On the Use of Skeletons when Learning in Bayesian Networks. - Harald Steck (UAI, 2000)

Constraint-based structural learning in Bayesian networks using finite data sets. - Harald Steck (2001)

On the Dirichlet Prior and Bayesian Regularization. - Harald Steck; Tommi S. Jaakkola (NIPS, 2002)

Unsupervised Active Learning in Large Domains. - Harald Steck; Tommi S. Jaakkola (UAI, 2002)

Bias-Corrected Bootstrap and Model Uncertainty. - Harald Steck; Tommi S. Jaakkola (NIPS, 2003)

Ranking by Dependence - A Fair Criteria. - Harald Steck (UAI, 2006)

Hinge Rank Loss and the Area Under the ROC Curve. - Harald Steck (ECML, 2007)

Automated Heart Wall Motion Abnormality Detection from Ultrasound Images Using Bayesian Networks. - Maleeha Qazi; Glenn Fung; Sriram Krishnan; Rómer Rosales; Harald Steck; R. Bharat Rao; Don Poldermans; Dhanalakshmi Chandrasekaran (IJCAI, 2007)

On Ranking in Survival Analysis: Bounds on the Concordance Index. - Vikas C. Raykar; Harald Steck; Balaji Krishnapuram; Cary Dehing-Oberije; Philippe Lambin (NIPS, 2007)

Bayesian Co-Training. - Shipeng Yu; Balaji Krishnapuram; Rómer Rosales; Harald Steck; R. Bharat Rao (NIPS, 2007)

Predictive Discretization during Model Selection. - Harald Steck; Tommi S. Jaakkola (AISTATS, 2007)

Learning the Bayesian Network Structure: Dirichlet Prior vs Data. - Harald Steck (UAI, 2008)

Data-Efficient Information-Theoretic Test Selection. - Marianne Mueller; Rómer Rosales; Harald Steck; Sriram Krishnan; Bharat Rao; Stefan Kramer 001 (AIME, 2009)

Subgroup Discovery for Test Selection: A Novel Approach and Its Application to Breast Cancer Diagnosis. - Marianne Mueller; Rómer Rosales; Harald Steck; Sriram Krishnan; Bharat Rao; Stefan Kramer 001 (IDA, 2009)

Training and testing of recommender systems on data missing not at random. - Harald Steck (KDD, 2010)

A Generalized Probabilistic Framework and its Variants for Training Top-k Recommender System. - Harald Steck; Yu Xin (PRSAT@RecSys, 2010)

KL-divergence kernel regression for non-Gaussian fingerprint based localization. - Piotr Mirowski; Harald Steck; Philip Whiting; Ravishankar Palaniappan; Michael MacDonald; Tin Kam Ho (IPIN, 2011)

Item popularity and recommendation accuracy. - Harald Steck (RecSys, 2011)

Multi-value probabilistic matrix factorization for IP-TV recommendations. - Yu Xin; Harald Steck (RecSys, 2011)

Circle-based recommendation in online social networks. - Xiwang Yang; Harald Steck; Yong Liu 013 (KDD, 2012)

On top-k recommendation using social networks. - Xiwang Yang; Harald Steck; Yang Guo 001; Yong Liu 013 (RecSys, 2012)

Public Space Behavior Modeling With Video and Sensor Analytics. - Tin Kam Ho; Kim Matthews; Lawrence O'Gorman; Harald Steck (Bell Labs Tech. J., 2012)

Learning the Bayesian Network Structure: Dirichlet Prior versus Data - Harald Steck (CoRR, 2012)

Probability kernel regression for WiFi localisation. - Piotr Mirowski; Philip Whiting; Harald Steck; Ravishankar Palaniappan; Michael MacDonald; Detlef Hartmann; Tin Kam Ho (J. Locat. Based Serv., 2012)

Evaluation of recommendations: rating-prediction and ranking. - Harald Steck (RecSys, 2013)

REDD 2014 - international workshop on recommender systems evaluation: dimensions and design. - Panagiotis Adamopoulos; Alejandro Bellogín; Pablo Castells; Paolo Cremonesi; Harald Steck (RecSys, 2014)

A survey of collaborative filtering based social recommender systems. - Xiwang Yang; Yang Guo 001; Yong Liu 013; Harald Steck (Comput. Commun., 2014)

Gaussian Ranking by Matrix Factorization. - Harald Steck (RecSys, 2015)

Interactive Recommender Systems: Tutorial. - Harald Steck; Roelof van Zwol; Chris Johnson 011 (RecSys, 2015)

Calibrated recommendations. - Harald Steck (RecSys, 2018)

Markov Random Fields for Collaborative Filtering. - Harald Steck (NeurIPS, 2019)

Embarrassingly Shallow Autoencoders for Sparse Data. - Harald Steck (WWW, 2019)

Collaborative Filtering via High-Dimensional Regression. - Harald Steck (CoRR, 2019)

Autoencoders that don't overfit towards the Identity. - Harald Steck (NeurIPS, 2020)

ADMM SLIM: Sparse Recommendations for Many Users. - Harald Steck; Maria Dimakopoulou; Nickolai Riabov; Tony Jebara (WSDM, 2020)

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)

On the Regularization of Autoencoders. - Harald Steck; Dario García-García (CoRR, 2021)

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)

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)

Is Cosine-Similarity of Embeddings Really About Similarity? - Harald Steck; Chaitanya Ekanadham; Nathan Kallus (WWW (Companion Volume), 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)