H. Steck

发表

Unsupervised Active Learning in Large Domains

Tommi S. Jaakkola, Harald Steck, T. Jaakkola, 2002, UAI.

Tumor volume combined with number of positive lymph node stations is a more important prognostic factor than TNM stage for survival of non-small-cell lung cancer patients treated with (chemo)radiotherapy.

P. Lambin, H. Steck, D. de Ruysscher, 2008, International journal of radiation oncology, biology, physics.

On the Dirichlet Prior and Bayesian Regularization

Tommi S. Jaakkola, Harald Steck, T. Jaakkola, 2002, NIPS.

A survey of collaborative filtering based social recommender systems

Yang Guo, Yong Liu, Harald Steck, 2014, Comput. Commun..

Rating Prediction based on Social Sentiment from Textual Reviews using MaxEnt Classifier

H. Steck, W. Zhu, Y. Liu, 2019 .

On a Non-Local Search Strategy for Learning in Bayesian Networks

H. Steck, 2007 .

Improving medical predictive models via Likelihood Gamble Pricing

Glenn Fung, Shipeng Yu, H. Steck, 2008 .

Hinge Rank Loss and the Area Under the ROC Curve

Harald Steck, H. Steck, 2007, ECML.

A Simple but Highly Effective Approach to Evaluate the Prognostic Performance of Gene Expression Signatures

Glenn Fung, Harald Steck, Philippe Lambin, 2011, PloS one.

On Ranking in Survival Analysis: Bounds on the Concordance Index

Balaji Krishnapuram, Harald Steck, Vikas C. Raykar, 2007, NIPS.

Harald Steck, H. Steck, 2011, RecSys '11.

Robust prognostic value of a knowledge-based proliferation signature across large patient microarray studies spanning different cancer types

P Lambin, B. Krishnapuram, P. Lambin, 2008, British Journal of Cancer.

Calibrated recommendations

Harald Steck, H. Steck, 2018, RecSys.

Automated Heart Wall Motion Abnormality Detection from Ultrasound Images Using Bayesian Networks

Glenn Fung, Rómer Rosales, R. Bharat Rao, 2007, IJCAI.

Markov Random Fields for Collaborative Filtering

Harald Steck, H. Steck, 2019, NeurIPS.

of the Workshop on Recommendation Utility Evaluation : Beyond RMSE ( RUE 2011 ) held at the 6 th ACM International Conference on Recommender Systems ( RecSys 2012 ) 9 September 2012 Dublin , Ireland

Christian Posse, Pablo Castells, Harald Steck, 2012 .

Training and testing of recommender systems on data missing not at random

Harald Steck, H. Steck, 2010, KDD.

Embarrassingly Shallow Autoencoders for Sparse Data

Harald Steck, H. Steck, 2019, WWW.

Deep Learning for Recommender Systems: A Netflix Case Study

Justin Basilico, Linas Baltrunas, Dawen Liang, 2022, AI Mag..

Constraint-based structural learning in Bayesian networks using finite data sets

Harald Steck, H. Steck, 2001 .

Bias-Corrected Bootstrap and Model Uncertainty

Tommi S. Jaakkola, Harald Steck, T. Jaakkola, 2003, NIPS.

Learning the Bayesian Network Structure: Dirichlet Prior versus Data pdf

Harald Steck, H. Steck, 2008, UAI 2008.

Predictive Discretization during Model Selection

Tommi S. Jaakkola, Harald Steck, T. Jaakkola, 2004, AISTATS.

On top-k recommendation using social networks

Yang Guo, Yong Liu, Harald Steck, 2012, RecSys.

Circle-based recommendation in online social networks

Harald Steck, Xiwang Yang, Yong Liu, 2012, KDD.

Public space behavior modeling with video and sensor analytics

Tin Kam Ho, Harald Steck, Lawrence O'Gorman, 2012, Bell Labs Technical Journal.

Autoencoders that don't overfit towards the Identity

Harald Steck, H. Steck, 2020, NeurIPS.

Evaluation of recommendations: rating-prediction and ranking

Harald Steck, H. Steck, 2013, RecSys.

Subgroup Discovery for Test Selection: A Novel Approach and Its Application to Breast Cancer Diagnosis

Rómer Rosales, Stefan Kramer, R. Bharat Rao, 2009, IDA.

Gaussian Ranking by Matrix Factorization

Harald Steck, H. Steck, 2015, RecSys.

Interactive Recommender Systems: Tutorial

Harald Steck, Roelof van Zwol, Chris Johnson, 2015, RecSys.

Bayesian Co-Training

R. Bharat Rao, Shipeng Yu, Balaji Krishnapuram, 2007, J. Mach. Learn. Res..

On the Regularization of Autoencoders pdf

Harald Steck, Dario García-García, H. Steck, 2021, ArXiv.

Learning the Bayesian Network Structure: Dirichlet Prior vs Data

Harald Steck, H. Steck, 2008, UAI.

Large Language Models as Zero-Shot Conversational Recommenders

Bodhisattwa Prasad Majumder, H. Steck, Nathan Kallus, 2023, CIKM.

A Generalized Probabilistic Framework and its Variants for Training Top-k Recommender System

Yu Xin, Harald Steck, Yu Xin, 2010, PRSAT@RecSys.

ADMM SLIM: Sparse Recommendations for Many Users

Harald Steck, Tony Jebara, Maria Dimakopoulou, 2020, WSDM.

Negative Interactions for Improved Collaborative Filtering: Don’t go Deeper, go Higher

H. Steck, Dawen Liang, 2021, RecSys.

REDD 2014 -- international workshop on recommender systems evaluation: dimensions and design

Panagiotis Adamopoulos, Alejandro Bellogín, Pablo Castells, 2014, RecSys '14.