, Lequn Wang, Dawen Liang, Nathan Kallus, Julian J. McAuley
Off-Policy Evaluation for Large Action Spaces via Policy Convolution. WWW (2024)
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 (2024)
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 (2023)
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)
Noveen Sachdeva, Lequn Wang, Dawen Liang, Nathan Kallus, Julian J. McAuley
Off-Policy Evaluation for Large Action Spaces via Policy Convolution. CoRR (2023)
Dawen Liang, Nikos Vlassis
Local Policy Improvement for Recommender Systems. CoRR (2022)
Harald Steck, Dawen Liang
Negative Interactions for Improved Collaborative Filtering: Don't go Deeper, go Higher. RecSys (2021)
Nicolas Chopin, Mike Gartrell, Dawen Liang, Alberto Lumbreras, David Rohde, Yixin Wang
Bayesian Causal Inference for Real World Interactive Systems. KDD (2021)
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) (2021)
Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei
Causal Inference for Recommender Systems. RecSys (2020)
Da Tang, Dawen Liang, Nicholas Ruozzi, Tony Jebara
Learning Correlated Latent Representations with Adaptive Priors. CoRR (2019)
Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi
Correlated Variational Auto-Encoders. ICML (2019)
Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi
Correlated Variational Auto-Encoders. DGS@ICLR (2019)
Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi
Correlated Variational Auto-Encoders. CoRR (2019)
Rahul G. Krishnan, Dawen Liang, Matthew D. Hoffman
On the challenges of learning with inference networks on sparse, high-dimensional data. AISTATS (2018)
Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei
The Deconfounded Recommender: A Causal Inference Approach to Recommendation. CoRR (2018)
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara
Variational Autoencoders for Collaborative Filtering. CoRR (2018)
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara
Variational Autoencoders for Collaborative Filtering. WWW (2018)
Rahul G. Krishnan, Dawen Liang, Matthew D. Hoffman
On the challenges of learning with inference networks on sparse, high-dimensional data. CoRR (2017)
Dawen Liang, Laurent Charlin, James McInerney, David M. Blei
Modeling User Exposure in Recommendation. WWW (2016)
Dawen Liang, Jaan Altosaar, Laurent Charlin, David M. Blei
Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence. RecSys (2016)
Dustin Tran, Alp Kucukelbir, Adji B. Dieng, Maja Rudolph, Dawen Liang, David M. Blei
Edward: A library for probabilistic modeling, inference, and criticism. CoRR (2016)
Dawen Liang, Laurent Charlin, James McInerney, David M. Blei
Modeling User Exposure in Recommendation. CoRR (2015)
Dawen Liang, John W. Paisley
Landmarking Manifolds with Gaussian Processes. ICML (2015)
Dawen Liang, Matthew D. Hoffman, Gautham J. Mysore
Speech dereverberation using a learned speech model. ICASSP (2015)
Dawen Liang, Minshu Zhan, Daniel P. W. Ellis
Content-Aware Collaborative Music Recommendation Using Pre-trained Neural Networks. ISMIR (2015)
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)
Dawen Liang, Matthew D. Hoffman
Beta Process Non-negative Matrix Factorization with Stochastic Structured Mean-Field Variational Inference. CoRR (2014)
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)
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) (2014)
Dawen Liang, Daniel P. W. Ellis, Matthew D. Hoffman, Gautham J. Mysore
Speech decoloration based on the product-of-filters model. ICASSP (2014)
Dawen Liang, Matthew D. Hoffman, Gautham J. Mysore
A Generative Product-of-Filters Model of Audio. ICLR (Poster) (2014)
Dawen Liang, John W. Paisley, Dan Ellis
Codebook-based Scalable Music Tagging with Poisson Matrix Factorization. ISMIR (2014)
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) (2014)
Dawen Liang, Matthew D. Hoffman, Daniel P. W. Ellis
Beta Process Sparse Nonnegative Matrix Factorization for Music. ISMIR (2013)
Dawen Liang, Guangyu Xia, Roger B. Dannenberg
A Framework for Coordination and Synchronization of Media. NIME (2011)
Guangyu Xia, Dawen Liang, Roger B. Dannenberg, Mark Harvilla
Segmentation, Clustering, and Display in a Personal Audio Database for Musicians. ISMIR (2011)