Segmentation, Clustering, and Display in a Personal Audio Database for Musicians. - Guangyu Xia; Dawen Liang; Roger B. Dannenberg; Mark Harvilla (ISMIR, 2011)

A Framework for Coordination and Synchronization of Media. - Dawen Liang; Guangyu Xia; Roger B. Dannenberg (NIME, 2011)

Beta Process Sparse Nonnegative Matrix Factorization for Music. - Dawen Liang; Matthew D. Hoffman; Daniel P. W. Ellis (ISMIR, 2013)

Speech decoloration based on the product-of-filters model. - Dawen Liang; Daniel P. W. Ellis; Matthew D. Hoffman; Gautham J. Mysore (ICASSP, 2014)

Codebook-based Scalable Music Tagging with Poisson Matrix Factorization. - Dawen Liang; John W. Paisley; Dan Ellis (ISMIR, 2014)

MIR_EVAL: A Transparent Implementation of Common MIR Metrics. - Colin Raffel; Brian McFee; Eric J. Humphrey; Justin Salamon; Oriol Nieto; Dawen Liang; Daniel P. W. Ellis (ISMIR, 2014)

Methods and Prospects for Human-Computer Performance of Popular Music. - Roger B. Dannenberg; Nicolas E. Gold; Dawen Liang; Guangyu Xia (Comput. Music. J., 2014)

Active Scores: Representation and Synchronization in Human-Computer Performance of Popular Music. - Roger B. Dannenberg; Nicolas E. Gold; Dawen Liang; Guangyu Xia (Comput. Music. J., 2014)

Beta Process Non-negative Matrix Factorization with Stochastic Structured Mean-Field Variational Inference. - Dawen Liang; Matthew D. Hoffman (CoRR, 2014)

A Generative Product-of-Filters Model of Audio. - Dawen Liang; Matthew D. Hoffman; Gautham J. Mysore (ICLR (Poster), 2014)

Speech dereverberation using a learned speech model. - Dawen Liang; Matthew D. Hoffman; Gautham J. Mysore (ICASSP, 2015)

Landmarking Manifolds with Gaussian Processes. - Dawen Liang; John W. Paisley (ICML, 2015)

Content-Aware Collaborative Music Recommendation Using Pre-trained Neural Networks. - Dawen Liang; Minshu Zhan; Daniel P. W. Ellis (ISMIR, 2015)

librosa: Audio and Music Signal Analysis in Python. - Brian McFee; Colin Raffel; Dawen Liang; Daniel P. W. Ellis; Matt McVicar; Eric Battenberg; Oriol Nieto (SciPy, 2015)

Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence. - Dawen Liang; Jaan Altosaar; Laurent Charlin; David M. Blei (RecSys, 2016)

Modeling User Exposure in Recommendation. - Dawen Liang; Laurent Charlin; James McInerney; David M. Blei (WWW, 2016)

Edward: A library for probabilistic modeling, inference, and criticism. - Dustin Tran; Alp Kucukelbir; Adji B. Dieng; Maja Rudolph; Dawen Liang; David M. Blei (CoRR, 2016)

Understanding Music Semantics and User Behavior with Probabilistic Latent Variable Models. - Dawen Liang (2016)

On the challenges of learning with inference networks on sparse, high-dimensional data. - Rahul G. Krishnan; Dawen Liang; Matthew D. Hoffman (AISTATS, 2018)

Variational Autoencoders for Collaborative Filtering. - Dawen Liang; Rahul G. Krishnan; Matthew D. Hoffman; Tony Jebara (WWW, 2018)

The Deconfounded Recommender: A Causal Inference Approach to Recommendation. - Yixin Wang 002; Dawen Liang; Laurent Charlin; David M. Blei (CoRR, 2018)

Correlated Variational Auto-Encoders. - Da Tang; Dawen Liang; Tony Jebara; Nicholas Ruozzi (ICML, 2019)

Learning Correlated Latent Representations with Adaptive Priors. - Da Tang; Dawen Liang; Nicholas Ruozzi; Tony Jebara (CoRR, 2019)

Causal Inference for Recommender Systems. - Yixin Wang 002; Dawen Liang; Laurent Charlin; David M. Blei (RecSys, 2020)

Bayesian Causal Inference for Real World Interactive Systems. - Nicolas Chopin; Mike Gartrell; Dawen Liang; Alberto Lumbreras; David Rohde; Yixin Wang 002 (KDD, 2021)

Negative Interactions for Improved Collaborative Filtering: Don't go Deeper, go Higher. - Harald Steck; Dawen Liang (RecSys, 2021)

Deep Learning for Recommender Systems: A Netflix Case Study. - Harald Steck; Linas Baltrunas; Ehtsham Elahi; Dawen Liang; Yves Raimond; Justin Basilico (AI Mag., 2021)

Local Policy Improvement for Recommender Systems. - Dawen Liang; Nikos Vlassis (CoRR, 2022)

Large Language Models as Zero-Shot Conversational Recommenders. - Zhankui He; Zhouhang Xie; Rahul Jha; Harald Steck; Dawen Liang; Yesu Feng; Bodhisattwa Prasad Majumder; Nathan Kallus; Julian J. McAuley (CIKM, 2023)

Switching the Loss Reduces the Cost in Batch Reinforcement Learning. - Alex Ayoub; Kaiwen Wang; Vincent Liu; Samuel Robertson; James McInerney; Dawen Liang; Nathan Kallus; Csaba Szepesvári (ICML, 2024)

Neighborhood-Based Collaborative Filtering for Conversational Recommendation. - Zhouhang Xie; Junda Wu; Hyunsik Jeon; Zhankui He; Harald Steck; Rahul Jha; Dawen Liang; Nathan Kallus; Julian J. McAuley (RecSys, 2024)

Off-Policy Evaluation for Large Action Spaces via Policy Convolution. - Noveen Sachdeva; Lequn Wang; Dawen Liang; Nathan Kallus; Julian J. McAuley (WWW, 2024)

Risk-Sensitive RL with Optimized Certainty Equivalents via Reduction to Standard RL. - Kaiwen Wang; Dawen Liang; Nathan Kallus; Wen Sun 002 (CoRR, 2024)

Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation. - Zhankui He; Zhouhang Xie; Harald Steck; Dawen Liang; Rahul Jha; Nathan Kallus; Julian J. McAuley (WSDM, 2025)

Does Weighting Improve Matrix Factorization for Recommender Systems? - Alex Ayoub; Samuel Robertson; Dawen Liang; Harald Steck; Nathan Kallus (WWW, 2025)

Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems. - Yaochen Zhu; Chao Wan; Harald Steck; Dawen Liang; Yesu Feng; Nathan Kallus; Jundong Li (WWW, 2025)

From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System. - Rohan Surana; Junda Wu; Zhouhang Xie; Yu Xia 007; Harald Steck; Dawen Liang; Nathan Kallus; Julian J. McAuley (CoRR, 2025)